PERCEPTRON (ref. 1, 2) is a general classname for a potentially large and diversi­

fied family of machines capable of perceivingand recognizing patterns. According to a re­cent definition of the ter m by its originator"it is a convenient name to encompass all of theneural net type of "intelligent" machines.However, in this paper, the concern is pri­marily with the simpler mechanisms, par­ticularly those which have been described inthe literature under this name. The intent ofthis paper to to discuss preliminary experi­ments relating to photo interpretation, re­recently completedb on a laboratory modelperceptron and to consider some of the im­plications and probable extensions of thework.

The machine on which the experimentswere performed, called the MARK I Percep­tron, can be taught to recognize simplegraphic forms. It tends strongly to generalize

Perceptron Applicationsin Photo Interpretation*

ALBERT E. MURRAY,

Principal Research Engineer,Cognitive Systems Grp. Physics Div.,

Cornell A eronautical Laboratory, Inc.,Buffalo 21, N. Y.

ABSTRACT: Perceptron is a general name for a large family of perceiving and/orrecognizing automata. Many of these, when given suitable sensory equipment.resembling an eye, and consisting of a lens and a retinal mosaic of light-sensitiz'eelements, may be "taught" to reliably recognize or classify simple visual pat­terns. A laboratory model of a small and relatively simple perceptron has beenused in experiments to estimate its potential in photo interpretation. In thisprogram, it demonstrated an ability to learn, from a few examples, to visuallyidentify simple, compact, well-defined objects, either alone or in company withother objects. The test objects successfully used ranged from alphabet lettersthrough simple geomet'ric figures to simplified images from aerial photographs.Superimposed images of other well-defined objects or of relatively disorganizedbackgrounds do not seriously degrade the performance except in very special orsevere cases. A plan is outlined fo·r further work toward development of anautomatic photo-classification machine.

from a sample of training forms to givecorrect, identifying responses to any newforms which are similar to those in which ithad experience during training. By suitabletraining on a few examples, the machine canalso be taught to separate or distinguishforms which are generally similar but whichpossess certain kinds of distinguishing charac­teristic(s) .

The MARK I, which is representativeof all elementary perceptrons, was notdesigned for specific tasks such as photointerpretation, but rather as a minimummodel for some portion of the (visual) percep­tion mechanism of a brain. It represents thesimplest organization, with the smallestnumber of detailed specifications, of neuron­like elements, that might be expected toexhibit brain-like properties. Future photointerpretation research in our laboratory willemploy machines which are related to percep­trons but whose development is aimed spe-

• F. Rosenblatt, in C. A. L. Report VG-1196-G-8 cifically at applications in photo interpreta-(Tentative Title: Neurodynamics-Perceptrons tion.and the Theory of Neural Nets). The experiments which will be discussed

b Mr. John Hay participated substantially in the herein were concerned with operations whichdesign and conduction of these experiments. Worksupported by Office of Naval Research, Contract were deemed to be typical of, or fundamentalNo. Nonr-3161(00), Project NR 387-026. to, those required in the interpretation of

* Presented at the Society's 27th Annual Meeting, The Shoreham Hotel, Washington, D.C., March19-22, 1961.

627

628 PHOTOGRAMMETRIC ENGINEERING

ALBERT E. MURRAY

aerial photographs. It is hoped that thiscontinuing program may lead to the develop­ment of machines capable of performing alimited number of tasks which are selectedfor the purpose of reducing the burden whichlarge quantities of survey data would placeon trained personnel. A simple but worthyautomation goal would be the ability to scanlarge quantities of mostly uninterestingphotographic material in search of only onekind (or a small number of kinds) of objector feature, or one class of topography orterrain. The ability to recognize simpletarget" forms in relatively featureless en­vironments is not a trivial task for a machine,but it is a capability which should be inherentin the class of perceptual mechanisms towhich the MARK I Perceptron belongs.Consequently, the experiments reported here­in begin with this problem, at a very formaland idealized level. They advance, through aseries of tests of ascending difficulty, towardthe recognition of practically interestingtarget forms in real and complex surroundings.

c The term "target" as used in this paper, isintended to replace the phrase "one (kind of)object, feature, topography or terrain:' and is. toindicate the object, etc. as one for which we Wisha recognition machine to give an identifying re­sponse or alarm. It is not intended as a militaryterm.

