Object class detection using local image features and point pattern matching constellation search

Authors:
Alexander Drobchenko;Jarmo Ilonen;Joni-Kristian Kamarainen;Albert Sadovnikov;Heikki Kälviäinen;Miroslav Hamouz
Affiliations:
Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology;Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology;Centre for Vision, Speech and Signal Processing, University of Surrey;Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology;Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology;Centre for Vision, Speech and Signal Processing, University of Surrey
Venue:
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Year:
2007

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Abstract

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.