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| Format: | Artículo científico |
| Language: | en |
| Published: |
Instituto Politécnico Nacional
2016
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| Online Access: | https://www.redalyc.org/articulo.oa?id=61549258002 |
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Table of Contents:
- Comparison of Local Feature Extraction Paradigms Applied to Visual SLAM Víctor R. López-López Leonardo Trujillo Pierrick Legrand Victor H. Díaz-Ramírez Gustavo Olague Computación SLAM Local features genetic programming composite correlation filter The detection and description of locally salient regions is one of the most widely used low-level processes in modern computer vision systems. The general approach relies on the detection of stable and invariant image features that can be uniquely charac- terized using compact descriptors. Many detection and description algorithms have been proposed, most of them derived using different assumptions or problem models. This work presents a comparison of different approaches towards the feature extraction problem, namely: (1) standard computer vision techniques; (2) automatic synthesis techniques based on genetic programming (GP); and (3) a new local descriptor based on composite correlation filtering, proposed for the first time in this paper. The considered methods are evaluated on a difficult real-world problem, vision-based simultaneous localization and mapping (SLAM). Using three experimental scenarios, results indicate that the GP-based methods and the correlation filtering techniques outperform widely used computer vision algorithms such as the Harris and Shi-Tomasi detectors and the Speeded Up Robust Features descriptor. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61549258002 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.20