Image Segmentation: Principles, Techniques, and Applications

Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field

The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture.

Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors--such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression--to assist graduate students and researchers apply and improve image segmentation in their work.

• Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology.
• Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory.
• Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc.
• Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc.

Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.

影像分割

由該領域的領導者撰寫，總結並改進了當前影像分割方法的新理論、方法和應用

影像分割的過程根據像素的特徵將影像分成不同的區域，從而得到一個簡化的影像，可以更有效地進行分析。影像分割在許多領域中都有廣泛的應用，包括工業檢測、生物醫學、智能交通和建築等。

影像分割：原理、技術和應用是一本關於計算機視覺領域的最新技術和方法的集合。該書涵蓋了基本概念、新理論和方法，以及包括醫學影像、遙感、模糊聚類和分水嶺變換在內的各種實際應用。深入的章節介紹了作者們開發的創新方法，如卷積神經網絡、圖卷積網絡、可變形卷積和模型壓縮，以幫助研究生和研究人員在他們的工作中應用和改進影像分割。

• 描述了影像分割的基本原理和相關的數學方法，如聚類、神經網絡和數學形態學。
• 介紹了基於經典影像處理和機器學習理論的快速準確影像分割的新方法。
• 提出了改進的卷積神經網絡用於場景分割、物體識別和變化檢測等的技術。
• 強調了影像分割在交通影像分析、醫學影像分析、遙感應用和材料分析等各種應用場景中的影響。

影像分割：原理、技術和應用是本科和研究生課程的重要資源，如影像和視頻處理、計算機視覺和數字信號處理，同時也適用於從事計算機視覺和影像分析研究的研究人員，以改進他們的技術和方法。

Tao Lei, Professor, School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, China. His research interests include image processing, pattern recognition, and machine learning and he has authored and co-authored more than 100 research papers.

Asoke K. Nandi, Professor, Department of Electronic and Electrical Engineering, Brunel University London, UK. He is also Distinguished Visiting Professor, Xi'an Jiaotong University, China. Professor Nandi has authored over 600 technical publications, including 250280 journal papers as well as fourfive books.

陶磊，中國陜西科技大學電子信息與人工智能學院教授。他的研究興趣包括圖像處理、模式識別和機器學習，並且他已經撰寫和合著了100多篇研究論文。

Asoke K. Nandi，英國倫敦布魯內爾大學電子與電氣工程系教授。他還是中國西安交通大學的特聘訪問教授。Nandi教授已經撰寫了超過600篇技術論文，包括250280篇期刊論文和四五本書籍。