FINAL TECHNICAL REPORT ANALYSIS OF PRENORMALIZATION TECHNIQUES FOR IMAGERY SCREENING

• Document Type: CREST
• Collection: NGA Records (Formerly NIMA)
• Document Number (FOIA) /ESDN (CREST): CIA-RDP78B04770A000200020017-1
• Release Decision: RIPPUB
• Original Classification: K
• Document Page Count: 76
• Document Creation Date: December 28, 2016
• Document Release Date: September 27, 2005
• Sequence Number: 17
• Publication Date: April 29, 1966
• Content Type: REPORT

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FINAL TECHNICAL REPORT
ANALYSIS OF PRENORMALIZATION TECHNIQUES
FOR
IMAGERY SCREENING
25X1
25X1
29 APRIL 1966
Prepared by:
25X1
Declassification Review by NGA/DoD
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FINAL TECHNICAL REPORT
ANALYSIS OF PRENORMALIZATION TECHNIQUES
FOR
IMAGERY SCREENING
25X1
25X1
29 APRIL 1966
Prepared by.
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ILLEGIB
ILLEGIB
25X1
FREQUENCY-DOMAIN SYSTEM
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 t
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FOREWORD
This final technical report describes the results
211
obtained byl during the course of the
program conducted under
objective of the program was to evaluate the time- and fre-
quency-domain Integral-Scan Parameter-Generator.
25X1
25X1
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I
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This is the final technical report on a study of the time-
and frequency-domain Integral-Scan Parameter-Generator. The
report includes the details of the experiments conducted to
evaluate the performance of the systems with aerial recon-
naissance imagery. The test results indicate that the measure-
ments made by the prenormalizing system are descriptive of the
imagery under view and that the measurements have a high
degree of invariance to translation and rotation.
t
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TABLE OF CONTENTS
SECTION PAGE
I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . 1
A. PROGRAM DESCRIPTION . . . . . . . . . . . . . . 8
II. EXPERIMENTAL PROGRAM . . . . . . . . . . . . . . . . 11
A. TIME-DOMAIN ANALYSIS . . . . . . . . . . . . . . 11
1. System Description . . . . . . . . . . . . . . . 11
2. Computer Program . . . . . . . . . . . . . . . . 12
3. Experiments . . . . . . . . . . . . . . . . . . 17
B. FREQUENCY-DOMAIN ANALYSIS . . . . . . . . . . . 18
1. System Description . . . . . . . . . . . . . . . 18
2. Computer Program . . . . . . . . . . . . . . . . 20
3. Experiments . . . . . . . . . . . . . . . . . . 28
C. IMAGERY . . . . . . . . . . . . . . . . . . . . 32
D. STATISTICAL ANALYSIS . . . . . . . . . . . . . . 34
III. EXPERIMENTAL RESULTS . . . . . . . . . . . . . . . . 41
A. TIME DOMAIN . . . . . . . . . . . . . . . . . . 41
1. General Results . . . . . . . . . . . . . . . . 41
2. Diagnostic Tests . . . . . . . . . . . . . . . . 42
3. Statistical Analysis . . . . . . . . . . . . . . 45
B. FREQUENCY DOMAIN . . . . . . . . . . . . . . . . 46
1. General Results . . . . . . . . . . . . . . . . 46
2. Diagnostic Tests . . . . . . . . . . . . . . . . 49
3. Statistical Analysis . . . . . . . . . . . . . . 51
APPENDIX II. GENERAL COMMENTS ON AUTOMATIC TARGET-RECOGNITION
RESEARCH
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FIGURE
PAGE
1 Automatic Target-Recognition Model (Without Video
Processing) . . . . . . . . . . . . . . . . . . . . .
2
2 Automatic Target-Recognition Model (With Video
Processing) . . . . . . . . . . . . . . . . . . . . .
3
3 Frequency-Domain Processor . . . . . . . . . . . . .
5
4 Time-Domain Processor . . . . . . . . . . . . . . . .
6
5 Circuit Implementation of the Correlation Function
13
6 Function Diagram: Adaptive Learning Program (CDS
G-15 Computer) . . . . . . . . . . . . . . . . . . .
15
7 Block Diagram: Frequency-Domain System and Computer
Analysis . . . . . . . . . . . . . . . . . . . . . .
19
8 Example of Paper-Tape-Punch Output Format . . . . . .
21
9 Conversion Program Flow Chart . . . . . . . . . . . .
23
10 Function Diagram: Adaptive Learning Program (SDS-925
Computer) . . . . . . . . . . . . . . . . . . . . . .
25
11 Adaptive Learning Program Flow Chart . . . . . . . .
27
12 Random Orientation of Imagery . . . . . . . . . . . .
30
13 Standardized Orientation of Imagery . . . . . . . . .
31
14 Correlation of Filter Outputs for Tanks-Versus-Back-
ground Experiments . . . . . . . . . . . . . . . . .
44
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TABLE
PAGE
1 Imagery Used in Experimental Program . . . . . . . .
33
2 Time-Domain Statistical Data . . . . . . . . . . . .
45
3 Targets Versus Background: Test Results . . . . . .
47
4 Targets Versus Targets: Test Results . . . . . . . .
48
5 Geometric Shapes: Test Results . . . . . . . . . . .
50
6 Frequency-Domain Statistical Data (Rotation) . . . .
52
7 Frequency-Domain Statistical Data (Rotation-Transla-
tion) . . . . . . . . . . . . . . . . . . . . . . . .
52
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211
including a survey of
the overall developments in automatic recognition equipment,
shows that the development of procedures for removing variations
in pattern-recognition error-rates caused by translation and
rotation should be emphasized over the development of complex
sensing or decision logic. However, so long as research
programs use only cut film, the need for such prenormalization
is not evident, and the processing equipment can consist of
a scanner and a decision logic. (See figure 1.)
However, once roll film is introduced into the research
program or operational procedures are simulated, rotation and
translation of imagery occur immediately, and one of three
techniques for handling this variability is required. First,
the decision logic can be trained for all possible orientations
of the target imagery. Second, some form of invariant
parameter-generation can be used. Third, the film stage can
be designed to rotate and translate the imagery. It is easier
to add video-processing techniques to eliminate rotation and
translation variances (figure 2) than to develop a film stage
to rotate or translate the film.
to translation and rotation, of the prenormalization equipment
implementing these techniques. Photographs of the hardware
techniques developed by and the invariance, 25X1
The program covered by this report concentrated on
demonstrating both the capabilities of the two prenormalizing
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INPUT
(FILM)
OPTICAL
SCANNER
VIDEO
SIGNAL
ADAPTIVE
RECOGNITION
SYSTEM
N7
OUTPUT
(CLASSIFICATIONS)
Figure 1. Automatic Target-Recognition Model
(Without Video Processing)
11
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INPUT
(FILM)
OPTICAL
SCANNER
VIDEO
SIGNAL
VIDEO
PROCESSOR
ADAPTIVE
RECOGNITION
SYSTEM
OUTPUT
(CLASSIFICATIONS)
Figure 2. Automatic Target-Recognition Model
(With Video Processing)
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are presented as Frontispieces to this report. Available
decision logics were used, and no effort was made to improve
existing logics or to develop further the scanning or
processing techniques originally generated in the program.
The principal objective of the program was to demonstrate
experimentally that target images can be recognized even
when information comprising several orders of magnitude has
been eliminated. It is not possible to determine exactly
which measures of an image completely classify or describe
it. However, it has been found that it is possible to
judiciously choose a small number of meaningful measures and
still accomplish recognition of the imagery.
The two generalized preprocessing techniques employed
in this program are shown in conceptual form as figures 3 and
4. Frequency-domain processing (figure 3) is based on the
generation of D-cells from two-dimensional power spectrum
data, and time-domain processing (figure 4) is based on the
correlation of image derived video signals with sets of
orthogonal polynomial functions. Both techniques will be
discussed in more detail in a later section. In particular
target search problems, if some preliminary data about the
imagery is supplied, it is possible to develop preprocessing
equipment which is devised to extract parameters best suited
to describing the targets being sought. This would improve
the error rate and eliminate the necessity for having all of
the band-pass filters and general orthogonal functions
available.
