Efficient search and verification for function based classification from real range images
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摘要
In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about 150 real raw range images of object instances from 10 classes.
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论文评审过程:Received 10 January 2006, Accepted 23 October 2006, Available online 12 December 2006.
论文官网地址:https://doi.org/10.1016/j.cviu.2006.10.003