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DC Field | Value | Language |
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dc.contributor.author | Azzopardi, George | - |
dc.contributor.author | Fernandez-Robles, Laura | - |
dc.contributor.author | Alegre, Enrique | - |
dc.contributor.author | Petkov, Nicolai | - |
dc.date.accessioned | 2018-02-08T10:43:08Z | - |
dc.date.available | 2018-02-08T10:43:08Z | - |
dc.date.issued | 2016-12 | - |
dc.identifier.citation | Azzopardi, G., Fernandez-Robles, L., Alegre, E., & Petkov, N. (2016). Increased generalization capability of trainable COSFIRE filters with application to machine vision. 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico. 3356-3361. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/26580 | - |
dc.description.abstract | 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 | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Computer vision | en_GB |
dc.subject | Milling machinery -- Automation | en_GB |
dc.title | Increased generalization capability of trainable COSFIRE filters with application to machine vision | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 23rd International Conference on Pattern Recognition (ICPR) | en_GB |
dc.bibliographicCitation.conferenceplace | Cancun, Mexico, 4-8/12/2016 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/ICPR.2016.7900152 | - |
Appears in Collections: | Scholarly Works - FacICTAI |
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Increased_generalization_capability_of_trainable_COSFIRE_filters_2016.pdf Restricted Access | 2.07 MB | Adobe PDF | View/Open Request a copy |
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