Probabilistic Search for Object Segmentation and Recognition

Hillenbrand, Dr. Ulrich and Hirzinger, Prof. Dr. Gerd (2002) Probabilistic Search for Object Segmentation and Recognition. [Conference Paper]

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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.

Item Type:Conference Paper
Keywords:scene analysis, object recognition, grouping, segmentation, surface geometry, statistical modeling, range-data processing, searching, optimization
Subjects:Computer Science > Machine Vision
Computer Science > Machine Learning
Computer Science > Robotics
ID Code:2393
Deposited By: Hillenbrand, Dr. Ulrich
Deposited On:09 Aug 2002
Last Modified:11 Mar 2011 08:54

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