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In this paper, we propose a new depth super-resolution technique based on multiple dictionary learning. A novel dictionary selection method using basis pursuit is proposed to generate multiple dictionaries adaptively. A sparse representation of each low-resolution input patch is derived based on the learned dictionaries, and then used to reconstruct the corresponding high-resolution patch. Experimental...
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
Electroencephalogram (EEG) might be the most predictive and reliable physiological indicator of mental fatigue. However, the extraction of key features from massive EEG data for mental fatigue identification remains a challenge. The objective of this study is to identify the key EEG features in relationship to mental fatigue, from a broad pool of EEG features generated by quantitative EEG (qEEG) techniques,...
Measurement of wound healing status is very important for monitoring progress in individual patients. Tissue classification is a vital step in the development of an automatic measurement system for wound healing assessment. We present a new tissue classification protocol using the RGB (Red, Green and Blue) histogram distributions of pixel values from wound color images. These three histogram distributions...
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