3-D Object Recognition Using Neural Network Classification and Pyramid Feature Extraction Technique

• Main Authors: Lin, Chuan-Chung, 林傳崇
• Other Authors: Sheng-Fuu Lin
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/12081833368431441615

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.