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This paper presents an approach to object detection which is based on recent work in statistical models for texture synthesis and recognition 7, 4, 23, 17 . Our method follows the texture r ecognition work of De Bonet and Viola 4 . We use feature vectors which capture the joint occurrence o f l o cal features at multiple resolutions. The distribution of feature v e ctors for a set of training images of an object class is estimated by clustering the data and then forming a mixture o f gaussiandoi:10.1109/iccv.1999.790386 dblp:conf/iccv/RikertJV99 fatcat:yowwrefehvfcreuym3n5bk7oem