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» POP: Patchwork of Parts Models for Object Recognition
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NIPS
2001
15 years 1 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 11 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 2 months 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 4 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
237
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Book
5396views
16 years 10 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