Probabilistic object recognition and localization
Bernt Schiele, Alex Pentland · 1999
Objects can be represented by regions of local structure as well as dependencies between these regions. The appearance of local structure can be characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This paper presents a technique in which the appearance of objects is represented by the joint statistics of local neighborhood operators. A probabilistic technique based on joint statistics is developed for the identification of multiple objects at arbitrary positions and orientations. Furthermore, by incorporating structural dependencies, a procedure for probabilistic localization of objects is obtained. The current recognition system runs at approximately 10 Hz on a Silicon 02. Experimental results are provided and an application using a head mounted camera is described.