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» Object Detection Via Boosted Deformable Features
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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
2009
IEEE
15 years 2 months ago
Shape Band: A Deformable Object Detection Approach
In this paper, we focus on the problem of detecting/ matching a query object in a given image. We propose a new algorithm, shape band, which models an object within a bandwidth ...
Xiang Bai (Huazhong University of Science and Tec...
NIPS
2003
13 years 9 months ago
Mutual Boosting for Contextual Inference
Mutual Boosting is a method aimed at incorporating contextual information to augment object detection. When multiple detectors of objects and parts are trained in parallel using A...
Michael Fink 0002, Pietro Perona
CVPR
2008
IEEE
14 years 9 months ago
Structure-perceptron learning of a hierarchical log-linear model
In this paper, we address the problems of deformable object matching (alignment) and segmentation with cluttered background. We propose a novel hierarchical log-linear model (HLLM...
Long Zhu, Yuanhao Chen, Xingyao Ye, Alan L. Yuille
CVPR
2001
IEEE
14 years 9 months ago
Learning Representative Local Features for Face Detection
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...