Recognition of 3-D Objects from Multiple 2-D Views by a Self-Organizing Neural Architecture

The recognition of 3-D objects from sequences of their 2-D views is modeled by a neural architecture, called VIEWNET that uses View Information Encoded With NETworks. VIEWNET illustrates how several types of noise and varialbility in image data can be progressively removed while incornplcte image features are restored and invariant features are discovered using an appropriately designed cascade of processing stages. VIEWNET first processes 2-D views of 3-D objects using the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and removes noise from the images. Boundary regularization and cornpletion are achieved by the same mechanisms that suppress image noise. A log-polar transform is taken with respect to the centroid of the resulting figure and then re-centered to achieve 2-D scale and rotation invariance. The invariant images are coarse coded to further reduce noise, reduce foreshortening effects, and increase generalization. These compressed codes are input into a supervised learning system based on the fuzzy ARTMAP algorithm. Recognition categories of 2-D views are learned before evidence from sequences of 2-D view categories is accumulated to improve object recognition. Recognition is studied with noisy and clean images using slow and fast learning. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of 2-D views of jet aircraft with and without additive noise. A recognition rate of 90% is achieved with one 2-D view category and of 98.5% correct with three 2-D view categories.National Science Foundation (IRI 90-24877); Office of Naval Research (N00014-91-J-1309, N00014-91-J-4100, N00014-92-J-0499); Air Force Office of Scientific Research (F9620-92-J-0499, 90-0083

Incremental grouping of image elements in vision

One important task for the visual system is to group image elements that belong to an object and to segregate them from other objects and the background. We here present an incremental grouping theory (IGT) that addresses the role of object-based attention in perceptual grouping at a psychological level and, at the same time, outlines the mechanisms for grouping at the neurophysiological level. The IGT proposes that there are two processes for perceptual grouping. The first process is base grouping and relies on neurons that are tuned to feature conjunctions. Base grouping is fast and occurs in parallel across the visual scene, but not all possible feature conjunctions can be coded as base groupings. If there are no neurons tuned to the relevant feature conjunctions, a second process called incremental grouping comes into play. Incremental grouping is a time-consuming and capacity-limited process that requires the gradual spread of enhanced neuronal activity across the representation of an object in the visual cortex. The spread of enhanced neuronal activity corresponds to the labeling of image elements with object-based attention

Stability and Likelihood of Views of Three Dimensional Objects

Can we say anything general about the distribution of two dimensional views of general three dimensional objects? In this paper we present a first formal analysis of the stability and likelihood of two dimen- sional views (under weak perspective projection) of three dimensional objects. This analysis is useful for various aspects of object recognition and database indexing. Examples are Bayesian recognition; indexing to a three dimensional database by invariants of two dimensional images; the selection of "good" templates that may reduce the complexity of correspondence between images and three dimensional objects; and ambiguity resolution using generic views