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INTEGRAL
SCANNER
PRELIMINARY PROPERTY'
FILTERS
SECONDARY
FILTERS
CIRCULAR ANALYSIS
FILTERS
~~H
Figure 3. Frequency-Domain Processor
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F PROPERTY FILTER
MI
DATA
INPUT
INTEGRAL
SCANNER
ROPERTY FILTER
?10
THRESHOLD
CIRCUIT a
COUNTER
THRESHOLD
CIRCUITa
COUNTER
THRESHOLD
CIRCUIT a
COUNTER
Figure 4. Time-Domain Processor
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During this program, the stress was not on defining
the absolute recognition-rates but on proving the invariance
of these rates to rotation and translation. Recognition
rates can always be improved by modifying existing equipment;
however, the invariance of those rates must be a fundamental
part of the approach. Also, simple error-calculations are
not sufficient for analysis of the experimental results; the
calculations must be analyzed for invariance during rotation
and translation, invariance between target classes, and
consistency of recognition rate.
The Chi-square test has been chosen for analysis of the
data. Other tests, such as the analysis of variance, or
factorial analysis, would have yielded additional information.
However, the imagery data required for these tests could not
be obtained during the program.
The integral scanner used during this program was the
optomechanical device outlined in Appendix I and detailed in
the final hardware report submitted under separate cover.
The resolution and flexibility of the equipment were limited;
we now feel that integral scanning equipment should be based
on an easily programmed electronically-generated flying-line.
Because of the unique sensing capability of the flying spot
and the flying-line, a combination of the two would be most
desirable. However, the full potential of the existing
optomechanical flying-line scanner can be more fully exploited.
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The original objectives of this program were to define
the operational goals for automatic screening of photographic
intelligence data; to study, test, and evaluate the techniques
applicable to the problem; and to generate a design for an
operational prototype system. At the sponsor's request, work
on the first and third objectives was discontinued so that
the second could be emphasized.
A problem arose when the GFE CONFLEX I had a failure in
its disc memory, necessitating the programming of a CONFLEX
simulator on the CDC G15 and SDS 925 computers. Because of
limits of the memory size of the computers, only two--class
experiments could be run.
The CONFLEX I malfunction also entailed making the
extraction equipment independent of the CONFLEX. The original
plan was to go directly from the integral scan prenormaliz-
ation equipment into the CONFLEX I. Since the CONFLEX
became unavailable it became necessary to revert to a punched-
paper-tape output and to process all of the experimental data
in three phases. The first phase was data collection and
punched-paper-tape output; the second phase was conversion
of the punched-paper-tape output to SDS 925 format input;
and the third phase was the use of the CONFLEX simulator on
the SDS 925 computer.
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An extensive testing program requires a considerable
amount of visual imagery. Several sources of this imagery
were contacted, and imagery was received from the Air Force
and the Army. Although ideally some measure of quality
should have been assigned to the imagery, this was far
beyond the scope of the program; therefore, imagery subjectively
established as being of differing complexity was used in the
test data-base.
Unfortunately, most of the imagery was of cultural
rather than military targets, a factor that in no way
affected the validity of the program results but did limit
the results since the inclusion of more military targets would
have eliminated potential questions concerning the military
significance of the data. Because it was possible to conduct
only two-class experiments and there were but limited examples
for each class, most of the tests were simulated screening
operations. That is, the task of the decision logic was to
distinguish man-made or other significant visual features from
imagery having non-significant content.
Work on defining the operational objectives for
automatic screening of photographic intelligence-data and
the generation of'a design for an operational prototype system
was discontinued part way through the program at the sponsor's
request. That effort has not been included in this report,
but it is worthy of note that the completed research indicated
the possiblity that an automatic screener could be designed
to separate significant targets from background.
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A review of the test results will show that many more
experiments'are possible. A full evaluation of the integral
scanning technique has just begun, although several thousand
images have been processed. The urgent need for a standardized
data base of several hundred examples for each of several
hundred target classes has been demonstrated. A large portion
of each automatic target-recognition program is always concerned
with the collection or generation of suitable input materials.
In summary, although many outside factors have caused a
change in the. original direction of the program, many-useful
results have been obtained. The integral scanner and its
video processing equipment are now completely independent of
the CONFLEX I. The processor output is punch-paper-tape that
can be utilized in any decision logic with suitable modifications.
Thus, this equipment can be used with any suitable input
imagery and with any suitable decision logic.
The capability now exists for the processing of several
thousand images a month and by simple change of.output to a
higher-speed peripheral unit, or by going on line, this test
capability can be increased manyfold. The system has become
a research tool capable of generating a standardized input
for differing decision 'logics; the integral scanner alone
can be used to generate standardized linear signals for dif-
fering preprocessors. Finally, extensive testing has demonstrated
that the integral scanning equipment with time-domain and
frequency-domain processing does yield statistically signifi-
cant invariant error rates.
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211
II. EXPERIMENTAL PROGRAM
A. TIME-DOMAIN ANALYSIS
During the intial phase of this contract two parallel
research efforts were being conducted. One dealt with a
prenormalization approach based on the time-domain analysis
of signals obtained by the integral-scan sequence, the other
considered frequency-domain analysis. When the breakdown of
CONFLEX I dictated the instrumentation of a paper-punch-tape
output of the frequency-domain analysis scanner developed
under this contract, the time-domain processor and scanner
were used in conducting the tests. The purpose of this
procedure was to establish some basis of comparison between
the two processing approaches (i.e., frequency-domain versus
time-domain) by using identical imagery in similar tests on
both systems.
A brief description of the time-domain analysis approach,
the system developed, and the computer processing used
precedes a delineation of the tests conducted on this system.
1. System Description
A detailed description of the integral-scan technique
itself and the motivation behind it is presented in the final
report submitted under I land synopsized in Appendix I.
There it was established that changes in rotation and transla-
tion of an object in the scanned field result in changes in
the time of occurrence of the video waveshapes of the objects.
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Thus, when a method independent of time of occurrence was
used in processing this waveform, a set of measures (or
properties) invariant to translation and rotation could be
established. Time-domain analysis, diagrammed in figure 4,
is matching a set of ten orthogonal property filters to the
video waveform passed through delay lines. Essentially, the
approach consists of-looking, as it were, through a weighted
window at the waveshape and establishing a level of correla-
tion between this waveform and the various property filters.
The correlation level is then thresholded and can be counted.
A tailored form of the standard correlation measure used in
statistics is employed, and a generalized. circuit implementa-
tion of this measure is shown in figure 5.
Of the 31 operational amplifiers that make up the
component circuits, 11 are used in the normalizing calculation
of the video information. The remaining 20 perform the final
summing operation of the ten orthogonal property filters to
which the video is matched. The output pulses from the threshold
circuits of these ten filters are counted and displayed on
ten-stage binary counters, one of which is set for each of
the ten property filters.
2. Computer Program
Since CONFLEX I was not available for testing, a computer
211
program was written for
CDC G-15 computer. The program
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 - - ? Mr Re Rease 2 1/2 IA- 8BJw
pprove o AO 00 -1 -
F(T)---X
DELAY LINE
1 23-----30
2 LAY LINE
1123--- --30
+32 XR 32R
ONE
FILTER
32TrOTX 32T
1/32
1
V32
- XR+X R - ) :
XR-x R.T JFrx
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-6x
-(-XR+XR+T ) =
2
(x2 -x )=+~x_
SQUARE
ROOT
FOR}I COUNTER
f / '
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simulated an adaptive structure,1 using a linearly inde-
pendent coding2 on the values obtained from the property
counters for each target sample. These values were recorded
on paper-punch-tape and computer processed.
