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CORR
2010
Springer
108views Education» more  CORR 2010»
15 years 27 days ago
Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics
We revisit the problem of model-based object recognition for intensity images and attempt to address some of the shortcomings of existing Bayesian methods, such as unsuitable prior...
Vasileios Zografos, Bernard F. Buxton
ICPR
2010
IEEE
15 years 7 months ago
Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition
We present a fast and robust graph matching approach for 2D specific object recognition in images. From a small number of training images, a model graph of the object to learn is a...
Jerome Revaud, Guillaume Lavoue, Yasuo Ariki, Atil...
131
Voted
CVPR
2008
IEEE
16 years 5 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 ...
162
Voted
IJCV
2000
180views more  IJCV 2000»
15 years 3 months ago
Probabilistic Models of Appearance for 3-D Object Recognition
We describe how to model the appearance of a 3-D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses pro...
Arthur R. Pope, David G. Lowe
186
Voted
ICASSP
2011
IEEE
14 years 7 months ago
Detecting moving objects from dynamic background with shadow removal
Background subtraction is commonly used to detect foreground objects in video surveillance. Traditional background subtraction methods are usually based on the assumption that the...
Shih-Chieh Wang, Te-Feng Su, Shang-Hong Lai