Venkatesan Rajinikanth – författare
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890 kr
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2 115 kr
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743 kr
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1 091 kr
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A Beginner’s Guide to Image Multi-Level Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement.
Explains basic concepts and the implementation of Image Multi-Level Thresholding (grayscale and RGB images)
Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcome
Describes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholding
Illustrates integration of the thresholding technique with bio-inspired algorithms
Includes current findings and future directions of image multi-level thresholding and its practical implementation
Emphasizes the need for multi-level thresholding with suitable examples
The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.
1 091 kr
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A Beginner’s Guide to Image Multi-Level Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement.
Explains basic concepts and the implementation of Image Multi-Level Thresholding (grayscale and RGB images)
Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcome
Describes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholding
Illustrates integration of the thresholding technique with bio-inspired algorithms
Includes current findings and future directions of image multi-level thresholding and its practical implementation
Emphasizes the need for multi-level thresholding with suitable examples
The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.
883 kr
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In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book:
Provides broad background on various image thresholding and segmentation techniques
Discusses information on various assessment metrics and the confusion matrix
Proposes integration of the thresholding technique with the bio-inspired algorithms
Explores case studies including MRI, CT, dermoscopy, and ultrasound images
Includes separate chapters on machine learning and deep learning for medical image processing
883 kr
Läs direkt efter köp
In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book:
Provides broad background on various image thresholding and segmentation techniques
Discusses information on various assessment metrics and the confusion matrix
Proposes integration of the thresholding technique with the bio-inspired algorithms
Explores case studies including MRI, CT, dermoscopy, and ultrasound images
Includes separate chapters on machine learning and deep learning for medical image processing
Metaheuristics and Optimization in Computer and Electrical Engineering
Volume 2: Hybrid and Improved Algorithms
1 728 kr
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2 281 kr
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This book discusses different methods of modifying the original metaheuristics and their application in computer and electrical engineering. As the race to develop advanced technology accelerates, a new era of "metaheuristics" has emerged. Through researched-based techniques and collaborative problem-solving, this book helps engineers to find efficient solutions to their engineering challenges. With the help of an expert guide and the collective knowledge of the engineering community, this comprehensive guide shows readers how to use machine learning and other AI techniques to reinvent smart engineering. From understanding the fundamentals to mastering the latest metaheuristics models, this guide provides with the skills and knowledge that need to stay ahead in the technology race. In the previous volume, authors focused on the application of original metaheuristics on electrical and computer sciences. This volume learns how AI and modified metaheuristics can be used to optimize algorithms and create more efficient electrical engineering designs. It gets insights on how data can be effectively processed and discover new techniques for creating sophisticated automation systems. It maximizes the potential of readers’ computer and electrical engineering projects with powerful metaheuristics and optimization techniques.
Metaheuristics and Optimization in Computer and Electrical Engineering
Volume 2: Hybrid and Improved Algorithms
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