Object Class Detection Using Local Image Features and Point Pattern Matching Constellation Search

Several novel methods based on locally extracted image features and spatial constellation models have recently been introduced for invariant object class detection and recognition. The accuracy and reliability of the methods depend on the success of both tasks: image feature extraction and spatial constellation model search. In this study a novel method for object class detection is introduced. It combines supervised Gabor-based confidence-ranked image features and affine invariant point pattern matching. The method is able to deal with occlusions and its potential is demonstrated on a standard face database.

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