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» Unsupervised scene analysis: A hidden Markov model approach
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INCDM
2010
Springer
208views Data Mining» more  INCDM 2010»
13 years 9 months ago
Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis
Abstract. Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies i...
Zhiyuan Yao, Annika H. Holmbom, Tomas Eklund, Barb...
PAMI
2010
273views more  PAMI 2010»
13 years 2 months ago
Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views
We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects...
Xiaogang Wang, Kinh Tieu, W. Eric L. Grimson
BMCBI
2010
145views more  BMCBI 2010»
13 years 7 months ago
Clustering metagenomic sequences with interpolated Markov models
Background: Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and s...
David R. Kelley, Steven L. Salzberg
ICIP
2005
IEEE
14 years 1 months ago
HMM-based motion recognition system using segmented PCA
In this paper, we propose a novel technique for modelbased recognition of complex object motion trajectories using Hidden Markov Models (HMM). We build our models on Principal Com...
Faisal I. Bashir, Wei Qu, Ashfaq A. Khokhar, Dan S...
PRL
2000
182views more  PRL 2000»
13 years 7 months ago
Bayesian MLP neural networks for image analysis
We demonstrate the advantages of using Bayesian multi layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by c...
Aki Vehtari, Jouko Lampinen