It is shown that both changes in viewing position and illumination conditions canbe compensated for, prior to recognition, using combinations of images taken from different viewing positions and different illumination conditions.It is also shown that, in agreement with psychophysical findings, the computation requires at least a sign-bit image as input - contours alone are not sufficient. 1 Introduction The task of visual recognition is natural and effortless for biological systems, yet the problem of recognition has been proven to be very difficult to analyze from a computational point of view. The fundamental reason is that novel images of familiar objects are often not sufficiently similar to previously seen images of that object. Assuming a rigid and isolated object in the scene, there are two major sources for this variability: geometric and photometric. The geometric source of variability comes from changes of view position. A 3D object can be viewed from a variety of directions, each resulting with a different 2D projection. The difference is significant, even for modest changes in viewing positions, and can be demonstrated by superimposing those projections (see Figure 1, first row second image). Much attention has been given to this problem in the visual recognition literature ([9], and references therein), and recent results show that one can compensate for changes in viewing position by generating novel views from a small number of model views of the object [10, 4, 8].

Neurons encoding simple visual features in area VI such as orientation, direction of motion and color are organized in retinotopic maps. However, recentphysiological experiments have shown that the responses of many neurons in VI and other cortical areas are modulated by the direction ofgaze. We have developed a neural network model of the visual cortex to explore the hypothesis that visual features are encoded in headcentered coordinatesat early stages of visual processing. New experiments are suggested for testing this hypothesis using electrical stimulations and psychophysical observations.

An effective to develop an abstract UI model which solution to this problem must have three encompasses the essence of those systems.

Dynamic Constraints," present a images or pictures because "having Gregory shows how it can be made

Dynamic Constraints," present a images or pictures because "having Gregory shows how it can be made

In some Winter, 1991) brought a broad nostalgic The principles of statistical pattern mature and highly technical disciplines, smile to my face. I believe that recognition I had employed then are this mode of thought can be I am the unnamed Yale junior faculty very general indeed. Unfortunately, this is not member to whose work Prof. Schank very principles form the basis of all the case in our chosen pursuit of the alluded. Perhaps the intervening contemporary speech recognition essence of mind which should be years have eradicated his memory of systems which, in their best incarnations seen as a young and interdisciplinary my name or, more likely, he wished here at Bell Laboratories and enterprise. I, transcribing fluent speech of virtually organisms, she was not constrained however, fully mindful of Oscar any speaker talking about a specific by the academic boundaries that Wilde's observation that the only topic and using a vocabulary of thousands have since evolved.

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I have presented a qualitative approach to the problem of recovering object structure from motion information and discussed some of its computational, psychophysical and implementational aspects. The computation of qualitative shape, as represented by the sign of the Gaussian curvature, can be performed by a field of simple operators, in parallel over the entire image. The performance of a qualitative shape detection module, implemented by an artificial neural network, appears to be similar to the performance of human subjects in an identical task.

Inspired by a visual motion detection model for the ra.bbit retina and by a computational architecture used for early audition in the barn owl, we have designed a chip that employs a correlation model to report the one-dimensional field motion of a scene in real time. Using subthreshold analog VLSI techniques, we have fabricated and successfully tested a 8000 transistor chip using a standard MOSIS process.