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» A New Bayesian Framework for Object Recognition
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ICIP
2008
IEEE
14 years 1 months ago
Matching tracking sequences across widely separated cameras
In this paper, we present a new solution to the problem of matching tracking sequences across different cameras. Unlike snapshot-based appearance matching which matches objects by...
Yinghao Cai, Kaiqi Huang, Tieniu Tan
ICPR
2006
IEEE
14 years 8 months ago
Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates
Recent research in object recognition has demonstrated the advantages of representing objects and scenes through localized patterns such as small image templates. In this paper we...
Ilkka Autio, Jussi T. Lindgren
ICCV
2005
IEEE
14 years 9 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu

Publication
363views
12 years 6 months ago
Detecting and Discriminating Behavioural Anomalies
This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex tempor...
Chen Change Loy, Tao Xiang, Shaogang Gong
CVPR
2012
IEEE
11 years 9 months ago
Estimating the aspect layout of object categories
In this work we seek to move away from the traditional paradigm for 2D object recognition whereby objects are identified in the image as 2D bounding boxes. We focus instead on: i...
Yu Xiang, Silvio Savarese