http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
In this book, the latest achievements of the computation time analysis theory and practical applications of intelligent algorithms are set out. There are five chapters: (1) new method of intelligent algorithm computation time analysis; (2)Application of intelligent algorithms in computer vision; (3)Application of intelligent algorithms in logistics scheduling; (4)Application of intelligent algorithms in software testing; and (5) application of intelligent algorithm in multi-objective optimization. The content of each chapter is supported by papers published in top journals. The authors introduce the work of each part, which mainly includes a brief introduction (mainly for readers to understand) and academic discussion (rigorous theoretical and experimental support), in a vivid and interesting way through excellent pictures and literary compositions. To help readers learn and make progress together, each part of this book provides relevant literature, code, experimental data, and so on.- Integrates the theoretical analysis results of intelligent algorithms, which is convenient for the majority of researchers to deeply understand the theoretical analysis results of intelligent algorithms and further supplement and improve the theoretical research of intelligent algorithms- Opens up readers' understanding of the theoretical level of intelligent algorithms and spreads the inherent charm of intelligent algorithms- Integrates the diverse knowledge of society and provides a more comprehensive and scientific knowledge of intelligent algorithm theory.
Front Cover -- Intelligent Algorithms -- Copyright Page -- Contents -- About the authors -- Preface -- Introduction -- 1 Application of intelligent algorithms in the field of computer vision -- 1.1 Precise capture of translucent visual effects: sampling algorithm based on pixel-level multiobjective optimization -- 1.1.1 Overview of Research Progress -- 1.1.2 Scientific principles -- 1.1.2.1 Problem description -- 1.1.2.2 A multiobjective image matting algorithm based on pixel-level global sampling -- 1.1.2.2.1 Pixel-level discrete multiobjective sampling strategy
1.1.2.2.2 Fast discrete multiobjective optimization algorithm -- 1.1.2.3 Experimental results and discussion -- 1.1.2.3.1 Performance verification experiment of the pixel-level multiobjective global sampling algorithm -- 1.1.2.3.2 Performance verification experiment of the pixel-level multiobjective global sampling-based matting algorithm -- 1.1.3 Summary -- 1.2 Strong collaboration of fuzzy logic and evolutionary computing -- 1.2.1 Overview of research progress -- 1.2.2 Scientific principles -- 1.2.2.1 Problem description
1.2.2.2 Multiobjective collaborative optimization image matting algorithm based on fuzzy multicriteria evaluation and decom... -- 1.2.2.2.1 Fuzzy multicriteria pixel pair evaluation method -- 1.2.2.2.2 Multiobjective collaborative optimization algorithm based on decomposition -- 1.2.2.3 Experimental results and discussion -- 1.2.2.3.1 Multiobjective optimization algorithm selection experiment -- 1.2.2.3.2 Fuzzy multicriteria pixel pair evaluation accuracy experiment -- 1.2.2.3.3 Comparative experiment on optimization performance of multiobjective collaborative optimization algorithm based o...
1.2.2.3.4 Multiobjective collaborative optimization method for image matting based on fuzzy multicriteria evaluation and de... -- 1.2.3 Summary -- 1.3 Another masterpiece when medicine meets artificial intelligence -- 1.3.1 Overview of research progress -- 1.3.2 Scientific principles -- 1.3.2.1 Blood vessel segmentation algorithm based on hierarchical matting model -- 1.3.2.1.1 Image segmentation -- 1.3.2.1.2 Extraction of vascular skeleton -- 1.3.2.1.3 Hierarchical matting model -- 1.3.2.1.4 Stratifying the unknown pixels -- 1.3.2.1.5 Correlation function -- 1.3.2.1.6 Hierarchical update
1.3.2.2 Experimental analysis -- 1.3.3 Summary -- 1.4 "Deep learning+image matting enhancement" trial with great effectiveness: pedestrian classification in infrared images -- 1.4.1 Overview of research progress -- 1.4.2 Scientific principles -- 1.4.2.1 Pedestrian classification algorithm based on automatic image matting and enhancement for infrared images -- 1.4.2.2 Preprocessing algorithm for infrared pedestrian images based on automatic image matting -- 1.4.2.2.1 Automatic algorithm for generating pedestrian trimap in infrared images -- 1.4.2.2.2 Far-infrared pedestrian extraction
Includes bibliographical references and index.