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Model based object recognition through hypothesis and parameter matching
Presents a model based method to recognize objects. Firstly hypotheses are generated from shape primitives and (their) Boolean operations to predict a complex object, and then are verified by finding the minimum cost in parameter space. A number of optimization techniques are considered and then applied to practical search on real-world data. The authors emphasize parameter estimation and consider the procedure as a numerical optimization problem. A technique for finding global minima is reported, and its efficiency is proven by applying the method for recognition of landuse patches in images of agricultural fields.<>
Model based object recognition through hypothesis and parameter matching
Presents a model based method to recognize objects. Firstly hypotheses are generated from shape primitives and (their) Boolean operations to predict a complex object, and then are verified by finding the minimum cost in parameter space. A number of optimization techniques are considered and then applied to practical search on real-world data. The authors emphasize parameter estimation and consider the procedure as a numerical optimization problem. A technique for finding global minima is reported, and its efficiency is proven by applying the method for recognition of landuse patches in images of agricultural fields.<>
Model based object recognition through hypothesis and parameter matching
Fang Luo (author) / Mulder, N.J. (author)
1993-01-01
261626 byte
Conference paper
Electronic Resource
English
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