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» Discovering Operators and Features for Object Detection
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ICCV
2005
IEEE
15 years 12 months ago
A Supervised Learning Framework for Generic Object Detection in Images
In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly impor...
Saad Ali, Mubarak Shah
ICMCS
2005
IEEE
182views Multimedia» more  ICMCS 2005»
15 years 3 months ago
An integrated approach for generic object detection using kernel PCA and boosting
In this paper we present a novel framework for generic object class detection by integrating Kernel PCA with AdaBoost. The classifier obtained in this way is invariant to changes...
Saad Ali, Mubarak Shah
CVPR
2009
IEEE
1119views Computer Vision» more  CVPR 2009»
16 years 5 months ago
Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation
In this paper, a novel feature named Adaptive Contour Feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in...
Wei Gao (Tsinghua University), Haizhou Ai (Tsinghu...
EC
2008
174views ECommerce» more  EC 2008»
14 years 9 months ago
Automated Design of Image Operators that Detect Interest Points
This work describes how evolutionary computation can be used to synthesize lowlevel image operators that detect interesting points on digital images. Interest point detection is a...
Leonardo Trujillo, Gustavo Olague
AAAI
2007
15 years 12 days ago
Detection of Multiple Deformable Objects using PCA-SIFT
In this paper, we address the problem of identifying and localizing multiple instances of highly deformable objects in real-time video data. We present an approach which uses PCA-...
Stefan Zickler, Alexei A. Efros