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PAMI
2007
155views more  PAMI 2007»
13 years 9 months ago
Localization of Shapes Using Statistical Models and Stochastic Optimization
—In this paper, we present a new model for deformations of shapes. A pseudolikelihood is based on the statistical distribution of the gradient vector field of the gray level. The...
François Destrempes, Max Mignotte, Jean-Fra...
CAIP
2003
Springer
166views Image Analysis» more  CAIP 2003»
14 years 3 months ago
Unsupervised Segmentation Incorporating Colour, Texture, and Motion
Abstract. In this paper we integrate colour, texture, and motion into a segmentation process. The segmentation consists of two steps, which both combine the given information: a pr...
Thomas Brox, Mikaël Rousson, Rachid Deriche, ...
NIPS
2004
13 years 11 months ago
A Topographic Support Vector Machine: Classification Using Local Label Configurations
The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this ass...
Johannes Mohr, Klaus Obermayer
CVPR
2011
IEEE
13 years 1 months ago
Connecting Non-Quadratic Variational Models and MRFs
Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approaches are ubiquitous in low-level vision, including image restoration, segmentation, opti...
Kevin Schelten, Stefan Roth
SCIA
2009
Springer
261views Image Analysis» more  SCIA 2009»
14 years 2 months ago
Dense and Deformable Motion Segmentation for Wide Baseline Images
In this paper we describe a dense motion segmentation method for wide baseline image pairs. Unlike many previous methods our approach is able to deal with deforming motions and lar...
Juho Kannala, Esa Rahtu, Sami S. Brandt, Janne Hei...