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The appearance of an object is composed of local structure.This local structure can be described and characterized by a vectorof local features measured by local operators such as Gaussianderivatives or Gabor filters. This article presents a technique whereappearances of objects are represented by the joint statistics ofsuch local neighborhood operators. As such, this represents a newclass of appearance based techniques for computer vision. Based onjoint statistics, the paper develops techniques for theidentification of multiple objects at arbitrary positions andorientations in a cluttered scene. Experiments show that thesetechniques can identify over 100 objects in the presence of majorocclusions. Most remarkably, the techniques have low complexity andtherefore run in real-time.