Computational Intelligence For Modelling Control Automation Intelligent Image Processing Data Analysis Information Retrieval
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Intelligent Image Processing, Data Analysis & Information Retrieval
This edited volume is dedicated to the theory and applications of Computational Intelligence techniques for Intelligent Image Processing, Data Analysis and Information Retrieval. It consists of 52 accepted research papers from the 1999 International Conference on Computational Intelligence for Modeling, Control and Automation - CIMCA'99. The goal of this conference was to provide a medium for the exchange of ideas between theoreticians and practitioners to address the important issues in computational intelligence for modelling, control and automation. The research papers presented in this book cover new techniques and applications in the of Image Processing, Computer Vision, Multimedia Systems, Filtering, Classification, Data Analysis, Prediction, Intelligent Database and Information Retrievals.
Computational Intelligence for Modelling, Control & Automation
This edited Book is dedicated to the theory and applications of Evolutionary Computation and Fuzzy Logic for Intelligent Control, Knowledge Acquisition and Information Retrieval. The book consists of 86 selected research papers from the 1999 International Conference on Computational Intelligence for Modelling, Control and Automation - CIMCA'99 The research papers presented in this book cover new techniques and applications in the following research areas: Evolutionary Computation, Fuzzy Logic and Expert Systems with their applications for Optimisation, Learning, Control, Scheduling and Multi-Criteria Analysis as well as Reliability Assessment, Information Retrieval and Knowledge Acquisition.
Deep Learning for Image Processing Applications
Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.