Fundamentals of digital image processing
Fundamentals of digital image processing
Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data
Signal Processing - Special section on information theoretic aspects of digital watermarking
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Generalized feature extraction using expansion matching
IEEE Transactions on Image Processing
Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling Stem Cells Lineages with Markov Trees
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
An EM algorithm to learn sequences in the wavelet domain
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Global optimization of wavelet-domain hidden Markov tree for image segmentation
Pattern Recognition
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An image of a three-dimensional target is generally characterized by the visible target subcomponents, with these dictated by the target-sensor orientation (target pose). An image often changes quickly with variable pose. We define a class as a set of contiguous target-sensor orientations over which the associated target image is relatively stationary with aspect. Each target is in general characterized by multiple classes. A distinct set of Wiener filters are employed for each class of images, to identify the presence of target subcomponents. A Karhunen-Loeve representation is used to minimize the number of filters (templates) associated with a given subcomponent. The statistical relationships between the different target subcomponents are modeled via a hidden Markov tree (HMT). The HMT classifier is discussed and example results are presented for forward-looking-infrared (FLIR) imagery of several vehicles.