Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison framework for 3d object classification methods
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
A parts-based approach for automatic 3D shape categorization using belief functions
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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Most research on 3-D object classification and recognition focuses on recognition of objects in 3-D scenes from a small database of known 3-D models. Such an approach does not scale well to large databases of objects and does not generalize well to unknown (but similar) object classification. This paper presents two ideas to address these problems (i) class selection, i.e., grouping similar objects into classes (ii) class prototyping, i.e., exploiting common structure within classes to represent the classes. At run time matching a query against the prototypes is sufficient for classification. This approach will not only reduce the retrieval time but also will help increase the generalizing power of theclassification algorithm. Objects are segmented into classes automatically using an agglomerative clustering algorithm. Prototypes from these classes are extracted using one of three class prototyping algorithms. Experimental results demonstrate the effectiveness of the two steps in speeding up the classification process without sacrificing accuracy.