However, since the sensory apparatus of theMARK I severely limits ground resolutionand has no gray scale, the use of actual airphotos was prohibited and could only beapproximated.

PRELIMIN ARY EXPERIMENTS WITH

SIMPLE FORMS

Early in the program there were run,several experiments of a type called "one-vs­many," in which the machine is trained torespond to some one shape, called "target."Following such training, it is tested on targetsand many other shapes as well. It is expectedto respond to the target shape whenever itappears (in any position or size permitted bythe experiment) and to ignore all other shapes.

The first experiments of this sort usedalphabet letters, with X as the target, andA, E, K, N, 0, R, S, T, U as non-targets, allof which were permitted to appear in anyupright position at one standard magnifica­tion. Tests showed a perfect detection rate forX's but a high over-all false alarm rate fromthe non-targets, distributed as shown inTable I.

In the next experiments, a group of subjectshapes was chosen which were intermediate ingeneral character between alphabet lettersand objects on the ground, as seen from theair. The result was an improvement in dis­crimination, evidenced by a drop in the false­alarm rate. The shapes were an X-shapedcross, a circle, a square, a triangle, an arbi­trary hatchwork of lines, and a moderatelyirregular distribution of irregularly shapeddots. They are called, respectively, X, 0,0,6., Lines, and Dots, and are pictured inFigure 1. The Lines and Dots patterns werelarge enough to more than fill the retina, andvarious portions of the patterns were arbi­trarily chosen and presen ted at different timesin anyone experiment. They were also usedas distributed backgrounds in later experi­ments.

In one set of experiments, X was the targetand 0, 0, D., Lines, and Dots the non­targets while, in a sister set, 0, was the targetand X,D, D., Lines and Dots were the non­targets. The results are given in Table II.

Note in this table that a substantial part of

TABLE I

FREQUENCY OF "TARGET" RESPONSE

X A E K N 0 R S T U Detection Rate 100%---------------

10/10 3/10 10/10 1/10 3/10 10/10 8/10 7/10 3/10 8/10 False Alarm Rate 59%

PERCEPT RON APPLICATIONS IN PHOTO INTERPRETATION

TABLE II

ExPT. 40. X AND 40.5

629

StimuliDetection

False-

RateAlarm

X 0 D f::. Lines Dots Rate------------------

Petceptron Trained on X 78/78 3/79 7/24 7/24 10/49 6/24 1.0 .17------------------

Perceptron Trained on 0 13/78 79/79 17/24 8/24 12/49 7/24 1.0 .29

the false alarms for the O-trained perceptroncame from D, which is the figure mostresembling O. Similarly, the X-trained ma­chine experienced the most difficulty with"Lines" in whose random hatchwork aremany incidental X's. The false-alarm rate is

considerably improved over that obtainedwith alphabet letters, but is still uncomfort­ably high. Now, it may be that, in some prac­tical circumstances, detection may be of suchimportance that one would be willing to pay afairly high price, in false-alarms, for extremely

xx

oo

LIN ES

oo

'", -.II.. , .. ;. ;•••~ • • f,'.' ',',­I '".'.'" . "'.'';' ,'..

DDTS

FIG. I A. Actual Patterns Projected onto Mark I retina.

X o

LINES

o

DOTS

FIG. lB. Pictures show how Projected Pattern in Fig. 1A "looked" to the Perceptron because ofQuantization by the Low Resolution Retina.

630 PHOTOGRAMMETRIC ENGINEERING

TABLE III

ExpT. 40.5 (No NEGATIVE TRAINING)

StimuliDetection

False-

RateAlarm

X 0 0 6 Dots Lines Rate------------

Perceptronf

I-Fix 12/12 1/12 5/12 4/12 3/12 3/12 1.0 0.27Trained ------------------

Pas. on X l 4-Fix 6/6 0/6 0/6 1/6 0/6 0/6 1.0 0.03------------

Perceptronf

I-Fix 3/12 12/12 12/12 4/12 5/12 4/12 1.0 0.47Trained ------------------

Pas. on 0 l 4-Fix 1/6 4/6 4/6 5/6 0/6 0/6 .7 0.33

\_----------_/FREQUENCY OF "TARGET" RESPONSE

high detection reliability for bona fide targets.On the other hand, there are means, discussedbelow, for reducing false-alarms in machinesof this type.