The computer program is presented in the function
diagram of figure 6. The output count of each filter is
used to generate 100 D-cells, in either a +1 or -1 state.
This state is dependent on a threshold level that varies
linearly from the first to the 100th D-cell. Hence, a
property count (filter output) that is exactly half of
the maximum count acceptable would produce 50 + D-cells
and the same number of - D-cells.
The 100 D-cells of each of the ten filters are in turn
multiplied by the disposition of 1000 weights in each of
two fields. (For a two-class experiment, one field would
be sufficient, but this program is designed to handle a
four-class problem; hence, the two fields.) At the begin-
ning of any learning cycle, the weights are set to zero; hence,
the results after multiplication are all zero. All of the
1 Widrow and Hoff, "Adaptive Switching Circuits," Stanford
Electronic Laboratory, Stanford, California, 1960.
2 Smith, "Contractor Control by Adaptive Pattern Recogni-
tion Techniques," Stanford Electronic Laboratory, Stanford,
California, 1964.
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Figure 6. Function Diagram: Adaptive Learning Program
(CDS G-15 Computer)
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weights in each field are summed, and the signs of these
sums are compared to an assigned class-code that is recorded
on the paper-punch-tape with the filter count of a given
sample.
For this four-class program, the code can be either ++,
+ --, or -+, where each pair of signs is compared to the
signs of the sums of the two fields. If either or both do
not compare (as would be the case here where all weights are
initially zero) the weights in either or both fields are
adjusted ("Gain Adjust") by incrementing or decrementing the
weight counters according to the D-cell inputs; i.e., a + D-
cell increments the counter, and a - D-cell decrements it.
Once again, weights are summed and the signs compared with
the assigned code. This process is repeated and the memory
(i.e., weights) is thus updated until the signs of the sums
of both fields agree with the correct class code for the
given sample.
In a like manner, each sum is compared to a threshold
set to define the degree of certitude with which a sample
is learned. If either or both sums do not exceed this
threshold, the weights in either or both fields are adjusted
in the manner explained above until the threshold level is
exceeded. These weights or counters can now be referred to
as a memory adapted to the sample represented by the input-
filter count.
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In a typical experiment in a learn mode, numerous
samples in up to four different classes are fed into this
computer program through tapes, and recycled through until
the memory is completely adapted to all samples in each
class. For each iteration, the sample number, an ERROR
or NO ERROR indication, and the field sums are typed out.
Once the memory convergence is reached, any number of
unknown samples can be tested against this memory for
classification into one of the four classes.
The recognize mode is similar in its operation to the
learn mode, except that no memory adjustment is involved.
The D-cell representation of the tested sample is multi-
plied by the 2000 adapted weights; the results are
summed and tested against the class code assigned to the
test sample, and against any recognition threshold that
may be programmed in. If any operation involves an
error, this information is simply typed out, and the
next unknown sample is tested.
3. Experiments
Tests were conducted using the time-domain system
and computer processing described above, and the limited
amount of imagery on hand. The classes of targets were
aircraft, bridges, road intersections, tanks and various
backgrounds. Varied numbers of learn samples were taught
in one orientation, and the resulting adapted memories
were tested against appropriate classes of test samples.
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In some cases, the test samples were the same learn samples
in similar orientations, to test scanner/processor repeat-
ability. In others, the test samples were the same samples
in different orientations to check invariance to translation
and rotation. Further, unknown (i.e., unlearned) samples
were tested for correct classification as well as invariance
to position. The results of these and subsequent tests on
the frequency-domain processor are detailed in another
section of this report.
1. System Description
A detailed theoretical and hardware description of the
frequency-domain system--the integral scanner, processor,
and punch-output- -is presented in the final report written
under The present description is concerned
with the output format of the processor data, the computer
program involved in the conversion of this data to a compat-
ible form for processing on a second, high-speed computer
system, and finally, the actual learn/recognize, CONFLEX-
simulation processing program. The functional block diagram
in figure 7 outlines the information flow of this frequency-
domain system approach.
It should be pointed out that the hardware implementa-
tion of an ideal automatic recognition system would combine
all of the components included in figure 7 with a more
sophisticated, mission-oriented form of input and output,
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DATA
INPUT
INTEGRAL
SCANNER
PAPER TAPE
PUNCH
PAPER
TAPE
CONVERSION
PROGRAM
PAPER
TAPE
LEARN/RECOGNIZE
PROGRAM
DATA (CONSOLE
OUTPUT TYPEWRITER)
FREQUENCY- DOMAIN
SYSTEM
COMPUTER
Figure 7. Block Diagram: Frequency-Domain System and Computer Analysis
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and without, of course, the paper-tape medium. The function-
breakdown approach was used in this program to provide
greater flexibility in testing and defining system-component
parameters.
Experience with the CDC G-15 program used in the time-
domain approach indicated the advisability of using the
SDS-925 computer for the learn/recognize processing. Because
tests involving a large number of samples showed a need for
more rapid processing than could be obtained from the G-15, an
adaptive learning program was written for the speedier
SDS-925, and a conversion of the paper-tape-punch output
format originally designed for processing on the G-15
became necessary. Computation time per sample was thus
reduced by an estimated factor of 40, and the resulting
more versatile program allowed closer examination of the
processes. The details of this development are discussed
in a later section of this report.
2. Computer Program
Conversion Program: The purpose of the conversion program
is: first, as mentioned above, to translate the punch
output into a tape input-format acceptable to the high-
speed SDS-925 computer used in the actual data processing;
second, to give each data sample an assigned class-code
by which a computed classification is checked with the
actual identification of the sample.
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It would be helpful here to review the output data-
format of the tape perforator,, described in the Punch Output
and Logic Section of the Final Report. Briefly,
a data sample is represented by a punch output of 400 D-cells
in either of three states, +1, 0, or -1, on five-level paper
tape. Each punch character contains two D-cells in the form
pictured below:
0-1
0+1
?e??eec?0???se??e??ieee HOLES
D-CELL 1 e 1
e ++1
ONE-PUNCH CHARACTER
Figure 8. Example of Paper-Tape-Punch Output Format
A punch in the fifth level indicates the presence of two
D-cells in that punch character. If no other punch exists,
both D-cells are in a 0-state, but any other punch in the
first or second level represents the disposition of one
D-cell, and in the third or fourth level, the disposition
of the second D-cell, according to the above figure. Taking
the hardware aspects of the CDC G-15 computer into consider-
ation, the paper-punch logic is designed to output a tape
block made up of seven punch characters plus a "tab" code
four times, followed by a "stop" code. The tape block,
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then, contains 56 D-cells. Four hundred D-cells of a given sample
require a total of eight tape blocks, the last of which
contains only four punch characters of information (i.e.,
eight D-cells, since 56 D-cells/tape block x 7 tape blocks
= 392 D-cells + 8 D-cells (of 8th tape block) = 400 D-cells/
data sample).
Figure 9 shows a generalized flow diagram of the
conversion program. This program accepts the 400 D-cells
and, using a table look-up approach, converts each D-cell
into an appropriate + 1, 0, or -1 representation acceptable
to the SDS-925 computer. The G-15 then punches these out
on five-level tape in ten blocks of 40 characters each,
each block separated by an "end of block" (carriage return)
code. The eleventh or last block is made up of one character,
the assigned class-code. The class-code is inserted, after
the conversion of a data sample, under program control
according to an instruction typed in at the beginning of
a conversion run, which designates how many samples are in
which class and the order of occurrence they follow.
Adaptive Learning Program: The approach used in the adap-
tive learning program written for the SDS-925 computer is
basically similar to that used in the G-15 program for the
time-domain processing described above. The main difference
in the two approaches is that, because of memory-size
limitations, the adaptive learning program can handle only
two-class, not four-class, experiments. Instead of the
1000 internally coded, two-state D-cells used with the
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ACCEPT:
NO. OF SAMPLES
NO. OF CLASS I
NO. OF CLASS 2
READ TAPE BLOCK
(54 D- CELLS).
