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» Object Recognition Using Segmentation for Feature Detection
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CVPR
2008
IEEE
14 years 9 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
TCSV
2008
291views more  TCSV 2008»
13 years 7 months ago
A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories
Abstract--This work is dedicated to a statistical trajectorybased approach addressing two issues related to dynamic video content understanding: recognition of events and detection...
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L...
CIVR
2007
Springer
173views Image Analysis» more  CIVR 2007»
14 years 1 months ago
Fast and cheap object recognition by linear combination of views
In this paper, we present a real-time algorithm for 3D object detection in images. Our method relies on the Ullman and Basri [13] theory which claims that the same object under di...
Jérome Revaud, Guillaume Lavoué, Yas...
WACV
2005
IEEE
14 years 1 months ago
Incorporating Background Invariance into Feature-Based Object Recognition
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformation...
Andrew N. Stein, Martial Hebert
CVPR
2004
IEEE
13 years 11 months ago
Scale-Invariant Shape Features for Recognition of Object Categories
We introduce a new class of distinguished regions based on detecting the most salient convex local arrangements of contours in the image. The regions are used in a similar way to ...
Frédéric Jurie, Cordelia Schmid