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This work targets people identification in video based on the way they walk (i.e. gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the...
Passive Millimeter Wave Images (PMMWI) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render difficult this task. In this paper we propose a method that combines image processing and statistical machine learning techniques to solve this localization/detection problem...
In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p < 1, lp-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between...
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