MULTIPLE LOOKS ANDNEGATIVE TRAINING

In conventional operation, an output re­sponse is asked for and is given as soon as astimulus image is fixated upon the retina ofthe MARK 1. On the other hand, peopleoften make several fixations upon a patternbefore making a final decision. To test thebenefit of multiple fixation, stimulus patternswere projected in four successive, system­atically related, positons on the retina, and anultimate response was calculated for each setof four immediate responses. This techniq ueis referred to by the term "4-fix", in Table III.

Some substantial reduction in false-alarmrate was obtained from 4-fix responses. Addi­tional reduction should also be obtainablefrom negative training. In this case, themachine is taught not only to give positive

responses to targets, but to give no suchresponses to non-targets. In general, the classof non-target objects likely to be encounteredby a photo interpretation machine is verylarge compared to the class of targets. Con­sequently, while it may be practical to trainon a large fraction of all the possible targetstimuli, this is not likely to be practical fornon-targets. Training, no matter how exten­sive within reason, is not likely to cover morethan a small sample of the possible non­targets. Therefore, an ability to generalize iseven more important in the reduction of false­alarms than is actual target recogni tion.

I n the next ex peri men t, a perceptrontrained to recognize X also received somenegative training on a small sample of 0,Lines, and Dots. \\Then trained to recognize0, it also received some negative training onX, Lines, and Dots. Neither perceptronreceived very thorough training on any of itsnon-target forms, nor did either perceptronreceive any training on 0 or 6. The results,are shown in Table IV. They may be com-

TABLE I\'

EXPT. 40.6 (SOME NEGATI\'E TRAI~[1\(;)

Sti1l1uliDetection

False-

RateAlarm

X 0 0 6 Dots Lines Rate------------

Perceptron

{I-Fix 11 /12 0/6 6/12 3/12 5/12 1/12 0.9 o 25

Trained --------------------- ---------Pas. on X 4-Fix 6/6 0/6 0/6 0/6 0/6 0/6 1.0 0

------------Perceptron

{I-Fix 0/6 9/12 4/12 3/12 1/12 4/12 0.8 0.20

Trained ------------------Pas. on 0 4-Fix 0/6 6/6 3/6 3/6 0/6 0/6 1.0 0.20

\_----------_/FREQUENCY OF "TARGET" RESPONSE

PERCEPTRO. APPLICATIONS IN PHOTO INTERPRETATIO~

TABLE V

FROM EXPT. 40.5

631

Stimuli False-

x61

DetectionAlarmX X 0 0 RateXO XO Line Dot 00 06 Line Dots Rate

--------------------Preceptron {I-FiX 12/12 6/6 6/6 9/12 6/6 0/6 1/6 0/6 0/6 0.93 .04

Trained --Pos. on X 4-Fix 6/6 6/6 6/6 5/6 6/6 0/6 0/6 1/6 0/6 0.97 .0-1-

----------------

Preceptron fJ·Fix 11/12 4/6 4/6 2/12 3/6 6/6 6/6 9/12 6/6 0.91 .-1-3Trained 1'---

!'os. on 0 l4.Fix 5/6 5/6 3/6 0/6 4/6 6/6 6/6 4/6 6/6 090 .50

pared directly with the result of the previousexperiment "Experiment 40.5 (No negativetraining)." (Table III).

To observe the effect of partial negativetraining, the two tables should be comparedrow by row. One would expect to find somereduction in the false-alarms from just thosestimulus forms for which negative trainingwas given. But there appears to be somecarry-over, or generalization, of the effect, notonly to untrained positions of a partly nega­tive-trained non-target shape, but also toother untrained non-target shapes. In fact,there appears to be even a slight (undesirable)reduction in target recognition. But a largerexperiment is needed to determine whether itis statistically significant. Furthermore,several cycles of target, non-target trainingcan reduce this effect if it is significant.

