Probabilistic Search for Object Segmentation and Recognition

Details Probabilistic Search for Object Segmentation and Recognition Probabilistic Search for Object Segmentation and Recognition

2002-08-05

arxiv.org/abs/cs/0208005v1

Proceedings ECCV 2002, Lecture Notes in Computer Science Vol. 2352, pp. 791-806

Computer Science

Computer Vision and Pattern Recognition

18 pages, 5 figures

Scientific paper

The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape.

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