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» POP: Patchwork of Parts Models for Object Recognition
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NIPS
2001
14 years 11 months ago
Probabilistic principles in unsupervised learning of visual structure: human data and a model
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...
TMM
2008
167views more  TMM 2008»
14 years 9 months ago
Mining Appearance Models Directly From Compressed Video
In this paper, we propose an approach to learning appearance models of moving objects directly from compressed video. The appearance of a moving object changes dynamically in vide...
Datong Chen, Qiang Liu, Mingui Sun, Jie Yang
CVPR
2012
IEEE
13 years 9 days ago
Unsupervised co-segmentation through region matching
Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiplescale mu...
José C. Rubio, Joan Serrat, Antonio M. L&oa...
ICVS
2003
Springer
15 years 3 months ago
Navigating through Logic-Based Scene Models for High-Level Scene Interpretations
This paper explores high-level scene interpretation with logic-based conceptual models. The main interest is in aggregates which describe interesting co-occurrences of physical obj...
Bernd Neumann, Thomas Weiss

Book
5396views
16 years 8 months ago
Markov Random Field Modeling in Computer Vision
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Stan Z. Li