IDENTIFICATION
SUBPROGRAM
CONVERSION TABLE LOOK-UP
FOR EACH D-CELL
STORE CONVERTED D-CELLS
O
PUNCH OUT BLOCK:
40 CONVERTED D-CELLS
"END OF BLOCK" CODE
PUNCH OUT:
CLASS CODE ,'END OF BLOCK CODE,
ACCORDING TO I D SUBPROGRAM
DECREMENT
NO. OF SAMPLES COUNTER
Figure 9. Conversion Program Flow Chart
-23-
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time-domain approach, here 400 three-state D-cell repre-
sentations of the samples, generated externally by the
frequency-domain processor and fed in via a format con-
version in the CDC G-15 computer, are employed. To
facilitate more rapid convergence upon an adapted memory
in a learning mode, the memory adjust ("Gain Adjust"
referred to above) routine was revised. Previously, an
error resulting from misclassification and/or threshold
requirements caused repeated adjustment of the memory
weights until a non-error condition existed, but in this
program any error causes only one memory adjustment. In
this manner, progressive learn samples in various classes
cause a more gradual adaptation of the memory weights,
hence a faster convergence.
The function diagram in figure 10 represents the
computer program. Four hundred D-cells and an assigned
class code on five-level paper tape are read in. Each
D-cell is equivalently multiplied by the disposition of
400 weights initially set to zero at the beginning of
any learn cycle. The weights are then summed, and the
magnitude of this sum is compared to a threshold set to
define the degree of certitude with which a sample is
learned. If the threshold is not exceeded, as would be
the case at the beginning of any learn cycle where all
weights are zero, the memory is adjusted by incrementing,
maintaining, or decrementing the weight counters accord-
ing to the +1, 0, or -1 disposition of the D-cell input
to that counter. If the threshold requirement is met,
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ADAPTED MEMORY
INPUT LEARN/ TEST I
D-CELLS
(+I,0,0R,-1 )
ASSIGNED
CLASS CODE
MEMORY ADJUST
(ONCE PER ITERATION DURING
LEARN PHASE ONLY)
Figure 10. Function Diagram: Adaptive Learning Program
(SDS-925 Computer)
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the sign of the sum (+ or -) is compared to the assigned
class code recorded on the punched tape with the D-cells
of the given sample. If this comparison does not hold,
the memory weights are adjusted in the manner described
above. In any case, whether the error involves the
threshold and/or the classification, the memory is adjusted
only once each pass for a given sample. Thereupon, the
next learn-sample tape is read in and the entire process
repeated until all learn samples are processed without a
memory adjustment (i.e., a pass involving no errors).
The generalized flow chart in figure 11 describes
details of the actual mechanics of the computer program.
With each sample input, the sample number and weight sum
over the threshold is typed out before any memory adjust-
ment takes place. To make errors more easily identifiable,
a routine was incorporated whereby any error appears as a
minus sum in the print-out. The routine consists of equiva-
lently multiplying the sum of the negatively identified
class (Class 1) by a -1 (i.e., the SUM = -SUM instruction
in the flow chart) and always subtracting the threshold.
In keeping with this convention in the memory adjust
routine, each weight is updated according to its input
D-cell and class code. If the sample is coded as a Class 1,
each + D-cell decrements its appropriate weight counter, and
each - D-cell increments its counter. For a Class 2 sample,
the + D-cell increments its counter, and the - D-cell causes
an appropriate decrement in its weight counter. In each
class, a 0-state D-cell maintains the existing state of its
weight.
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INPUT TAPE: ONE SAMPLE
(400 D-CELLS B CLASS CODE)
DO x
1-1,400
ACCEPT:
NO. OF SAMPLES,
THRESHOLD,
MODE
1 ii
LEARN ~~~~~ RECOGNIZE
UPDATE EACH WEIGHT (I)
ACCORDING TO D-CELL (I)
& CLASS CODE
OUTPUT (TYPE-OUT):
SAMPLE NO. S SUM,
ERROR/NO ERROR
INDICATION
DECREMENT
.NO. OF SAMPLES COUNTER
Figure 11. Adaptive Learning Program Flow Chart
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Several sense-switches are programmed in to provide a
variety of output. It is desirable to have the complete
print-out (for each sample, its sample number, its sum of
memory weights and error history, plus the pass number
and total errors per pass) when samples are being tested,
but limited print-outs can be used during extended learn
phases to conserve time. Provisions have been made also
to dump memory at any point in the learn cycle in order to
test against the unadapted memory and compare the results
with those from tests with a completely adapted memory.
The dump-memory provision also makes it possible to preserve
memories and later cross-test with samples of different tests.
3. Experiments
Two distinctively different types of experiments were
run. The first, called generalization, consisted of train-
ing the decision logic with images from two classes and
testing this decision logic with different images from the
same two classes. The second, called memorization, con-
sisted of testing the decision logic with the identical
imagery used for training the logic. In both types of
experiments, rotation and translation took place during
generation of the testing samples. Thus, for the results
derived from the generalization experiments to be meaning-
ful, it was necessary to have a standardized format for
the imagery.
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Figure 12(A) shows the random orientation of the
imagery within a frame. Figure 12(B) shows the actual
orientation of this imagery after a specific Rotation p.
It can be seen that after this rotation has occurred
the actual orientation of the imagery is unknown because
of the first incremental angle G.
In memorization experiments, image orientation is
not a problem because the target has been seen in its
original orientation. However, with a separate group
of images such as those used in generalization experi-
ments, some form of standard orientation must be main-
tained. Figure 13(A) shows a standardized image frame.
Figure 13(B) shows that a known rotation has rotated
this image through a known angle. Thus, if the perform-
ance in this experiment is related to rotation, it will
be quite easy to determine error rate changes with rota-
tion angle. However, if the imagery had not been
standardized, the rotation of the testing imagery in
relation to the rotation of the training imagery would
be completely unknown. Unfortunately, many targets do
not lend themselves to establishment of a standard
orientation. During the program, the major axis of the
imagery was aligned horizontally.
It is obvious that conducting a generalization experi-
ment is much more complex than conducting a memorization
experiment; therefore, most of the experiments were limited
to the latter type. However, it should be kept in mind
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(A)
(B)
Figure 12. Random Orientation of Imagery
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(A)
(B)
Figure 13. Standardized Orientation of Imagery
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that this program was not established as a target-
recognition program, but as a recognition-invariance
program. Therefore, the demonstration of invariance
can take place regardless of the type of experiment used
to demonstrate it.
Another facet of the experimental effort was
directed towards investigating the parameters of the
existing integral scanner. Tests were conducted to
examine the information content of single-sweep video
signals. Positive and negative transparencies of primi-
tive shapes in varying widths and field sizes such as
straight lines and line intersections, and other simple
geometric shapes (e.g., triangles, squares, hexagons,
circles, etc.) of a constant area of transmission were
produced for examinations of linear and angular resolu-
tion. Experiments with scanner noise and scanner repeata-
bility were also included.
An extensive pattern-recognition program requires an
extremely large, well-controlled, and representative data
developed a limited data base consisting of imagery in
2" x 2" super slide-holders. The imagery was assigned to
the data base on the basis of its content. Performance
of the target-recognition equipment during the program was
related only to the subjective content of the imagery.
base. During the present program,
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Other possible variations such as detail (characteristic
of individual image), context (the surrounding imagery),
density (number of targets), and image variables will all
have to be verified. Because it is possible to introduce
variations in performance throughout any experiment, an
extensive image-evaluation program was not warranted.
Such an analysis, either subcontracted or conducted using
GFE data, should be considered with any future program.
The characteristics required of the imagery selected
included:
1. The sensing signal would be transmitted by the
imagery.
2. The size of the target on the imagery was
approximately 1/10th the total frame size.
3. The imagery was of individual targets or groups
of identical targets.
