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» Modeling Image Textures by Gibbs Random Fields
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ECCV
2006
Springer
16 years 2 months ago
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation
Abstract. This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned ...
Jamie Shotton, John M. Winn, Carsten Rother, Anton...
TIP
2010
129views more  TIP 2010»
14 years 7 months ago
Image Segmentation by MAP-ML Estimations
Abstract--Image segmentation plays an important role in computer vision and image analysis. In this paper, image segmentation is formulated as a labeling problem under a probabilit...
Shifeng Chen, Liangliang Cao, Yueming Wang, Jianzh...
ICPR
2004
IEEE
16 years 1 months ago
Non-linear Reflectance Model for Bidirectional Texture Function Synthesis
A rough texture modelling involves a huge image data-set - the Bidirectional Texture Function (BTF). This 6-dimensional function depends on planar texture coordinates as well as o...
Jirí Filip, Michal Haindl
88
Voted
ICPR
2008
IEEE
15 years 7 months ago
Face super-resolution using 8-connected Markov Random Fields with embedded prior
In patch based face super-resolution method, the patch size is usually very small, and neighbor patches’ relationship via overlapped regions is only to keep smoothness of recons...
Kai Guo, Xiaokang Yang, Rui Zhang, Guangtao Zhai, ...
84
Voted
IJCV
2000
110views more  IJCV 2000»
15 years 12 days ago
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed ...
Javier Portilla, Eero P. Simoncelli