DOUBLE STIMULI A"D BACKGROU~D

The experimental results reported aboveconcern the effects of single and multiple

fixation, with or without negative tralnlllg,on the detection of single "target" shapes illany upright position, and the rejection ofsingle, isolated, "non-targets." The sameexperiments which produced those results,also yielded results on various combinationstimuli, obtained by projecting any t\\·o ofX, 0, 0, 6, Lines, and Dots simultaneously.Each member of any stimulus pair was placedon the machine's field of view in any uprightposition, randomly selected, and independentof the other member of the pair. The descrip­tion of traini ng already given still applies:the MARK I was split into two independenthalf-size perceptrons, one of \"hich wastrained positi\-e on X alone, one on 0 alone.As before, the experiments were divided intotwo sets which were identical except that, inone, some negative training was given on asmall sample of single non-targets. Thestimuli were then presented in pairs and theresults are given in Tables V and VI.

These results, like those in Tables III andIV show an advantage for 4-fix O\'er i-fix

TABLE VI

FROM EXPT. 40.6

Stimuli False-Detection AlarmX X 0 0 RateXO XO X6 Line Dot 00 06 Line Dots Rate

--------------------Perceptron

{I-Fix 6/12 10/12 10/12 5/12 8/12 4/12 3/12 1/12 2/12 .65 .21

Trained -----------------------Pas. on X 4-Fix 4/6 5/6 6/6 1/6 2/6 0/6 0/6 0/6 0/6 .60 0

----------------Perceptron

{I-Fix 5/12 1/12 2/12 0/12 2/12 9/12 4/12 7/12 5/12 .50 .10

Trained --~---------------------Pas. on 0 4-Fix 3/6 1/6 0/6 0/6 0/6 6/6 6/6 3/6 5/6 .77 .04

\_------------_/FREQUENCY OF "TARGET" RESPOKSE

632 PHOTOGRAMMETRIC ENGINEERING

xO

X -LINES

xO

ANOTHER EXAMPLEOF X-LINES. X

III 01 FFERENTPOSITION; LINES SAMEAS IN PHOTO AT LEFT

x6

X -DOTS

FIG. 2. Photographs show how several of the Combination Stimuli "looked" to the Perceptron, be­cause of Quantization of the Projected Image by the Low-Resolution Retina. Combination Patterns wereproduced by projecting any two of the slides shown in Figure I, simultaneously, via two projectors and aBeam Splitter. Several presentations of each combination were made in which the images were moved,independently, to new positions.

responses. The lowered detection rate fortrue targets is also plainly evident. Experi­ence which was gained later indicates thatthis undesirable drop might have been sub­stantially eliminated by continued, alternat­ing, positive and negative training. Inherentlydifficult tasks, such as mixed stimuli, nor­mally require several cycles of training to reacha practical plateau in performance, whereas theearly experiments reported in this paper pro­vided only one.

It is now important to find out, in the nearfuture, by repeated cycles of positive trainingon targets and negative training on a sampleof non-targets, whether one can decrease thefalse alarm frequency for all non-targetswithout a comparable reduction in targetdetection rate.

"REALISTIC" STIMULI

The pair presentation experiments rep­resent the first baby step toward the recogni­tion of well-defined target shapes in the pres­ence of other shapes and backgrounds. Thenext series of experiments, in which clutterplayed a prominent part, represent a some­what closer approach to realistic conditions,within the limited capabilities of the sensoryapparatus availabl~ for the MARK I Percep­tron.

Because of resolving power and gray-scale

limitations of its input device, only a verysmall fraction of the total information presen tin any area of an ordinary photograph couldactually enter the MARK 1. This degradationat the input tends to reduce a good photo­graph to an unintelligible hodgepodge ofblack spots. As a consequence. ordinary aerialphotographs could not be used directly. Onthe other hand. since a lack of ground. orgray-scale. resolution is not an inherent char­acteristic of perceptrons, it was permissible,for these preliminary tests. to compensate forthe input inadequacies by artificially restoringthe pattern continuity which would havebeen destroyed by direct photographic inputto the MARK 1.

