Digital Image Processing and Image Acquisition | 69.34 MB
Title: Digital Image Processing and Image Acquisition
Author: Bernd Jähne
Category: Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Programming, Programming Languages, Application Software
Language: Chinese | 727 Pages | ISBN: 7302561680
Description:
Digital image processing plays a crucial role in facilitating efficient storage, transmission, manipulation, and retrieval of images. Python, renowned for its open-source nature and strong community support, offers a user-friendly platform with extensive image processing capabilities through libraries such as computer vision and scikit-learn.
This textbook serves as a practical guide to digital image processing using Python, presenting fundamental concepts, techniques, and algorithms with illustrative examples in Python. Each chapter begins with clear learning objectives and concludes with exercises and multiple-choice questions for self-assessment. Drawing from a diverse range of sources including research articles and books, the references provided at the end of each chapter encourage further exploration.
Tailored for undergraduate and postgraduate students, research scholars, engineers, and faculty specializing in image processing, it assumes a foundational understanding of set theory, matrix algebra, probability, and random variables.
Digital image processing plays a crucial role in facilitating efficient storage, transmission, manipulation, and retrieval of images. Python, renowned for its open-source nature and strong community support, offers a user-friendly platform with extensive image processing capabilities through libraries such as computer vision and scikit-learn.
This textbook serves as a practical guide to digital image processing using Python, presenting fundamental concepts, techniques, and algorithms with illustrative examples in Python. Each chapter begins with clear learning objectives and concludes with exercises and multiple-choice questions for self-assessment. Drawing from a diverse range of sources including research articles and books, the references provided at the end of each chapter encourage further exploration.
Tailored for undergraduate and postgraduate students, research scholars, engineers, and faculty specializing in image processing, it assumes a foundational understanding of set theory, matrix algebra, probability, and random variables.
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