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» Novel image feature alphabets for object recognition
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ICIP
2007
IEEE
15 years 4 months ago
Dominant Sets-Based Action Recognition using Image Sequence Matching
Action recognition is one of the most active research fields in computer vision. In this paper, we propose a novel method for classifying human actions in a series of image seque...
Qingdi Wei, Weiming Hu, Xiaoqin Zhang, Guan Luo
CVPR
2010
IEEE
14 years 10 months ago
High performance object detection by collaborative learning of Joint Ranking of Granules features
Object detection remains an important but challenging task in computer vision. We present a method that combines high accuracy with high efficiency. We adopt simplified forms of...
Chang Huang, Ramakant Nevatia
CVPR
2011
IEEE
14 years 5 months ago
Mining Discriminative Co-occurrence Patterns for Visual Recognition
The co-occurrence pattern, a combination of binary or local features, is more discriminative than individual features and has shown its advantages in object, scene, and action rec...
Junsong Yuan, Ming Yang, Ying Wu
ICIP
2005
IEEE
15 years 11 months ago
Object tracking based on morphological elastic graph matching
This paper presents a novel method for real-time tracking of objects in video sequences. Tracking is performed using the so-called Morphological Elastic Graph Matching algorithm. ...
Georgios N. Stamou, Nikos Nikolaidis, Ioannis Pita...
ICCV
2011
IEEE
13 years 10 months ago
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers ...
Matthew D. Zeiler, Graham W. Taylor, Rob Fergus