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商品描述
In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.
Fuzzy Image Processing and Applications with MATLAB® presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few.
Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation.
Minimize Processing Errors Using Dynamic Fuzzy Set Theory
This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision—whether it is in the grey level of the image, geometry of an object, definition of an object’s edges or boundaries, or in knowledge representation, object recognition, or image interpretation.
The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.
商品描述(中文翻譯)
在與使用「清晰」數學的傳統影像分析方法相比，模糊集合技術提供了一個優雅的基礎和一套豐富的方法論，適用於各種影像處理任務。然而，對模糊處理的深入理解需要對基本原則和背景知識有堅實的掌握。
《Fuzzy Image Processing and Applications with MATLAB®》介紹了這個令人興奮且充滿活力的影像處理分支背後的整體科學和基本數學，這在遙感、醫學影像和視頻監控等應用領域變得越來越重要。
許多文本涵蓋了清晰集合的使用，但本書的獨特之處在於探討了對模糊集合影像處理的興趣激增和顯著增長。著名的作者清楚地闡述了模糊集合理論的理論概念和應用，以及它們在增強、分割、濾波、邊緣檢測、基於內容的影像檢索、模式識別和聚類等領域的影響。他們詳細描述了模糊的所有組成部分，包括預處理、閾值檢測和基於匹配的分割。
**最小化處理錯誤使用動態模糊集合理論**
本書作為MATLAB的入門書，展示了如何在模糊影像處理方法中實施它。它說明了如何使用代碼來改善計算，以幫助防止或處理不精確性——無論是在影像的灰階、物體的幾何形狀、物體邊緣或邊界的定義，或是在知識表示、物體識別或影像解釋中。
本書通過將模糊集合理論應用於影像閾值處理、分割、邊緣檢測、增強、聚類、顏色檢索、模式識別中的聚類及其他影像處理操作來解決這些考量。作者突出了關鍵思想，展示了他們自己新模糊方法的實驗結果以及不同作者提出的方法，提供了對教師、科學家和工程師等人有用的數據和見解。
