The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust Real-Time Face Detection
International Journal of Computer Vision
Boosted human-centric hybrid classifier
Proceedings of the 43rd annual Southeast regional conference - Volume 1
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Real-time moving object segmentation in H.264 compressed domain based on approximate reasoning
International Journal of Approximate Reasoning
Research on eye-state based monitoring for drivers' dozing
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people
International Journal of Approximate Reasoning
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Component based object detection approaches have been shown to significantly improve object detection performance in adversities such as occlusion, variations in pose, in and out of plane rotation and poor illumination. Even the best object detectors are prone to errors when used in a global object detection scheme (one that uses the whole object as a single entity for detection purpose), due to these problems. We propose a fuzzy approach to object detection that treats an object as a set of constituent components rather than a single entity. The object detection task is completed in two steps. In the first step, candidates for respective components are selected based on their appearance match and handed over to the geometrical configuration classifier. The geometrical configuration classifier is a fuzzy inference engine that selects one candidate for each component such that each candidate is a reasonable match to the corresponding component in terms of appearance and also a good fit for the overall geometrical model. The detected object consists of candidates that are not necessarily the best in terms of appearance match or the closest to the geometrical model in terms of placement. The output is a set of candidates that is an optimal combination satisfying both criteria. We evaluate the technique on a well known face dataset and show that the technique results in detection of most faces in a scale-invariant manner. The technique has been shown to be robust to in-plane rotations and occlusion.