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110
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NIPS
2004
14 years 11 months ago
Contextual Models for Object Detection Using Boosted Random Fields
We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which use boos...
Antonio Torralba, Kevin P. Murphy, William T. Free...
ACCV
2010
Springer
14 years 4 months ago
MRF-Based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos
Abstract. Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model i...
Vikas Reddy, Conrad Sanderson, Andres Sanin, Brian...
CVPR
2007
IEEE
15 years 11 months ago
Detection and segmentation of moving objects in highly dynamic scenes
Detecting and segmenting moving objects in dynamic scenes is a hard but essential task in a number of applications such as surveillance. Most existing methods only give good resul...
Aurélie Bugeau, Patrick Pérez
CVPR
1999
IEEE
15 years 11 months ago
Generic Object Detection using Model Based Segmentation
This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. The underlying assumption is that generic objects that are man made, fre...
Zhiqian Wang, Jezekiel Ben-Arie
89
Voted
CVPR
1999
IEEE
1071views Computer Vision» more  CVPR 1999»
15 years 11 months ago
Adaptive Background Mixture Models for Real-Time Tracking
A common method for real-time segmentation of moving regions in image sequences involves "background subtraction," or thresholding the error between an estimate of the i...
Chris Stauffer, W. Eric L. Grimson