4. The imagery was of targets with clearly delineated
outlines. Non-target imagery, such as opaque
edges and auxiliary information, was also included
with some of the targets. Table 1 lists the
imagery used in the experiments.
TABLE 1. IMAGERY USED IN EXPERIMENTAL PROGRAM
TARGETS: (Positive transparencies)
Tanks Dams
Runways Tactical vehicles (e.g., trucks,etc.)
Cities Railroads
Roads Buildings
Bridges Aircraft
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BACKGROUND: (Positive transparencies)
Natural terrain, varying from featureless terrain to that
having different levels of vegetation, shrub, and tree
cover as well as snow-covered and snow-patched terrain.
Bodies of water and shorelines.
PRIMITIVE SHAPES: (Positive and negative transparencies)
Geometric: Triangle, semicircle, rectangle, square,
pa rallelogram, trapezoid, pentagon, hexagon,
octagon, and circle.
Linear: Spoked figures varying from two (i.e., a straight
line centered on the field) to eight spokes, in sym-
metric and unsymmetric configurations. (Each spoked
figure has line widths of .025, .015, .005 and .002
inches.)
The integral scanning and video processing techniques
used on this program were designed to yield recognition
results invariant to image translation and rotation. The
main purpose of the program was to determine whether or
not this invariance was being realized. Two hypotheses were
tested. The first was that system recognition accuracy is
independent of image translation. The second was that
recognition accuracy is independent of image rotation.
Each hypothesis was tested for various imagery-classification
problems.
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The hypotheses were tested by means of the well-known
Chi-square test, a test discussed in most elementary texts
on statistics. This discussion, which is based on Cramer's
development,3 will point out certain properties of the test
that apply to this problem.
The Chi-square test is generally used to test the
hypothesis that a random variable X has a given distribution
function F(x). The expected values of the random variable
determined from the hypothesized distribution are compared
with values of the variable observed in an experiment. This
comparison is effected by dividing the range of the variable
into some finite number of mutually exclusive categories,
computing the probability of obtaining a value in each cate-
gory, and comparing the computed (expected) values with
experimentally obtained values. Actually, the hypothesis
that the unknown distribution function of the variable is
some distribution function giving the same probabilities
as the hypothesized distribution function, is essentially
what is tested.
Let the sample size be n and the number of categories
be k. If the (calculated) probability of obtaining a
variable value in group i is pi, then the expected number
of variable values falling in group i is npi. Let the
3 Harold Cramer, The Element of Probability Theory and Some
of Its Applications, John Wiley & Sons, New York, 1955.
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observed number of values in group i be denoted by f
The statistic X2 is defined as
X2 =
(fi-npi)2
npi
i=l
(1)
Obviously, a significant deviation from the expected values
tends to increase the value of X2.
The number of categories, k, determine the number of
degrees of freedom in the resulting X2 distribution. Since
we are subject to the linear constraint f = fi? there are
k-1 degrees of freedom. i=1
A level of significance for our test must be chosen.
For instance, we might say that we will reject the hypothesis
that our random variable has a certain distribution function
if less than p% of the X2 values have a value as large as the
calculated value. Thus, we have a p% probability of reject-
ing the hypothesis when it is, in fact, true. We can minimize
this probability, of course, but only at the expense of
increasing the probability of accepting the hypothesis when
it is false. Normally, a level of significance between one
and five percent is chosen.
For a p% level of significance, we accept the hypothesis
if X 2 s Xp and reject the hypothesis if X 2 > Xp. Here Xp 2
denotes the p% value of X2; i.e., the probability that
2 2
X > Xp is_P
100
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Now, suppose that we wish to test the hypothesis that
the recognition accuracy of our system is invariant to image
rotation; i.e., we hypothesize that the recognition rate p
is independent of the rotation of the image. Hence, the
error rate q = 1-p is also independent of the rotation of
the image. We can use the X2 test to test the hypothesis
that p and q are constant for imagery scanned at different
rotations. Suppose that we have made n. i classifications
at rotation r., i = 1, 2,..., k, and that f. is the observed
number of correct classifications at rotation r.. At each
i
rotation we have two categories: f. i correct classifications
and n,-f, errors. The expected number of correct classifi-
cations is nip and the expected number of errors is niq.
Since there are two categories, we have one degree of free-
dom, and we obtain a term
[(n_f)_nq]2 (finiP)2 ~ _ (f i-niP) 2 (2)
nip n i q niPq
However, there are k such terms contributing to X2. Summing
over the k rotations, we obtain
k 2
X 2 = L (finip)
niPq
with k degrees of freedom.
If we do not a priori know p, we must estimate p from
k
the sample. The total number of observations is n = n,
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(3)
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and the total number of correct classifications is f = ) f,.
i
Under the hypothesis that the probability of correct
recognition is constant, we obtain the maximum likelihood
estimate p* = f/n. Then q* = 1-p*. However, since we have
estimated a parameter from the sample, we must reduce the
degrees of freedom by one. Hence, we now have k-1 degrees
of freedom.
This test will be applied in the next section in order
to determine whether the probability of correct recognition
is invariant with respect to rotation and translation of
the imagery.
The Chi-Square test described above is the most power-
ful one that could be conducted on the existing data. The
test is used to determine whether there is a constant error
rate throughout the whole experiment. It is possible to
determine or establish a probability of rejecting a claim
when that claim is true. The probability of such an error
is called the level of significance. We are establishing
the amount of deviation that we will allow from a known
mean. However, in such a test, there is a danger inherent
in the acceptance of that hypothesis, because the probabi-
lity of its false acceptance cannot be obtained. Thus, we
are establishing a null hypothesis such that we are willing
to "reserve judgement" about its validity unless there is
clear evidence to lead to its rejection.
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We can establish the following criteria in a screening
test. The null hypothesis would be the following: Target
is present. Thus, we will establish the limits such that this
hypothesis will be accepted unless data is available to dis-
prove it. This level of significance could be called the
missed-target rate. Thus, in a screening test, we have con-
trol over the missed-target rate. However, because it is a
significance test, we have no control over the false-alarm rate.
As the missed-target rate or Type I rate is lowered more and
more, imagery will be indicated as possessing targets. If the
missed-target rate is made small enough, then every piece of
imagery will be inspected.
In most military pattern-recognition decisions, it is
necessary to specify both the acceptable false-alarm rate
and the acceptable missed-target rate. Because the missed-
target rate or the probability of Type I error must also be
controlled, a loss matrix must be established. Costs are
associated with both the false-alarm rate and the missed-
target rate, and a balance is struck between the two. Once
we have established both error rates, we no longer have a
significance test. Thus, we accept the null hypothesis of
the presence of targets in two directions.
It is sometimes useful to fit distributions that are
either univariate or multivariate to the data. Once such
distributions have been fitted, it is possible to calculate
any of the potentially useful moments. In our analysis of
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the time-domain filtering equipment, it was possible to form
histograms of the output data and to determine both which
filters were most significant and which memory weights contri-
buted to the decision. The frequency-domain filtering did
not lend itself to such an analysis because the only output
from the equipment was D-cell values. The original number
of D-cell values were cut from 5000 to 400. Because of the
slow paper-tape output from the frequency-domain filtering
equipment, it would have taken excessive amounts of time to
punch out 5000 D-cells. However, the equipment was still
designed to inspect the output of each filter.
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The results of the experimental program described above,
which involved more than 4500 samples, are presented according
to three general categories. First are the General Results,
which indicate the screening capability of the system approaches
used by presenting the percentage of samples correctly classi-
fied. Because of the vast amount of test-result data, the
General Results combine all of the individual experiments,
memorization as well as generalization, and include all
samples, regardless of the time-domain tests, where two-class
experiments were not used exclusively. Next, the results of
any diagnostic tests, designed to analyze the validity or
strength of various aspects of the overall approach, are pre-
sented. Finally, the invariance of the integral scanner
approach to translation and rotation is described in a
statistical analysis of the results of all of the varied
experiments.
