Optimizing Associative Information Transfer within Content-addressable Memory

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Contour-map encoding of shape for early vision

Contour maps provide a general method for recognizing 2-D shapes. All but blank images give rise to such maps, and people are good at recognizing objects and shapes from them. The maps are encoded easily in long feature vectors that are suitable for recognition by an associative memory. These properties of contour maps suggest a role for them in early visual perception. The prevalence of direction sensitive neurons in the visual cortex of mammals supports this view

The development of contour processing : evidence from physiology and psychophysics

Object perception and pattern vision depend fundamentally upon the extraction of contours from the visual environment. In adulthood, contour or edge-level processing is supported by the Gestalt heuristics of proximity, collinearity, and closure. Less is known, however, about the developmental trajectory of contour detection and contour integration. Within the physiology of the visual system, long-range horizontal connections in V1 and V2 are the likely candidates for implementing these heuristics. While post-mortem anatomical studies of human infants suggest that horizontal interconnections reach maturity by the second year of life, psychophysical research with infants and children suggests a considerably more protracted development. In the present review, data from infancy to adulthood will be discussed in order to track the development of contour detection and integration. The goal of this review is thus to integrate the development of contour detection and integration with research regarding the development of underlying neural circuitry.We conclude that the ontogeny of this system is best characterized as a developmentally extended period of associative acquisition whereby horizontal connectivity becomes functional over longer and longer distances, thus becoming able to effectively integrate over greater spans of visual space. Keywords

The development of contour processing: evidence from physiology and psychophysics

Object perception and pattern vision depend fundamentally upon the extraction of contours from the visual environment. In adulthood, contour or edge-level processing is supported by the Gestalt heuristics of proximity, collinearity, and closure. Less is known, however, about the developmental trajectory of contour detection and contour integration. Within the physiology of the visual system, long-range horizontal connections in V1 and V2 are the likely candidates for implementing these heuristics. While post-mortem anatomical studies of human infants suggest that horizontal interconnections reach maturity by the second year of life, psychophysical research with infants and children suggests a considerably more protracted development. In the present review, data from infancy to adulthood will be discussed in order to track the development of contour detection and integration. The goal of this review is thus to integrate the development of contour detection and integration with research regarding the development of underlying neural circuitry. We conclude that the ontogeny of this system is best characterized as a developmentally extended period of associative acquisition whereby horizontal connectivity becomes functional over longer and longer distances, thus becoming able to effectively integrate over greater spans of visual space

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

A Max-Plus Model of Asynchronous Cellular Automata

This paper presents a new framework for asynchrony. This has its origins in our attempts to better harness the internal decision making process of cellular automata (CA). Thus, we show that a max-plus algebraic model of asynchrony arises naturally from the CA requirement that a cell receives the state of each neighbour before updating. The significant result is the existence of a bijective mapping between the asynchronous system and the synchronous system classically used to update cellular automata. Consequently, although the CA outputs look qualitatively different, when surveyed on "contours" of real time, the asynchronous CA replicates the synchronous CA. Moreover, this type of asynchrony is simple - it is characterised by the underlying network structure of the cells, and long-term behaviour is deterministic and periodic due to the linearity of max-plus algebra. The findings lead us to proffer max-plus algebra as: (i) a more accurate and efficient underlying timing mechanism for models of patterns seen in nature, and (ii) a foundation for promising extensions and applications.Comment: in Complex Systems (Complex Systems Publications Inc), Volume 23, Issue 4, 201

Traditional and new principles of perceptual grouping

Perceptual grouping refers to the process of determining which regions and parts of the visual scene belong together as parts of higher order perceptual units such as objects or patterns. In the early 20th century, Gestalt psychologists identified a set of classic grouping principles which specified how some image features lead to grouping between elements given that all other factors were held constant. Modern vision scientists have expanded this list to cover a wide range of image features but have also expanded the importance of learning and other non-image factors. Unlike early Gestalt accounts which were based largely on visual demonstrations, modern theories are often explicitly quantitative and involve detailed models of how various image features modulate grouping. Work has also been done to understand the rules by which different grouping principles integrate to form a final percept. This chapter gives an overview of the classic principles, modern developments in understanding them, and new principles and the evidence for them. There is also discussion of some of the larger theoretical issues about grouping such as at what stage of visual processing it occurs and what types of neural mechanisms may implement grouping principles

A strongly goal-directed close-range vision system for spacecraft docking

In this presentation, we will propose a strongly goal-oriented stereo vision system to establish proper docking approach motions for automated rendezvous and capture (AR&C). From an input sequence of stereo video image pairs, the system produces a current best estimate of: contact position; contact vector; contact velocity; and contact orientation. The processing demands imposed by this particular problem and its environment dictate a special case solution; such a system should necessarily be, in some sense, minimalist. By this we mean the system should construct a scene description just sufficiently rich to solve the problem at hand and should do no more processing than is absolutely necessary. In addition, the imaging resolution should be just sufficient. Extracting additional information and constructing higher level scene representations wastes energy and computational resources and injects an unnecessary degree of complexity, increasing the likelihood of malfunction. We therefore take a departure from most prior stereopsis work, including our own, and propose a system based on associative memory. The purpose of the memory is to immediately associate a set of motor commands with a set of input visual patterns in the two cameras. That is, rather than explicitly computing point correspondences and object positions in world coordinates and trying to reason forward from this information to a plan of action, we are trying to capture the essence of reflex behavior through the action of associative memory. The explicit construction of point correspondences and 3D scene descriptions, followed by online velocity and point of impact calculations, is prohibitively expensive from a computational point of view for the problem at hand. Learned patterns on the four image planes, left and right at two discrete but closely spaced instants in time, will be bused directly to infer the spacecraft reaction. This will be a continuing online process as the docking collar approaches