IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithm Wavelet Design for Signal Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rate-Distortion Analysis of Discrete-HMM Pose Estimation via Multiaspect Scattering Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
Proceedings of the 24th international conference on Machine learning
Detecting Abnormal Events via Hierarchical Dirichlet Processes
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Hidden Markov models with stick-breaking priors
IEEE Transactions on Signal Processing
Hidden markov model networks for multiaspect discriminative features extraction from radar targets
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Development of head detection and tracking systems for visual surveillance
Personal and Ubiquitous Computing
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This article presents a new approach for target identification, in which we fuse scattering data from multiple target-sensor orientations. The multiaspect data is processed via hidden Markov model (HMM) classifiers, buttressed by physics-based feature extraction. This approach explicitly accounts for the fact that the target-sensor orientation is generally unknown or “hidden”. Discrimination results are presented for measured scattering data