1. General Results
Initial three-class experiments resulted in an overall
81.3 percent correct classification. These were generaliza-
tion experiments; ie., the samples tested were not included
in the learn phase of the tests. In addition, a random trans-
lation and rotation of the samples were tested. In detail,
the results were.- aircraft, 96.8% correct; crossroads, 67.9?
correct; bridges, 60.0% correct.
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The second set of generalization tests was limited to
two classes to permit comparison with subsequent identical
experiments using the frequency-domain system. Controlled
rotations of the test samples were also included in these
experiments. Basically, a target-versus-background discrimi-
nation was tested, using tanks as targets and natural terrain
and other man-made objects as background. Correct identifica-
tions were obtained for 88.9% of the tank samples and 61.4%
of the background for a weighted, overall correct classification
of 84.3% for these tests.
2. Diagnostic Tests
As mentioned in the description of the time-domain analy-
sis, the video waveform is matched to a set of ten orthogonal-
function property-filters, each implementing a particular
weighting function. Interchangeable resistor boards were used
in defining the weighting functions. For the majority of ex-
periments, the reference-function resistor-boards used defined
a series of ten orthogonal sine functions. A second set of
boards defining ten orders of Laguerre functions were also
constructed to permit examination and comparison of the outputs
of each filter for the different classes of targets tested.
This was, in effect, testing the strength of each filter in
distinguishing various classes.
First, for each of the ten filters and each of the two
target classes, histograms were used to record the number of
filter-output counts falling in various counting intervals
(e.g., 0-9, 10-19, 20-29, to the maximum counter output).
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Each increasing interval represents a higher level of corre-
lation between the video input of the sample and the weight-
ing function of the filter represented by the output count.
For all of the intervals, the count total in a given counting
interval in one class is compared to the total in the same
interval of the second class. In this manner, an overall
correlation between one class and the other for each filter
is computed. For good class discrimination, this correlation
should be low. Hence, an examination of the correlation
values of each filter indicates how effective that filter
is in distinguishing between the two classes tested.
Figure 14 shows the results of the computations when, in
one case, the filters implement ten Laguerre functions and
in the other, they implement ten sine functions. The data
in this figure shows that the sixth Laguerre function and
the second and third sine functions are the most effective
in distinguishing between tanks and background and that the
sixth through tenth sine functions have lesser effects.
Since the average of the ten Laguerre-function filters is
0.66 as compared to 0.76 for the ten sine-function filters,
one can conclude that the former functions would be more
effective in correctly classifying tanks and backgrounds.
From the foregoing, it is easy to see that an optimum
set of filters can be selected to distinguish between any
given classes of targets. The choice of weighting functions
would be determined by the selection of targets to be classi-
fied by a given recognition system, and the number of these
functions would define the degree of certitude with which a
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REFERENCE= LAGUERRE FUNCTIONS
MAXIMUM 1.0
- ---------------------------
MAXIMUM 10
CORRELATION
Figure 14. Correlation of Filter Outputs for
Tanks-Versus-Background Experiments
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classification is made. The development of an operational
system using a time-domain and matched-filter approach would
include such an assessment in the filter design.
3. Statistical Analysis
The primary emphasis of this program was on frequency-
domain processing, but sufficient data was collected to
perform the following test for rotational invariance under
time-domain processing. The system was trained to recognize
tanks against a background of nontargets, then a second set
of tank imagery was processed through the system with the
imagery oriented at controlled rotations from a fixed standard
orientation. The following results were obtained:
TABLE 2.
TIME-DOMAIN STATISTICAL DATA
ROTATION
00
900
1800
2700
CORRECT
45
45
45
43
178
ERROR
5
7
7
9
28
TOTAL
50
52
52
52
206
The maximum likelihood estimate p , for the probability of
correct recognition is p = 206 = 0.86. Let us hypothesize
that the probability of correct recognition, p , is invariant
to image rotation. This hypothesis will be tested by the
X2 test at a 5% level of significance. Xp2, for three degrees
of freedom and p = 5,is 7.815. Calculating the X2 statistic
as defined in equation (3) of section II-D on the basis of
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t
Table 1, we obtain X2 = 1.29. Since )(2 < )(p2, we accept the
hypothesis that the probability of correct recognition is
invariant with respect to image rotation.
B. FREQUENCY DOMAIN
1. General Results
To avoid the confusion of many pages of figures and
more clearly present the screening ability of the equipment,
effort has been made to combine the large amount of data of
the frequency-domain tests into two general classes: targets-
versus-background tests, and targets-versus-targets tests.
The former represents the basic level of discrimination; i.e.,
between man-made objects (e.g., tanks, roads, buildings, etc.)
and natural terrain. The latter is a higher level of screen-
ing between target types, such as roads-versus-bridges,
clearings-versus-woods, etc.
Targets-versus-Background:
The detections in each category are represented in
Table 3(A); the percentages are in Table 3(B). The Type I
error percentage is 23.1%; the Type II error percentage is
36.8%. The Type I error (missed targets) can be controlled
during the experiments, but reduction of it increases the
Type II error (false alarm). The costs associated with
varying these errors determine their final values. Usually,
missing a target incurs a larger penalty than causing a false
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TARGET
NON-TARGET
TOTAL
TARGET
362
150
512
NON-TARGET
109
258
367
TOTAL
471
408
879
(A)
TARGET
NON-TARGET
TARGET
76.9%
36.8%
NON-TARGET
23.1%
63.2%
(B)
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alarm. For example, crying."wolf" over missiles in Cuba
would have been embarrassing; missing them could have been
disastrous!
Targets-versus-Targets:
The results of these tests are presented in Table 4,
where the percentage of correct classification is listed
for each of the two target types tested in each experiment.
In evaluating these results, it should be remembered that,
since these are two-class experiments, a misclassified
Class I was identified as a Class II and vice versa.
TARGET CLASS I
CORRECT
TARGET CLASS II
CORRECT
Runway #1
84.2%
Runway #4
95.0%
Runway #2
94.7%
Runway #3
100. %
Roads
53.9%
Bridges &
Dams
78.9%
Clearings
63.2%
Woods
58.0%
Contiguous*
I
84.0%
Continuous
#I**
60.0%
Contiguous
II
72.3%
Continuous
#11
71.1%
Point-targets 65.4%
Extended-targets
77.8%
Runways 70.0%
Urban areas
75.0%
Runways 16.7%
Orchards
96.7%
Runways 88.0%
Roads
80.0%
* Contiguous targets are man-made objects, mainly buildings
and vehicles, the area of which occupies 25% to 50% of the
scanner's field-of-view.
** Continuous targets are railroads, highways, bridges, and
dams, all of which extend into and out of the field-of-
view.
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2. Diagnostic Tests
As the basis for investigating the parameters of the exist-
ing optomechanical scanner, sets of imagery representing various
primitive shapes were produced. One set is of geometric shapes,
all with equal areas and with bounds falling within the scanner
field-of-view. The background of each test slide is opaque
in respect to the transparent area of the geometric figure on
that slide. These geometric shapes were designed for examina-
tion of the angular resolution, so that as many other parameters
as possible were kept constant (e.g., equal areas of light
transmission for each shape, limiting shapes to within the
scanner field-of-view, etc.).
Essentially, the angular resolution of the integral scan-
ner is a measure of the ability of the scanner to distinguish an
n-sided polygon from an (n-l)-sided polygon. The two-class ex-
periments were set up to distinguish between the shapes of most
similar characteristics. To avoid any variation caused by sys-
tem output, three samples of each figure were used as learn
samples for that figure. Once the memory had adapted to the
six learn samples (i.e., three per class), various rotations and
translations of the figures were tested. The classes of figures
tested and the percentage of correct classifications of the
rotated and translated test samples are presented in Table 5.
