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Cognitive Computation
Published in: Cognitive Computation 6/2016

01-12-2016

Contour Detection in Colour Images Using a Neurophysiologically Inspired Model

Authors: Qi Wang, M. W. Spratling

Published in: Cognitive Computation | Issue 6/2016

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Abstract

Background

The predictive coding/biased competition (PC/BC) model of V1 has previously been applied to locate boundaries defined by local discontinuities in intensity within an image.

Objective

Here PC/BC is extended to perform contour detection for colour images. Methods The proposed extensions are inspired by neurophysiological data from single neurons in macaque primary visual cortex (V1).

Results

The behaviour of this extended model is consistent with the neurophysiological experimental results. Furthermore, when compared to methods used for contour detection in computer vision, the colour PC/BC model of V1 slightly outperforms some recently proposed algorithms which use more cues and/or require a complicated training procedure.

Conclusions

The colour PC/BC model of V1 can successfully simulate the responses properties of orientation-selective double-opponent neuron in macaque V1 and has practical applications for contour detection in natural images.
previous article A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention
next article Multi-criteria Group Decision-Making Based on Interval Neutrosophic Uncertain Linguistic Variables and Choquet Integral

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Metadata
Title
Contour Detection in Colour Images Using a Neurophysiologically Inspired Model
Authors
Qi Wang
M. W. Spratling
Publication date
01-12-2016
Publisher
Springer US
Published in
Cognitive Computation / Issue 6/2016
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-016-9432-6

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