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» Boosting Object Detection Using Feature Selection
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ICPR
2006
IEEE
14 years 8 months ago
Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection
We present a framework for object detection that is invariant to object translation, scale, rotation, and to some degree, occlusion, achieving high detection rates, at 14 fps in c...
Michael Villamizar, Alberto Sanfeliu, Juan Andrade...
ICPR
2004
IEEE
14 years 8 months ago
Object Recognition Using Segmentation for Feature Detection
: A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a se...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
CVPR
2007
IEEE
14 years 9 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof
ICDAR
2009
IEEE
14 years 2 months ago
Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm
We have proposed a complete system for text detection and localization in gray scale scene images. A boosting framework integrating feature and weak classifier selection based on...
Shehzad Muhammad Hanif, Lionel Prevost