Authors:
Pourya Hoseini
;
Shuvo Kumar Paul
;
Mircea Nicolescu
and
Monica Nicolescu
Affiliation:
Department of Computer Science and Engineering, University of Nevada, Reno, U.S.A.
Keyword(s):
Object Recognition, Active Vision, Next Best View, View Planning, Foreshortening, Classification Dissimilarity, Robotics.
Abstract:
Active vision represents a set of techniques that attempt to incorporate new visual data by employing camera motion. Object recognition is one of the main areas where active vision can be particularly beneficial. In cases where recognition is uncertain, new perspectives of an object can help in improving the quality of observation and potentially the recognition. A key question, however, is from where to look at the object. Current approaches mostly consider creating an occupancy grid of known object voxels or imagining the entire object shape and appearance to determine the next camera pose. Another current trend is to show every possible object view to the vision system during the training time. These methods typically require multiple observations or considerable training data and time to effectively function. In this paper, a next best view system is proposed that takes into account only the initial surface shape and appearance of the object, and subsequently determines the next
camera pose. Therefore, it is a single-shot method without the need to have any specifically made dataset for the training. Experimental validations prove the feasibility of the proposed method in finding good viewpoints while showing significant improvements in recognition performance.
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