2 September 1993 Neural net range image segmentation for object recognition
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Abstract
A technique for performing surface-based segmentation of range images using neural nets is introduced. In this approach, multilayered neural nets are used to classify local image patches according to the type of surface they belong to, based on features extracted from range and surface normal information. Central component to the efficiency and robustness is a near orientational invariant local data organization which takes place before features are extracted. This data organization reduces internal complexity by shifting the orientation invariance burden from the dimensionality of the feature spaces and/or from the internal architecture of the networks, to a much simpler sequencing of local data. The result is a well segmented image in which surfaces are properly labeled and delimited, without over segmentation. The approach shows to be robust in front of noise.
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Leda Villalobos, Francis L. Merat, "Neural net range image segmentation for object recognition", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152554; https://doi.org/10.1117/12.152554
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