Matching Local Invariant Features with Contextual Information
The main advantage of using local invariant features is their local character which yields robustness to occlusion and varying background. Therefore, local features have proved to be a powerful tool for finding correspondences between images, and have been employed in many applications. However, the...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:52528 |
| Acceso en línea: | https://ddd.uab.cat/record/52528 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.271 |
| Access Level: | acceso abierto |
| Palabra clave: | Image matching Local invariant features SIFT Contextual information Object recognition Imatges coincidents Característiques locals invariants Informació contextual Reconeixement d'objectes Imagenes coincidentes Características locales invariantes Información contextual Reconocimiento de objetos |
| Sumario: | The main advantage of using local invariant features is their local character which yields robustness to occlusion and varying background. Therefore, local features have proved to be a powerful tool for finding correspondences between images, and have been employed in many applications. However, the local character limits the descriptive capability of features descriptors, and local features fail to resolve ambiguities that can occur when an image shows multiple similar regions. Considering some global information will clearly help to achieve better performances. The question is which information to use and how to use it. Context can be used to enrich the description of the features, or used in the matching step to filter out mismatches. In this paper, we compare different recent methods which use context for matching and show that better results are obtained if contextual information is used during the matching process. We evaluate the methods in two applications: wide baseline matching and object recognition, and it appears that a relaxation based approach gives the best results. |
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