High-precision stereo disparity estimation using HMMF models
In this paper, stereo disparity reconstruction is formulated as a parametric segmentation problem in a Bayesian framework: the goal is to partition the reference image into a set of non-overlapping regions, inside each one of which a specific disparity model (which consists of two coupled membranes)...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | México |
| Institución: | Centro de Investigación en Matemáticas |
| Repositorio: | Repositorio Institucional CIMAT |
| Idioma: | inglés |
| OAI Identifier: | oai:cimat.repositorioinstitucional.mx:1008/893 |
| Acceso en línea: | http://cimat.repositorioinstitucional.mx/jspui/handle/1008/893 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/MSC/Redes Neurales info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120304 |
| Sumario: | In this paper, stereo disparity reconstruction is formulated as a parametric segmentation problem in a Bayesian framework: the goal is to partition the reference image into a set of non-overlapping regions, inside each one of which a specific disparity model (which consists of two coupled membranes) is adjusted. The problem of simultaneously finding the regions and the parameters of the corresponding models is formulated using a novel probabilistic framework which uses a hidden Markov random measure field model, which allows one to efficiently find the optimal estimators by minimization of a differentiable cost function. This framework also allows for the explicit modeling of occlusions, consistency constraints and correspondence of disparity and intensity discontinuities. It is shown experimentally that this method produces competitive results, with respect to state-of-the-art methods, for discretized (integer) disparities and significantly better results for high-precision real-valued disparities. |
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