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» Markov Random Field Models in Computer Vision
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ECCV
2006
Springer
16 years 3 months ago
Learning and Incorporating Top-Down Cues in Image Segmentation
Abstract. Bottom-up approaches, which rely mainly on continuity principles, are often insufficient to form accurate segments in natural images. In order to improve performance, rec...
Xuming He, Richard S. Zemel, Debajyoti Ray
ACCV
2010
Springer
14 years 8 months ago
Human Pose Estimation Using Exemplars and Part Based Refinement
In this paper, we proposed a fast and accurate human pose estimation framework that combines top-down and bottom-up methods. The framework consists of an initialization stage and a...
Yanchao Su, Haizhou Ai, Takayoshi Yamashita, Shiho...
BMCBI
2010
152views more  BMCBI 2010»
15 years 1 months ago
Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis
Background: One of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or ...
Jens Keilwagen, Jan Grau, Stefan Posch, Ivo Grosse
ICCV
1999
IEEE
16 years 3 months ago
Fluid Motion Recovery by Coupling Dense and Parametric Vector Fields
In this paper we address the problem of estimating and analyzing the motion in image sequences that involve fluid phenomena. In this context standard motion estimation techniques ...
Étienne Mémin, Patrick Pérez
125
Voted
CVPR
2007
IEEE
16 years 3 months ago
Multiview normal field integration using level set methods
In this paper, we propose a new method to integrate multiview normal fields using level sets. In contrast with conventional normal integration algorithms used in shape from shadin...
Ju Yong Chang, Kyoung Mu Lee, Sang Uk Lee