Figure 3 shows typical realistic "target"shapes in real photographs. It can be seenthat extremely low-resolution and. especially,quantizing to black-and-white, would leavelittle. if anything. to be recognized.

Figures 4 and 5 show stimulus patternsderived from tracings of aerial photographs.After the outlines were filled in to producesolid silhouettes, they were copied on to35 m m. slides. In the following experiments.background, consisting of some arbitrarilychosen subsection of the" Lines" or "Dots"slides. was independently added. through theuse of a second slide projector and a beamsplitting mirror. When subjected to the

PERCEPTRON APPLICATIONS IN PHOTO INTERPRETATION

A. AERIAL PHOTO OF SHIP 1M SPECULAR SEA CLUTTER

633

B. AERIAL PHOTO OF PERSOHMEL TREMCHES AMD OTHER AIRFIELD STRUCTURES

FIG. 3.

634 PHOTOGRAMMETRIC ENGINEERING

--

(Al SHIP (81 DOTS (Cl LINES

FIG. -lA. Picturcs showing original slides uscd ill Ship-ln-CllItter experiment.

SHIP SHIP IN CLUTTER(DOTS)

SHIP IN CLUTTER(LINES)

FIG. 411. Pictures showillg how-CArin Figurc 4A and culubillaliolls of (A) with (B) and with (C)appear after quantization by the Mark I Perceptron Retina. These are the visual data which actuallycllters the machine and upon which its_decisions are based.

Alc IN REVETMENT

uEMPTY REVETMENT SHED-TRENCH COMPLEX

FIG. SA. Pictures showing original slides used in the H.evetment Experiment.

Alc IN REVETMENT EMPTY REVETMENT SHED-TRENCH COMPLEX

FIG. SB. Pictures showing how the Mark 1 Retina Quantizes thc [mages in Figure SA. Quantizedimages, indicating what information actually enters the Perceptron, are obtained from the Sensory Sys­tem Monitor. A Bank of 20"X20" \Teon Indicator Lamps, wired in parallel with the Sensory Units.

degradation imposed by the MARK I inputapparatus, the lines or dots images lost muchof their specialized character and became asuitable simulation of a splotchy, partlyorganized, rather dense, background pattern.

An experiment to illustrate directly thedetection of targets in clutter, consisted oftraining the MARK 1* to a ship pattern andthen testing its ability to detect this pattern

* ..\ctually, only a half-size MARK I.

on a high noise background, such as would befurnished by specular sea clutter. As before,the stimulus was allowed only one rotationalorientation, but no restrictions were appliedto its location in the field of view. Responseswere recorded for 12 different arbitrarysampling positions. The scores are shown inTable VII.

In another experiment, the MARK I wastrained to detect revetments containing air­craft. Its ability to respond correctly to these

PERCEPTRON APPLICATIONS IN PHOTO INTERPRETATIO~

TABLE VII

FREQUENCY OF "TARGET" RESPONSE

635

ShipShip in Clutter Clutter A lone Detection False-

Rate in AlarmAlone Lines Dots Lines Dots Clutter Rate

12/12 JO/J2 10/12 3/12 0/12 .83 . 12

\---------------/

TABLE VI!!

FREQUEKCY OF "TARGET" RESPONSE

AIC in A/C Revetment Shed-l'rench Detection False-AlarmRevetment Alone Atone Complex Rat(~ Rate

- --,11/12 2/J2 0/12 0/12 .92 .06

\----------,----- /

and to reject empty revetments, aircraft notin revetments, and other airfield structureswas tested as follows: Positive training wasgiven for the "aircraft in revetment" stimulusof Figure 5. Some negative training was givenfor a few samples of "empty revetment." Notraining of either kind was given on the "shed­and-personnel-trench complex" nor on the"aircraft without revetment." In te~ts follo\\'­ing training, each of these stimuli was allowedto fall upright on any location in the field ofview and the recorded responses are shown inTable VIII.

