Recursive low level vision system

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)637-648
Journal / PublicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
Publication statusPublished - 1997
Externally publishedYes

Conference

TitleProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5)
CityOrlando, FL, USA
Period12 - 15 October 1997

Abstract

The paper addresses a very important, yet one of the difficult issues in computer vision and visualization - low level vision modeling. It proposes a novel low level vision model which recursively integrates adaptive filtering, segmentation and edge detection. The model has strong biological merits: a) the model architecture is based on a biologically inspired neural network - network of networks which simulates human visual cortex; b) evolutionary computation is applied to identify the hierarchy and clusters in the network. But the model does not constrain itself by the biological facts. Instead, it proposes that by using clustering method, adaptive filtering, segmentation and edge detection are naturally linked to one another. This is a completely computationally oriented concept and is the center of the. The feasibility of the concept will be demonstrated via a visual example.