This paper describes a system for multiple-object recognition and segmentation that (1) correctly identifies objects in a natural scene and provides a boundary for each object, (2) can identify multiple occurrences of the same object (e.g., two identical objects, side-by-side) in the scene from different training views. The algorithm is novel in that it employs statistical modeling to efficiently prune features from an identified object from the scene without disturbing similar features elsewhere in the scene. The originality of the approach allows one to analyze complex scenes that occur in nature contain multiple instances of the same object
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