A General Framework for Object Detection

A General Framework for Object Detection,10.1109/ICCV.1998.710772,Constantine P. Papageorgiou,Michael Oren,Tomaso Poggio

A General Framework for Object Detection   (Citations: 437)
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This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a com- pact representation of an object class which is used as an input to a suppori vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique in two domains whose inherent information content dif- fers significantly. The first system is face detection and the second is the domain of people which, in con- trast to faces, vary greatly in color, texture, and pat- terns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand- crafted) models or motion-based segmentation. The paper also presents a motion-based extension to en- hance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.
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