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CVPR
2004
IEEE
15 years 6 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...
ICCV
2005
IEEE
15 years 8 months ago
Learning Models for Predicting Recognition Performance
This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of s...
Rong Wang, Bir Bhanu
SIGMOD
2003
ACM
158views Database» more  SIGMOD 2003»
16 years 2 months ago
Processing Set Expressions over Continuous Update Streams
There is growing interest in algorithms for processing and querying continuous data streams (i.e., data that is seen only once in a fixed order) with limited memory resources. In ...
Sumit Ganguly, Minos N. Garofalakis, Rajeev Rastog...
MVA
2007
179views Computer Vision» more  MVA 2007»
15 years 2 months ago
Multi-object trajectory tracking
The majority of existing tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework using a Hidden Markov Model, where the distribution ...
Mei Han, Wei Xu, Hai Tao, Yihong Gong
ICIP
2006
IEEE
16 years 4 months ago
Determining Achievable Rates for Secure, Zero Divergence, Steganography
In steganography (the hiding of data into innocuous covers for secret communication) it is difficult to estimate how much data can be hidden while still remaining undetectable. To...
Kenneth Sullivan, Kaushal Solanki, B. S. Manjunath...