A Model-Free Voting Approach for Integrating Multiple Cues.
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Article: Vision for Robotics[Show abstract] [Hide abstract]
ABSTRACT: Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of various tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing vision-based control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.01/2009; 1(1):1-78. DOI:10.1561/2300000001
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ABSTRACT: The key issue addressed by this paper is the necessity to devise performance evaluation measures for systems that integrate multiple cues for tracking in video sequences. We propose a generic evaluation approach that can be implemented in systems that perform higher-level people tracking by integrating multiple low-level features extracted from the video data. Two new measures: video sequence accuracy (VSA) and voting average measure (VAM), are introduced and explained by using the two fundamental image processing techniques of edge and optical flow detection. The effectiveness of the approach is demonstrated using a set of real video sequences with ground truth.Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA 2008, Canberra, ACT, Australia, 1-3 December 2008; 01/2008
Conference Paper: Cue integration through discriminative accumulation[Show abstract] [Hide abstract]
ABSTRACT: Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme, which extends the idea of cue accumulation to discriminative classifiers. We derive and test the scheme for support vector machines (SVMs), but we also show that it is easily extendible to any large margin classifier. In the case of one-class SVMs the scheme can be interpreted as a new class of Mercer kernels for multiple cues. Experimental comparison with a probabilistic accumulation scheme is favorable to our method. Comparison with voting scheme shows that our method may suffer as the number of object classes increases. Based on these results, we propose a recognition algorithm consisting of a decision tree where decisions at each node are taken using our accumulation scheme. Results obtained using this new algorithm compare very favorably to accumulation (both probabilistic and discriminative) and voting scheme.Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on; 01/2004