LATTICE RECOGNITION

Many patterns of practical interest are notcharacterized so much by a distinguishingoutline shape as by some other identifyingproperties. One of these is sometimes periodic­ity. While periodic structures can occurnaturally, quite commonly they are man­made objects such as tank farms, orchards, orurban grids. To a human observer, it may bethe regular periodicity as much as any otherproperty, which arrests the attention whenthese are encountered in an aerial photograph.

An exploratory experiment was completedon the MARK I to test whether some repre­sentation of this property could be trainedinto and used by a simple random perceptron.If it could be, the simple perceptron, which isnormally very sensitive to size and outlinecharacteristics, could be expected to recog­nize small portions of the original traininglattice, regardless of its outline or size, orany reasonable portion of some other lattice

whose spacing constant was somewhat closeto that of the training lattice.

A regularly spaced dot pattern typical of atank farm, was projected to fill the en tirefield of view of the MARK 1. At this magnifi­cation, each dot was larger than but wascentered on one sensory unit of the 20X20unit retina. Several training presentations ofthis image \\·ere made, shifting it sidewayslittle-by-little between presentations until ithad moved a total of one lattice unit where,again, each dot fell exactly upon one sensoryunit. Similar training was given in the verticaldirection. Also, and this is important, rota­tional variation of up to 90° was included inthis experi mentt by means of a dove prism.

After training, the perceptron \\'as tested onvarious fragmen tar)' portions of the originallattice, in several magnifications, and inseveral lateral, vertical, and rotational posi­tions, wi th and wi thou t cl utter backgrou nds.Table IX shows the results obtained from 20tests of each stimulus. These 20 were arandom sampling of the permissible locationsand rotations.

As one would expect, the fidelity of re­sponse falls off as the image magnification ismade more and more different from that usedin training. If the changes in magnificationhad been carried further to, say, 0.5 and 2.0,one would expect some rise in this perform­ance because the sub-harmonic and harmonicpatterns thus produced would so closelyresemble the original pattern. The effect

t Because of the high symmetry properties ofthe pattern.

636 PHOTOGRAMMETRIC ENGINEERING

TABLE IX

FREQUENCY OF "TARGET" RESPONSE

IMAGE MAG IFICATION

I.OX1.2X 0.8X(Training mag.) 0.6X

_.

Original Lattice(lOX10 Tank Farm) 1.00 .95 .90 .40

5 X8 Tank Section .95 1.00 .45 o Data

JX6 Tank Section .80 .70 .20

JX6 Tank Section .60 .55 .00 No Data

Original Lattice on LowClutter Background (Dots) 1.00 1.00 No Data No Data

Original Lattice on HighClutter Background (Lines) .20 .20 No Data o Data

\---------------_/

would, of course, be blurred because of thetranslations and rotations which were per­mitted. But, if one point of the lattice wereanchored on one particular sensory field point,and no changes in rotation were permitted asthe magnification were decreased, the imageat O.SX would be indistinguishable (to theperceptron) from a 5 XS section of the latticeat a magnification of LOX. There were nodata for a 5 X 5 section at LOX but 80% ofthe responses to a 4 X 6 section at this magnification were correct.

CONCLUSIONS AND FORECASTS

There has now been completed the description of those preliminary experimentsmost directly related to potential applicationsin photo interpretation. They have demonstrated photo recognition potential in arelatively simple device, and have directedattention to problems which the next phaseof effort should attack.

One of the most evident needs, and perhapsthe easiest to provide, is a moderate degree 0

gray-scale resolution in the input apparatus.When this is furnished, actual photographicimages may be presented to the recognitiondevice without serious loss of their charac­teristic continuity. On the other hand, preser­vation of the continuity in the image bringsone face-to-face with the very difficult butnecessary task of deciding exactly how con­tinuity can be sensed and utilized in the

1. recognition of objects not characterized

by fixed outline shapes, and,2. segregation of target images from each

other and from their backgrounds.

These two uses are major problem classeswhich appear to embrace most of the out­standing challenges for the mechanization ofphoto interpretation. For the recognition ofpatterns on some basis other than rigid shape,one obviously must rely on attributes whichare not particularly related to shape (such ascolor, texture, periodicity, straightness, andcertain topological relations). For segregationof patterns, one is led to consider means fordetecting compactness and coherence offorms and to consider a hierarchical logicalstructure within the machine to deal with thehierarchical organization of ground patterns,in which properties and forms are often, if notusually, the elemental parts of larger patterns.

