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DC Field | Value | Language |
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dc.contributor.author | Azzopardi, George | |
dc.contributor.author | Petkov, Nicolai | |
dc.date.accessioned | 2016-02-23T13:32:08Z | |
dc.date.available | 2016-02-23T13:32:08Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013, Vol.35(2), p. 490-503 | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/8375 | |
dc.description.abstract | Background: Keypoint detection is important for many computer vision applications. Existing methods suffer from insufficient selectivity regarding the shape properties of features and are vulnerable to contrast variations and to the presence of noise or texture. Methods: We propose a trainable filter which we call Combination Of Shifted FIlter REsponses (COSFIRE) and use for keypoint detection and pattern recognition. It is automatically configured to be selective for a local contour pattern specified by an example. The configuration comprises selecting given channels of a bank of Gabor filters and determining certain blur and shift parameters. A COSFIRE filter response is computed as the weighted geometric mean of the blurred and shifted responses of the selected Gabor filters. It shares similar properties with some shape-selective neurons in visual cortex, which provided inspiration for this work. Results: We demonstrate the effectiveness of the proposed filters in three applications: the detection of retinal vascular bifurcations (DRIVE dataset: 98.50 percent recall, 96.09 percent precision), the recognition of handwritten digits (MNIST dataset: 99.48 percent correct classification), and the detection and recognition of traffic signs in complex scenes (100 percent recall and precision). Conclusions: The proposed COSFIRE filters are conceptually simple and easy to implement. They are versatile keypoint detectors and are highly effective in practical computer vision applications. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IEEE Transactions on Pattern Analysis and Machine Intelligence | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Medical innovations | en_GB |
dc.subject | Medical technology | en_GB |
dc.subject | Optical character recognition | en_GB |
dc.subject | Tracking and trailing | en_GB |
dc.title | Trainable COSFIRE filters for keypoint detection and pattern recognition | en_GB |
dc.type | article | 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.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/TPAMI.2012.106 | |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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PAMI2013.pdf | 4.46 MB | Adobe PDF | View/Open |
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