Abstract
To have a general-purpose machine vision capability, we must be able to recognize things; we argue that most natural objects have a part structure that we can recover from image data and thus use as the basis for “general-purpose” recognition. We describe a “parts” representation that is fairly general purpose, despite having only a small number of parameters. Having this expressive power captured by a small number of parameters allows us to approach the problem of recovering an object’s part structure by use of the model-based vision technique of global search-and-match. We present several examples of recovering part structure using various types of range imagery to show that the recovery procedure is robust.
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