| Journal: | Computación y sistemas |
| Database: | PERIÓDICA |
| System number: | 000410207 |
| ISSN: | 1405-5546 |
| Authors: | López López, Víctor R1 Trujillo, Leonardo1 Legrand, Pierrick2 Díaz Ramírez, Victor H3 Olague, Gustavo4 |
| Institutions: | 1Instituto Tecnológico de Tijuana, Posgrado en Ciencias de la Ingeniería, Tijuana, Baja California. México 2Universite de Bordeaux I, Institut de Mathematiques de Bordeaux, Talence, Gironde. Francia 3Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital, Tijuana, Baja California. México 4Centro de Investigación Científica y de Educación Superior de Ensenada, División de Física Aplicada, Ensenada, Baja California. México |
| Year: | 2016 |
| Season: | Oct-Dic |
| Volumen: | 20 |
| Number: | 4 |
| Pages: | 565-588 |
| Country: | México |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Experimental, aplicado |
| English abstract | 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 characterized 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 |
| Disciplines: | Ciencias de la computación |
| Keyword: | Programación, Visión por computadora, Programación genética, Filtro de correlación compuesta |
| Keyword: | Computer science, Programming, Computer vision, Genetic programming, Composite correlation filter |
| Full text: | Texto completo (Ver HTML) |