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CLASS I
CORRECT
CLASS II
CORRECT
Triangle
88.0%
Semi-Circle
92.0%
Rectangle
84.0%
Square
54.2%
Parallelogram
88.0%
Trapezoid
100%
Pentagon
100%
Hexagon
80.0%
Octagon
100%
Circle
100%
More important in this experiment, however, was an examina-
tion of the learning curve (the rate at which the memory was able
to adapt to the shapes taught). Most laudable was the rapid
convergence of the adaptive memory in distinguishing an octagon
from a circle; only three learn passes were required. More than
twice that number of passes were required in tests comparing
shapes that to the human observer appear to be more easily dis-
tinguishable (e.g., triangle versus semicircle, etc.). A close
investigation of the error rates involved in the learn cycle
showed that scanner repeatability played some part in the varying
convergence rates. As evidenced by the overall results, all of
the geometric shapes were well within the angular-resolution
capabilities of the scanner.
A more rigorous test of both the angular and the linear
resolution of the integral scanner was implemented using linear
spoked figures. The figures varied from two ( i.e., a straight
line centered on the field) to eight spokes in symmetric and
unsymmetric configurations. The unsymmetric configuration
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involves a discrete angular displacement of one spoke of the
symmetric figure. Each spoked figure was produced in line
widths of 0.25, 0.15,.005, and .002 inches, and tests were
run to discriminate between symmetric and unsymmetric configur-
ations of the same figure. In addition, various translations
and rotations were tested. Here, also, three samples of each
figure were used as learn samples.
The rate of convergence to an adapted memory clearly
showed, in all experiments, the ability of the scanner to
discriminate between the symmetric and unsymmetric configura-
tions. However, the overall results with the test samples
produced a 52.1% correct classification level. This, although
better than chance, seemed to indicate that the information
content of the video signal, particularly for the figures of
the smaller line widths, closely bordered the video noise-
level caused by non-uniform slit-widths in the scanner; i.e.,
slit-width dimensions approximating those of the spoked
figures. This area is definitely one that could be improved
by using an electronic flying-line scanner.
3. Statistical Analysis
The following test was conducted to determine the
effects of imagery rotation on frequency-domain analysis.
The system was trained to recognize buildings against a back-
ground of cultural features, then the imagery was rotated
through a number of angles. The recognition rate at the end
of the rotation was noted, and the results are presented in
Table 6.
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TABLE 6. FREQUENCY-DOMAIN STATISTICAL DATA
(ROTATION)
Rotation 300 600 900 1800 2100 2400 2700 3000 3300 Total
The maximum likelihood estimate, p , for the probability of 94 - correct recognition is p = 135 0.70. Again, our hypothesis is
that the probability of correct recognition, p , is invariant to
image rotation. Using a five percent level of significance, we
find the X p 2 is equal to 15.507 for eight degrees of freedom.
Calculating the X2 statistic on the basis of Table 6, we obtain
= 9.85. Since X < Xp
X we accept the invariance hypothesis.
2 2 2
Finally, a test was run in which a class of several types
of targets was compared with mixed backgrounds. After the system
was trained on imagery presented at a standard orientation,
recognition tests were run with the imagery both translated and
rotated. The results are presented in Table 7.
TABLE 7. FREQUENCY-DOMAIN STATISTICAL DATA
(ROTATION-TRANSLATION)
Orientation 450 270? 0.2" 0.2"
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The maximum likelihood estimate for the probability of correct
recognition is p = 120 = 0.77. The hypothesis in this case
is that the recognition rate is invariant to both rotation and
translation of the imagery. The 5 percent value of X2 for three
degrees of freedom is 7.815. In accordance with Table 7, X2 is
0.44. Hence X2 < X p 2, and our hypothesis is acceptable.
The above results support the hypothesis that the recogni-
tion capabilities of the system are invariant to rotation and
translation of the imagery.
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As has been explained in the introduction of this report,
several changes in final objectives took place during the pro-
gram as a result of sponsor request. These changes affected
primarily the physical products of this program. Although it
had originally been planned to supply diagrams and fabrication
data for an automatic target recognition system, such highly
detailed data is not now included due to redirection of the
effort by the sponsor; however, the basic information necessary
to build such equipment is included in this report. Based on
our research our conclusions are as follows:
1. The equipment can extract parameters from visual imagery
that are representative of the imagery and are invariant to
translation and rotation.
2. A scientific basis for the fabrication of a screening
device has been established.
3. Discrimination of man- and non-man-made or significant
and nonsignificant imagery can be made by automatic equipment
with a high degree of accuracy.
4. Automatic equipment, combined with the cognitive pro-
cesses of the human interpreter, would be a potentially very
significant combination for image processing.
5. Improper training or "over-training" may have occurred
because the decision logic was trained until no errors were pres-
ent. This implies that the examples shown to the training
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routine included not only good examples of the class but very
poor examples of the class. Thus, when the actual testing pro-
gram was run, a rotated and translated image was being compared
with good and poor examples of a class. The degradation in
the class model set up by this method may have caused some of
the error.
Based on our research, our recommendations are as follows:
1. Fabrication of a prototype screening device that can
be used with humans to test all kinds of militarily significant
imagery.
2. Continuation of research in the fields of scanning,
preprocessing, and decision logic.
3. Existing equipment should be fully exploited and con-
tinued extensive testing be conducted on it.
4. An electronic analog of the opto-mechanical scanner
should be developed. The capability of such a scanner would
exceed that of a serial flying-spot scanner and only the optical
techniques of spatial filtering and hologram generation could
come close to matching the versatility of the integral scanning
approach to image processing. In addition, the electronic or
opto-mechanical integral scanner is not affected by those pro-
blems which affect the optical systems used in the other systems.
5. The two different kinds of video processors should be
compared. Thus, the effects of varying image quality, image
content, and target density, on the performance of the time-domain
filtering and the frequency-domain filtering could be examined.
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Each one of these prenormalizers may have a group of targets
for which it will have optimum performance. The time-domain
analyzer also is flexible in that the weighting functions can
be changed easily. Thus, the current sine wave orthogonal func-
tions can be replaced with other orthogonal functions. Some
comparisons have been made using Laguerre functions, and further
investigation would be beneficial.
In summary, it is evident that many useful ideas have been
generated by this program. These ideas will permit the fabri-
cation of a prototype of an operational imagery screener. How-
ever, what is more important is the fact that a capability has
been established that has only begun to be exploited. With a
suitable, well-controlled data base of 10 or 20,000 examples of
imagery, meaningful results can be made immediately available
to the experimenters. It is felt that it would be a waste of
the sponsor's investment not to continue to use this equipment.
While further tests are conducted using this existing hardware,
the fabrication of an all-electronic flying-line scanner could
commence for later incorporation in the system.
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INTRODUCTION
"Prenormalization" designates the removal of
unessential variability (i. e. , variability which has
no effect on the class membership of a pattern)
from patterns prior to pattern-learning and pattern-
recognition operations in order to achieve invari-
ance to translation and rotation. For example, in
target-detection and target- classification applica-
tions of prenormalization, knowledge of the posi-
tion and orientation of the target with respect to
some limited viewing is unessential information.
THE INTEGRAL SCAN
A means for extracting image data and re-
taining the essential characteristic of image con-
tinuity has been developed aturing a study
of complex image analysis. T e study, beginning
with analyses of various optical transforms, re-
sulted in a novel scanning technique utilizing an
integral rather than a point scan of the image
field. This prenormalization approach is based on
the time-domain analysis of signals obtained by the
integral-scan sequence developed ati
The ensuing brief description of the integral-
scan technique and of the motivation behind it is
presented with reference to figures 1 and 2. Figure
1 shows an idealized airfield being scanned by an
extremely narrow slit. The slit is being moved
across the viewing field bounded by Yp, Y1, and X0,
Xl at a certain velocity. The output due to the air-
field is also shown in figure 1. It should be noted
that the integral scan maintains image continuity
and connectivity information that is normally lost
with the conventional spot-scanning techniques. An
important feature of this method of scanning is that
the waveshape produced by an object is invariant
to translation. If the airfield is moved up or down,
the entire waveshape remains unchanged; if the air-
field is moved right or left, the waveshape due to
the airfield remains unchanged and only its time of
occurrence varies.
