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Genetic algorithm-based optimization for COSFIRE filters with application to object recognition

version 1.1.0.0 (1.09 MB) by Laura Fernandez Robles
Increasing generalization ability of COSFIRE filters by optimizing the selection of contour features

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Updated 07 Sep 2016

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The recently proposed trainable COSFIRE filters are highly effective in a wide range of computer vision applications, including object recognition, image classification, contour detection and retinal vessel segmentation. A COSFIRE filter is selective for a collection of contour parts in a certain spatial arrangement. These contour parts and their spatial arrangement are determined in an automatic configuration procedure from a single user-specified pattern of interest. The traditional configuration, however, does not guarantee the selection of the most distinctive contour parts. We propose a genetic algorithm-based optimization step in the configuration of COSFIRE filters that determines the minimum subset of contour parts that best characterize the pattern of interest. We use a public dataset of images of an edge milling head machine equipped with multiple cutting tools to demonstrate the effectiveness of the proposed optimization step for the detection and localization of such tools. The optimization process that we propose yields COSFIRE filters with substantially higher generalization capability. With an average of only six COSFIRE filters we achieve high precision P and recall R rates (P=91.99%,R=96.22%). This outperforms the original COSFIRE filter approach (without optimization) mostly in terms of recall. The proposed optimization procedure increases the efficiency of COSFIRE filters with little effect on the selectivity.
George Azzopardi; Laura Fernández-Robles; Enrique Alegre and Nicolai Petkov. Increased generalization capability of trainable COSFIRE filters with application to machine vision. ICPR 2016. In print.

Cite As

Laura Fernandez Robles (2021). Genetic algorithm-based optimization for COSFIRE filters with application to object recognition (https://www.mathworks.com/matlabcentral/fileexchange/59028-genetic-algorithm-based-optimization-for-cosfire-filters-with-application-to-object-recognition), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (2)

You can find the ground truth images in: http://pitia.unileon.es/varp/node/395

wissal guedri

i run the code but there is an error :
Reference to non-existent field 'groundtruthIm'.

Error in COSFIRE_Application (line 229)
gt{end}.groundtruthIm = gt{end}.groundtruthIm > 0;
Would you help me please?

MATLAB Release Compatibility
Created with R2014a
Compatible with any release
Platform Compatibility
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