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- W137731712 abstract "The dissertation is concerned with computer recognition of range data using 3-D invariant features. We have derived new 3-D invariant features and developed pattern recognition algorithms using these features. The recognition systems based on these invariant features are robust and reliable. Specifically, we have done the following: (1) An innovative pattern recognition algorithm using 3-D moment invariants and multi-layer neural classifier--it can do multiview 3-D object recognition and is suitable for real time implementation. (2) An efficient invariant complex waveform representation for 3-D curves--this representation can reduce the complexity of the matching and segmentation of 3-D curves to that of 1-D waveforms. Moreover, the point correspondence problem can be efficiently solved by waveform matchings. (3) A range image analysis algorithm which is capable of analyzing a complex scene--the range image is first segmented using the split-and-merge technique and the functional approximation method. The segmented image is represented by a region adjacent graph with invariant attributes. The distance between attributed graphs is computed. This scalar distance measure is useful in model-based object inference and object classification." @default.
- W137731712 created "2016-06-24" @default.
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- W137731712 date "1990-01-01" @default.
- W137731712 modified "2023-09-24" @default.
- W137731712 title "Range data recognition using three-dimensional invariant features" @default.
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