I t is fortunate that these considerations arecompatible with what we believe can beengineered into advanced machines whoseancestry can be traced to a simple perceptronof the MARK I type. Of the many specificproblems which must be surmounted to reachthese goals, three are here suggested forimmediate attention.

1. A search for pattern properties whichare moderately primitive but signifi­cantly more sophisticated or abstractthan those on which a simple percep­tron performs its classification, and thedesign of mechanisms for incorpora-

PERCEPTRON APPLICATIONS IN PHOTO INTERPRETATION 637

tion in a recognition machine to senseand utilize these properties in theidentification of ground objects.

II. A two-pronged attack on the sensingand utilization of continuity, begin­ning at the most elementary level, andaimed primarily at the recognitiontof boundaries or edges.(a) The most general case appears to

be the easiest: the sensing ofthreadlike continua without re­gard for the regularity of theirdirection. It seems likely that theability to sense threads is basic tothe desired ability to recognizeclosed boundaries which at leastdefine the existence and locationof an object even if they do notidentify it. Furthermore, the classof figures which may be calledribbons, including roads, rivers,and some beaches, consist pri­marily of two threads whose sepa­ration tends to remain constant.Therefore, one of the early goals tobe sought is the development ofmechanisms for recognizing rivers,roads, and other ribbon-like formshaving generally unpredictablelength and regularity of direction.

(b) The more difficult case appears tobe the recognition of regularity inthread-like continua, particularlystraightness. Any practical ma­chine is likely to have a sensory mo­saic similar to a retina but substan­tially coarser than that of a humaneye. A perfectly straight boundaryprojected onto a man's retina in afixed arbitrary position wouldexcite a pattern of receptor cellswhose boundary is irregular. Yet,somehow, the processing appara­tus which we carry in our headsobtains a sense of straightnessfrom such an irregular representa­tion of a "truly" straight line and,to a remarkable degree, detectsirregularities which are muchsmaller than the size of its ownretinal mosaic elements. This

t As opposed to merely emphasizing or isolat­ing them in a two-dimensional representation.

ability is usually stated 10 termsof vernier acuity.

A significant part of the prob­lem of straight-line detection inmachines is apparently in theproblem of providing vernier acu­ity of an order greater than theretinal resolution, and an explana­tion of the mechanism in peoplemay very well lead to a solution ofthe problem for machines, or viceversa. Because it appears to be soclosely related to the topic ofcontinuity, mechanisms for im­provement of apparent vernieracuity are included as an immedi­ate research goal for the continuitystudy.

Ill. The third problem area deservingimmediate attention is simultaneousclassification at several levels. It isrelated to topic II in the sense thatboth bear upon the so-called figure­ground problem of perceiving, as sepa­rate entities, the elementary objects ofan ensemble of objects. Multiple-levelclassification processes may be thoughtof as those which may give severalresponses to a complex pattern; theresponses must form an orderable setcorresponding to the orderable levelsof organization within a complexwhich consists of patterns, subpat­terns, sub-sub-patterns, etc. It is notdifficult to provide an elementaryability of this sort in primitive per­ceptrons and some of their foreseeabledescendants, and it is intended togive special attention to this possibil­ity in the immediate future.

REFERENCES

1. Rosenblatt, F., "A Theory of Statistical Sepa­rability in Cognitive Systems," C.A.L. ReportNo. VG-1196-G-1, January 1958, 263 pp.ASTlA No. AD204076.

2. Murray, A. E., "A Review of the PerceptronProgram," Proceedings of the National Elec­tronic Conference, Vol. XV (1959).

3. Murray, A. E., "Perceptron Applicability toPhotointerpretation," Phase I Report (ProjectPICS). CAL. Report No. VE-1446 G-1,November, 1960, 58 pp.

4. Platt, John R., "How We See Straight Lines,"Scientific American, 202, No.6, June 1960.