| Summary: | 碩士 === 國立交通大學 === 控制工程系 === 84 === We propose an approach to 3-D object recognition
irrespective of its position, size, and orientation. We use
a fuzzy measure technique to find an optimal threshold value
and obtain the shape of the object from the background in
an input image. We then build an image pyramid data structure
to extract the invariant features. This is supported by a
segmentation technique using annular and sector windows.
After obtaining the features of the object, we adopt a
neural network model, the supervised fuzzy adaptive Hamming net,
as a classifier whose purpose is to partition the feature
space into decision regions corresponding to each object class.
The simulationresults show that the proposed method can obtain a
satisfactory performance. So, the proposed method provides a
suitable approach to 3-D object recognition.
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