A framework for statistical 3-D object recognition
نویسندگان
چکیده
In this contribution we describe an object{oriented software architecture for image segmentation, 3{D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identi cation applies the Bayesian decision rule. We show results of 3{D object recognition experiments based on the observation of 2{D points or lines.
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ورودعنوان ژورنال:
- Pattern Recognition Letters
دوره 18 شماره
صفحات -
تاریخ انتشار 1997