A self-organizing neural network is developed for recognition of 3-D objects from sequences of their 2-D views. Called VIEWNET because it uses view information encoded with networks, the model 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. 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 which learns 2-D view categories. Evidence from sequences of 2-D view categories is stored in a working memory. Voting based on the unordered set of stored categories determines 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 aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view category and of up to 98.5% correct with three 2-D view categories.
A theory of 3-D visual perception and figure/ground separation by visual cortex is described. Called FACADE Theory it suggests a solution of the 3-D figure/ground problem for biological vision and makes many predictions whereby it can be tested. The theory further develops my 3-D vision theory of 1987 which used multiple receptive field sizes or scales to define multiple copies of two interacting systems: a Boundary Contour System (BCS) for generating emergent boundary segmentations of edges textures and shading and a Feature Contour System (FCS) for discounting the illuminant and filling in surface representations of Form-And-Color-And-DEpth or FACADEs. The 1987 theory did not posit interactions between the several scales of the BCS and FCS. The present theory suggests how competitive and cooperative interactions that were previously defined within each scale also act between scales. 2 / SPIE Vol. 1382 Intelligent Robots and Computer Vision IX: Neural Biological and 3-D Methods (1990)
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