Biologically Inspired Connectionist Model for Image Feature Extraction in 2D Pattern Recognition
Abstract
A new edge detection method is presented which borrows from recent research into primate vision biology, and offers improved noise performance over classical methods. Beginning with spatio-temporal shunting models for retinal cones, horizontal cells, bipolar cells, and retinal ganglions, a set of simplified steady-state solutions are developed which lend themselves to efficient computation on standard computer equipment. The retinal model output is found to be nominally equivalent to the classical edge detector, but is produced differently. Developed somewhat speculatively from incomplete biological information, a simplified model of the Lateral Geniculate Nucleus (LGN) has been produced. Taking the output of the retinal model, the LGN simple cell and interneuron models perform noise reduction and segment completion. An orienting subsystem is used to adaptively infer segment strengths and orientations, throwing out spurious and foreshortened edges, while retaining and filling in the longer edges.
Recommended Citation
R. K. Chafin and C. H. Dagli, "Biologically Inspired Connectionist Model for Image Feature Extraction in 2D Pattern Recognition," Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 2704 - 2709, Institute of Electrical and Electronics Engineers, Jan 1999.
Department(s)
Engineering Management and Systems Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 1999
