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» Markov Random Field Models in Computer Vision
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KDD
2006
ACM
147views Data Mining» more  KDD 2006»
16 years 2 months ago
Summarizing itemset patterns using probabilistic models
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interp...
Chao Wang, Srinivasan Parthasarathy
ICPR
2002
IEEE
16 years 2 months ago
Probabilistic Models for Generating, Modelling and Matching Image Categories
In this paper we present a probabilistic and continuous framework for supervised image category modelling and matching as well as unsupervised clustering of image space into image...
Hayit Greenspan, Shiri Gordon, Jacob Goldberger
ICVS
2001
Springer
15 years 6 months ago
Adapting Object Recognition across Domains: A Demonstration
High-level vision systems use object, scene or domain specific knowledge to interpret images. Unfortunately, this knowledge has to be acquired for every domain. This makes it diffi...
Bruce A. Draper, Ulrike Ahlrichs, Dietrich Paulus
CVPR
2009
IEEE
16 years 8 months ago
Memory-based particle filter for face pose tracking robust under complex dynamics
A novel particle filter, the Memory-based Particle Filter (M-PF), is proposed that can visually track moving objects that have complex dynamics. We aim to realize robustness aga...
Dan Mikami (NTT), Kazuhiro Otsuka (NTT), Junji YAM...
SIGGRAPH
2000
ACM
15 years 5 months ago
Acquiring the reflectance field of a human face
We present a method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint. We first acqu...
Paul E. Debevec, Tim Hawkins, Chris Tchou, Haarm-P...