In figure 2, another shape of airfield is being
scanned. by four slits in the order indicated. The
waveform resulting from the scanning operation
is also shown. If the airfield is rotated within
the resolution capability of the scanning arrange-
ment (i. e. , in 45? steps), the only change that
results is a change in the phase (i. e. , time-order)
of the signal. If a large number of slits, each
at a slightly different angle,are used, then the
rotational resolution of the scanning process will
be quite good and the only effective change due to
any rotation would be a phase change (i. e., change
in time-order).
Thus, for the integral scan with many slits
having small angular displacements, the only
changes caused in the output waveform are changes
in the time of occurrence of the waveshapes due
to the objects in the field of view. Therefore, if
a method of processing this waveform independent
of the time of occurrence is used, a set of mea-
sures (or properties) invariant to translation and
rotation can be obtained.
SYSTEMS UTILIZING THE INTEGRAL SCAN
The integral-scan approach to pattern recog-
nition has been incorporated in several research
programs at rimarily in the fields of
character recognition and prenormalization of
aerial reconnaissance photography. Under these
programs, several scanners have been built and
are presently in operation in conjunction with
electronics using time-domain and frequency-
domain analysis, and subsequent computer pro-
cessing to examine and distinguish various classes
of primitive shapes, character fonts, and aerial
imagery. At the same time, examination of the
parameters and limitations of these existing
scanners has led to the design of new and improved
methods of implementing the integral scan for use
in future systems.
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OUTPUT
AMPLITUDE
SCANNING
SLIT
SCAN*3
SCAN #2
SCAN
#1
SCAN
#2
SCAN
#3
SCAN
#4
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GENERAL COMMENTS
ON
AUTOMATIC TARGET-RECOGNITION RESEARCH
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GENERAL COMMENTS
ON
AUTOMATIC TARGET-RECOGNITION RESEARCH
The time for actually fabricating the equipment is here!
Technology has increased the generation of reconnaissance or
intelligence information, but the photointerpreter is still
limited to traditional PI aids. Thus, very little has been done
to alleviate the "dumbbell effect" (figure 1). The ability to
utilize the large amount of imagery now being acquired is severe-
ly restricted by current data handling capabilities. Conse-
quently, the data necessary for military decisions cannot flow
through to the users in a short enough time to be wholly useful.
Recent experiences have shown that emergencies can quickly strain
imagery-interpretation capabilities and thus further restrict
this flow, but this appendix will make no effort to discuss these
problems because they fall outside the scope of the research.
The existing equipment will be quite valuable in the field
of automatic research. Because the parameters used in this pro-
gram to describe the imagery are being generated independent
of a decision logic, it is possible to use them in any available
decision logic. Thus, the integral scanner can be used as a
standardized parameter for testing differing decision logics.
This application would not necessarily be based upon its invari-
ance characteristics but only upon the uniqueness of the equip-
ment and the parameters it generates.
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DATA COLLECTION
USER REQUIREMENTS
FOR IMAGERY
Figure 1. Dumbbell Effect
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?
The original criteria for selecting test imagery should
be supplemented with an approach having a sound theoretical
basis. First, sufficiently high-quality imagery should be used
in order that system performance can be evaluated under condi-
tions of uniform degradation. Target density and target detail
should be varied. Low target-density could be defined as one
target per photograph; high target-density, as two or more tar-
gets. Low target-detail could be defined as seven or less sig-
nificant features per target, which could be clearly seen on the
negative; high target-detail could be regarded as eight or more
such features. These photographs should also be processed to
various levels of contrast, resolution, and scale.
Of equal importance, looking at the broader picture of the
research effort in automatic target-recognition, is the estab-
lishment of a common data base. Several organizations are work-
ing on varied approaches to this problem. Ultimately, the effort
should converge at a common point, the recognition of a target.
But what is a typical target? Because of the nature of aerial
photography, this can be only generally defined. But despite
this factor, a common data base can be established for use in
comparing the efforts of individual companies. Without such a
data base to use as a standard, it would be possible for one de-
vice to classify a photograph as an airfield and for another
device to classify the same photograph as a bridge. The inbred
idiosyncrasies of each company's target-recognition device seem
to have been the determining factor in what photography has been
used for testing, since good results promote any program. For
this reason, it is certainly difficult for a contractor to com-
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pare the research efforts of various companies, or for those
companies-themselves to know where they stand in the overall
picture.
Effort should be made, in cooperation with the agencies
that are the sources of the aerial photography needed, to estab-
lish a common data base to provide this basis for comparing all
target-recognition equipment. Since there is no representative
aerial photograph of any target, this data base could take the
form of a library of indexed film rolls containing multiple
examples of many classes of targets under varied conditions
(e.g., rotation, scale change, environment, seasonal change,
film quality, etc.). Were such a data base available to each
company involved in target-recognition work, it would benefit
not only the sponsor but the other individual companies. Dupli-
cation of effort could be avoided, and system merits could be
more equally weighed.
Another area of importance is a review of psychological
test programs. These programs have performance data for human
beings with various kinds of stimulus materials. With such
materials, it is possible to compare the performance of the
automatic equipment with that of the unassisted human. The best
approach would be to have direct man-versus-machine comparison.
However, until such experimentation is possible the use of
previously scaled imagery will be sufficient. There are two
screening experiments for which human performance has been
established. One, conducted by
under Air Force sponsorship, has
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211
already been reported in RADC-TDR-63-313. The second, conducted
It appears that the IBM report will contain much human perfor-
mance data that would be useful for these purposes.
under Army sponsorship, is in the approval stage.
Several programs have been conducted to demonstrate a
relationship between image parameters and photointerpreter per-
formance. Unfortunately, most of the programs have been incon-
clusive, partially because the kinds of image parameter measure-
ments that can be made are almost endless. Because it is de-
sired to demonstrate that automatic equipment is competitive
with human performance , selections of image parameters should
be restricted to parameters already used in experiments on human-
factors.
There are two main sources for information on such experi-
ments. The United States Army Personnel Research Office has
started an extensive program in screening research that includes
contracts to outside sources as well as in-house research. These
programs can provide guidelines for the experiments that should
be conducted on automatic equipment. It has always been felt
that the image quality of reconnaissance photography would affect
the performance of the photointerpreter. Resolution, contrast,
and scale also affect this performance. The Air Force at Wright-
Patterson is supporting a program to develop other measures of
photo quality. About all that has been discovered from these
various experiments is that it is difficult to relate photointer-
preter performance directly to any single or group of image param-
eters. However, it would be wise to make some quality measure-
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V
ments on the imagery as a data base is developed. Until some
kind of agreement can be reached by the experimenters, an
extensive image mensuration program would be a waste of time.
The best approach would be to limit measurements to target
contrast and to image density, detail, scale, content, and con-
text.
Four statistical values which can be useful as diagnostic
tools for determining the characteristics of the distributions
of imagery parameters are measures of the following:
1. Central tendency.
2. Dispersion, including range, average or mean deviation,
or standard deviation.
3. Skewness.
4. Kurtosis.
These measures will be valuable if there is a univariate distri-
bution. If there are multivariate distributions, the calcula-
tions of the covariance matrix can be used as a useful diagnos-
tic tool.
Many statistical approaches have been developed that can
be used in pattern-recognition experiments. However, the appli-
cation of many of them depends on a greater volume of more con-
trolled data than is now on hand for testing.
Many considerations for future research have been included
in this appendix, some generated as the result of this program
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and some reflecting the general attitude of
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^
V
towards this area. It appears that the successful conduct of
research now depends almost solely on an ample supply of imagery.
Every effort should be made to assure that such a supply is
available to the researchers.
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