this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the- ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 could potentially model the brain as a generafive model in such a way that feedback serves to disambiguate and 'explain away' the earlier representa- tion. The Helmholtz machine 4, 5 was an excellent step towards approximating this proposal, with feedback implementing priors. Its development, however, was rather limited, dealing only with binary images. Moreover, its feedback mechanisms were engaged only during the learning of the feedforward connections but not during perceptual inference, though the Gibbs sampling process for inference can potentially be interpreted as top-down feedback disambiguating low level representations? Rao and Ballard's predictive coding/Kalman filter model 6 did integrate generafive feedback in the perceptual inference process, but it was primarily a linear model and thus severely limited in practical utility. The data-driven Markov Chain Monte Carlo approach of Zhu and colleagues 7, 8 might be the most successful recent application of this proposal in solving real and difficult computer vision problems using generafive models, though its connection to the visual cortex has not been explored. Here, we bring in a powerful and widely applicable paradigm from artificial intelligence and computer vision to propose some new ideas about the algorithms of visual cortical process- ing and the nature of representations in the visual cortex. We will review some of our and others' neurophysiological experimental data to lend support to these ideas

In this work we show how to combine bottom-up and topdown approaches into a single figure-ground segmentation process. This process provides accurate delineation of object boundaries that cannot be achieved by either the topdown or bottom-up approach alone. The top-down approach uses object representation learned from examples to detect an object in a given input image and provide an approximation to its figure-ground segmentation. The bottomup approach uses image-based criteria to define coherent groups of pixels that are likely to belong together to either the figure or the background part. The combination provides a final segmentation that draws on the relative merits of both approaches: The result is as close as possible to the top-down approximation, but is also constrained by the bottom-up process to be consistent with significant image discontinuities. We construct a global cost function that represents these top-down and bottom-up requirements. We then show how the global minimum of this function can be efficiently found by applying the sum-product algorithm. This algorithm also provides a confidence map that can be used to identify image regions where additional top-down or bottom-up information may further improve the segmentation. Our experiments show that the results derived from the algorithm are superior to results given by a pure top-down or pure bottom-up approach. The scheme has broad applicability, enabling the combined use of a range of existing bottom-up and top-down segmentations. 1.

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons' responses reflecting higher order perceptual computations related to Ullman's (Cognition 1984;18:97 -- 159) visual routines and Marr's (Vision NJ: Freeman 1982) full primal sketch, 2 1 2 D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons' precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later...

The activities of neurons in primary visual cortex have been shown to be significantly influenced by stimuli outside their classical receptive fields. We propose that these contextual influences serve pre-attentive visual segmentation by causing relatively higher neural responses to important or conspicuous image locations, making them more salient for perceptual pop-out. These locations include boundaries between regions, smooth contours, and pop-out targets against backgrounds. The mark of these locations is the breakdown of spatial homogeneity in the input, for instance, at the border between two texture regions of equal mean luminance. This breakdown causes changes in contextual influences, often resulting in higher responses at the border than at surrounding locations. This proposal is implemented in a biologically based model of V1 in which contextual influences are mediated by intra-cortical horizontal connections. The behavior of the model is demonstrated using examples of text...

Abstract not found

A fundamental question in visual neuroscience is how to represent image structure. The most common representational schemes rely on differential operators that compare adjacent image regions. While well-suited to encoding local relationships, such operators have significant drawbacks. Specifically, each filter’s span is confounded with the size of its sub-fields, making it difficult to compare small regions across large distances. We find that such long-distance comparisons are more tolerant to common image transformations than purely local ones, suggesting they may provide a useful vocabulary for image encoding.. We introduce the “Dissociated Dipole, ” or “Sticks ” operator, for encoding non-local image relationships. This operator de-couples filter span from sub-field size, enabling parametric movement between edge and region-based representation modes. We report on the perceptual plausibility of the operator, and the computational advantages of non-local encoding. Our results suggest that non-local encoding may be an effective scheme for representing image structure.

This paper presents a model on the potential functional roles of the early visual cortex in the primate visual system. Our hypothesis is that early visual areas, such as V1, are important for continual interaction among various higher order visual areas during visual processing. The interaction is mediated by recurrent connections between higher order visual areas and V1, manifested in the longlatency context-sensitive activities often observed in neurophysiological experiments, and is responsible for the re-integration of information analysed by the higher visual areas. Specifically, we considered the case of integrating `what' and `where' information from the ventral and dorsal streams. We found that such a cortical architecture provides simple solutions and fresh insights into the problems of attentional routing and visual search. The computational viability of this architecture was tested by simulating a largescale neural dynamical network.

Figure–ground assignment is an important visual process; humans recognize, attend to, and act on figures, not backgrounds. There are many visual cues for figure–ground assignment. A new cue to figure–ground assignment, called lower region, is presented: Regions in the lower portion of a stimulus array appear more figurelike than regions in the upper portion of the display. This phenomenon was explored, and it was demonstrated that the lower-region preference is not influenced by contrast, eye movements, or voluntary spatial attention. It was found that the lower region is defined relative to the stimulus display, linking the lower-region preference to pictorial depth perception cues. The results are discussed in terms of the environmental regularities that this new figure–ground cue may reflect. Figure–ground assignment is a well-known psychological phenomenon; illustrations of figure–ground assignment appear in most introductory psychology textbooks, and most psychology students recognize these examples. Figure–ground assignment is the process by which the visual system organizes a visual scene into figures (occluding, foreground regions) and grounds (occluded regions) following the initial formation of those regions (Palmer & Rock, 1994). Determining which regions are figures and which are grounds is an important visual process because everyday visual scenes contain multiple objects that often overlap and partially occlude one another. Figure–ground processes have been studied most extensively by perceptual and cognitive scientists

TABLE OF CONTENTS 2.1. Visual areas are defined by receptive field tuning properties 2.2. Combining the distributed information

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