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Appearance-based active object recognition
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DOI：10.1016/S0262-8856(99)00075-X.png)
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摘要
En 中文
We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to improve the classification result obtained from a single view. The approach uses an appearance-based object representation, namely the parametric eigenspace, and augments it by probability distributions. This enables us to cope with possible variations in the input images due to errors in the pre-processing chain or changing imaging conditions. Furthermore, the use of probability distributions gives us a gauge to perform view planning. Multiple observations lead to a significant increase in recognition rate. Action planning is shown to be of great use in reducing the number of images necessary to achieve a certain recognition performance when compared to a random strategy. (C) 2000 Elsevier Science B.V. All rights reserved.
Keyword:
action planning
object recognition
information fusion
parametric eigenspace
probability theory
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