Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/29076| Title: | COSFIRE : a trainable features approach to pattern recognition |
| Authors: | Azzopardi, George Petkov, Nicolai |
| Keywords: | Pattern recognition systems Computer vision Computer graphics Data structures (Computer science) Artificial intelligence Optical pattern recognition |
| Issue Date: | 2013 |
| Publisher: | Radboud University Nijmegen |
| Citation: | Azzopardi, G., & Petkov, N. (2013). COSFIRE : a trainable features approach to pattern recognition. BENELEARN 2013, Nijmegen. |
| Abstract: | In a recent work (Azzopardi & Petkov, 2013), we proposed a trainable features approach to visual pattern recognition. It is called COSFIRE, which stands for Combination of Shifted Filter Responses. A COSFIRE operator is automatically configured by a specified pattern of interest, referred to as a prototype, and is then able to detect the same and similar patterns in other images. The configuration comprises the determination of the orientations of dominant contour parts and their mutual spatial arrangement. |
| URI: | https://www.um.edu.mt/library/oar//handle/123456789/29076 |
| Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| COSFIRE_A_trainable_features_approach_to pattern_recognition_2013.pdf | 490.39 kB | Adobe PDF | View/Open | |
| Presenttion_COSFIRE_A_trainable_features_approach_to_pattern_recognition_2013.pdf | 15.55 MB | Adobe PDF | View/Open |
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