Abstract
This paper presents a viewpoint planning method for an active object recognition by using the shape information of objects. The proposed method uses the certainty that a given object is classifed into one category. The certainty is represented by a probabilistic model. Moreover, the categorization possibility of unknown objects are quantied by the entropy indicating the ambiguity of the recognition. When an object is observed, the certainty is calculated by using the measured data. For each viewpoint, the entropy is computed based on the certainty. We choose as a next viewpoint the viewpoint with minimum entropy. From the experimental results using real objects, the proposed method can achieve en efficient object recognition compared with the method which selects viewpoints randomly.
