T S Murugesh – författare
866 kr
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The book provides a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives are to offer an overview to the elementary concepts and practices appropriate to digital image processing as well as to provide theoretical exposition. It starts with an expanded coverage of the fundamentals to provide a more comprehensive and cohesive coverage of the topics including but not limited to:
Applications and tools for image processing, and fundamentals with several implementation examples Concepts of image formation OpenCV installation with step-by-step screen shots Concepts behind intensity, brightness and contrast, color models Ways by which noises are created in an image and the possible remedial measures Edge detection, image segmentation, classification, regression, classification algorithms Importance of frequency domain in image processing field Relevant code snippets and the MATLAB® codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understanding
The video lectures for specific topics through YouTube enable easy inference for the readers to apply the learnt theory into practice. The addition of contents at the end of each chapter such as quizzes and review questions fully prepare the readers for further study.
Graduate students, post graduate students, researchers, and anyone in general interested in image processing, computer vision, machine learning domains etc. can find this book an excellent starting point for information and an able ally.
866 kr
Läs direkt efter köp
The book provides a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives are to offer an overview to the elementary concepts and practices appropriate to digital image processing as well as to provide theoretical exposition. It starts with an expanded coverage of the fundamentals to provide a more comprehensive and cohesive coverage of the topics including but not limited to:
Applications and tools for image processing, and fundamentals with several implementation examples Concepts of image formation OpenCV installation with step-by-step screen shots Concepts behind intensity, brightness and contrast, color models Ways by which noises are created in an image and the possible remedial measures Edge detection, image segmentation, classification, regression, classification algorithms Importance of frequency domain in image processing field Relevant code snippets and the MATLAB® codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understanding
The video lectures for specific topics through YouTube enable easy inference for the readers to apply the learnt theory into practice. The addition of contents at the end of each chapter such as quizzes and review questions fully prepare the readers for further study.
Graduate students, post graduate students, researchers, and anyone in general interested in image processing, computer vision, machine learning domains etc. can find this book an excellent starting point for information and an able ally.
2 494 kr
Skickas inom 10-15 vardagar
730 kr
Skickas inom 10-15 vardagar
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Skickas inom 10-15 vardagar
788 kr
Skickas inom 10-15 vardagar
935 kr
Läs direkt efter köp
Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.
935 kr
Läs direkt efter köp
Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.