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Format: 1 online resource (253 pages)

Edition: First edition.

ISBN: 9780443217593

Note: 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 -- 1.4.2.2.3 Pedestrian classification algorithm based on depth infrared images using alpha mattes for segmentation -- 1.4.2.3 Experimental results and discussion -- 1.4.2.3.1 Classification performance verification experiment of pedestrian classification algorithm based on automatic imag... -- 1.4.2.3.2 Analysis of the performance improvement of pedestrian classification based on infrared image preprocessing using ... -- 1.4.2.3.3 Analysis of the impact of infrared image pedestrian preprocessing based on automatic image matting on the perform... -- 1.4.2.3.4 Comparison experiment on preprocessing effect of pedestrian detection in infrared images based on automatic image... -- 1.4.2.3.5 Limitations of infrared image pedestrian classification method based on automatic image matting -- 1.4.3 Summary -- References. , 2 Application of intelligent algorithms in the field of logistics planning -- 2.1 Overview of research progress -- 2.2 Scientific principles -- 2.2.1 Problem description -- 2.2.2 Problem analysis -- 2.2.3 Algorithm design -- 2.2.4 Algorithm analysis -- 2.2.5 Experimental analysis -- 2.3 Summary -- References -- 3 Application of intelligent algorithms in the field of software testing -- 3.1 How to solve excessive overhead of software testing-starting with the automated test case generation problem -- 3.1.1 Overview of research progress -- 3.1.2 Scientific principle -- 3.1.2.1 Problem description -- 3.1.2.2 Differential evolution based on test-case-path relationship matrix for automated test case generation -- 3.1.2.3 Experimental results and discussion -- 3.1.3 Summary -- 3.2 Effective natural language processing programs testing by random heuristic algorithm and scatter search strategy -- 3.2.1 Overview of research progress -- 3.2.2 Scientific principles -- 3.2.2.1 Problem description -- 3.2.2.2 Automated path coverage test case generation based on random heuristic algorithm with scatter search strategy -- 3.2.2.3 Experimental results and discussion -- 3.2.3 Summary -- References -- 4 Application of multiobjective optimization intelligence algorithms -- 4.1 Many-objective evolutionary algorithm based on pareto-adaptive reference points -- 4.1.1 Overview of research progress -- 4.1.2 Scientific principles -- 4.1.2.1 General framework -- 4.1.2.2 Environmental selection -- 4.1.2.2.1 Adaptive normalization -- 4.1.2.2.2 Estimation of shapes and update of the reference point -- 4.1.2.2.3 Fitness assignment -- 4.1.2.2.4 Classification by a hypercube -- 4.1.2.2.5 Select solutions one by one -- 4.1.2.3 Experimental analysis -- 4.1.2.4 Experimental settings -- 4.1.2.4.1 Population size and termination condition -- 4.1.2.4.2 Parameter settings. , 4.1.2.5 Experimental results on DTLZ test problems -- 4.1.2.6 Empirical findings for WFG and WFG−1 test problems -- 4.1.3 Summary -- 4.2 A powerful approach to configure software products- -- 4.2.1 Survey of advancements in research -- 4.2.2 Fundamental scientific concepts -- 4.2.2.1 Problem statements -- 4.2.2.1.1 The OSPS problems with soft constraints -- 4.2.2.2 MOEAs based on estimation of distribution -- 4.2.2.3 Integrating EoD into decomposition-based MOEAs -- 4.2.2.3.1 Normalization and estimation of ideal/nadir points -- 4.2.2.3.2 Reproduction operators -- 4.2.2.3.3 Updating subproblems -- 4.2.2.4 A new repair operator for the OSPS problem -- 4.2.2.5 Handling soft constraints -- 4.2.2.6 Experimental studies -- 4.2.2.6.1 Experimental settings -- 4.2.2.6.2 Performancee metric -- 4.2.2.6.3 Results on two-objective OSPS instances -- 4.2.2.6.4 Results on three-objective OSPS instances -- 4.2.2.6.5 Results in four-objective OSPS instances -- 4.2.2.7 Further discussions -- 4.2.3 Summary -- References -- 5 A new approach to intelligent algorithms for running time complexity analysis -- 5.1 Overview of research progress -- 5.1.1 Running time of (1,λ) evolutionary strategy -- 5.1.2 Running time of the evolutionary strategy and the covariance matrix adaptation evolutionary strategy -- 5.1.3 Running time of the improved covariance matrix adaptation evolutionary strategy -- 5.2 Scientific principle -- 5.2.1 Problem description -- 5.2.2 Problem modeling -- 5.2.3 Problem analysis -- 5.2.4 Estimation method -- 5.2.5 Experimental analysis: simulation of gain probability density distribution function -- 5.2.6 Experimental analysis: fitting of the average gain surface -- 5.3 Summary -- References -- Index -- Back Cover.

Additional Edition: Print version: Huang, Han Intelligent Algorithms San Diego : Elsevier,c2024 ISBN 9780443217586

Language: English