Abstract

Recognizing three-dimensional (3D) shape based on cues extracted from object curves, is discussed. For fast recognition it is desirable that the 3D curve representation is inherently simple and invariant to affine and projective transformations, and to have the object recognizer fast and computationally simple. These goals are achieved by adopting a model-based approach using B-splines for curve representation and a backpropagation neural network for text/marking recognition. The object shape was computed from the image curves using stereo imaging, and the object type was identified by recognizing or reading the text/markings on the object based on features that are invariant to the object shape, to rotation, to scaling, and to translation. >

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