Multiresolution neural network for the extraction of the primal sketch

Richard Lepage; Daniel Crevier

doi:10.1117/12.188909

Abstract

A goal of computer vision is the construction of scene descriptions based on information extracted from one or more 2D images. A reconstruction strategy based on a four-level representational framework is presented. We are interested in the second representational level, the Primal Sketch. It makes explicit important information about the two-dimensional image, primarily the intensity changes and their geometrical distribution and organization. The intensity changes corresponding to physical features of the observed scene appear at several spatial scales, in contrast to spurious edges, and image analysis performed at multiple resolutions is therefore more robust. We propose a compact pyramidal neural network implementation of the multiresolution representation of the input images. Features of the scene are detected at each resolution level and feedback interaction is built between pyramid levels in order to reinforce edges which correspond to physical features of the observed scene. A vigilance neuron determines the importance granted to each spatial resolution in the feature extraction process.

Citation Download Citation

Richard Lepage, Richard Lepage, Daniel Crevier, Daniel Crevier, } "Multiresolution neural network for the extraction of the primal sketch", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188909; https://doi.org/10.1117/12.188909

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