Information Theory in Image Processing and Pattern Recognition

Dear Colleagues,

Information theory (IT) explores techniques to quantify, store and communicate information.  The use of IT in image processing and pattern recognition covers theoretical and applied aspects involving the transmission, storage, and processing of information in different fields, such as computer science, biology, mathematics, chemistry, physics, engineering, medicine, and others. In this context, analysis based on entropy has made relevant advances, especially for the development of computer-aided detection (CADe) and computer-aided diagnosis (CADx), with new insights and approaches into the different processes of segmentation, feature extraction, feature selection, classification, representation learning, deep learning, learning deep features, and others. Thus, this Special Issue provides a forum for discussing challenging topics in information theory in image processing and pattern recognition, with new insights, theories, methods and approaches, and applications. Some issues of interest include, but are not limited to, the following:

• IT in image processing and pattern recognition, considering CADe and CADx, with segmentation, texture analysis, feature analysis, classification and interpretation, exploring and applying the entropy concepts;
• Multiscale and multidimensional approaches with entropy concepts;
• Computer vision and machine learning devoted to CADe and CADx, exploring entropy issues in deep learning, representation learning, cooperative learning for multi-view analysis, learning deep features, and ensembles;
• Analysis based on explainable artificial intelligence with entropy.

Prof. Dr. Leandro Alves Neves
Prof. Dr. Marcelo Zanchetta do Nascimento
Dr. Thaína Aparecida Azevedo Tosta
Guest Editors

Manuscript Submission Information

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