{"product_id":"digital-and-discrete-geometry-theory-and-algorithms-modern-methods","title":"Digital and Discrete Geometry: Theory and Algorithms - Modern methods","description":"\u003cp\u003eIn this review of Digital and Discrete Geometry: Theory and Algorithms the bottom line is clear: this is a methodical, research-oriented textbook for readers who need rigorous, algorithmic treatments of geometry for computing. The book is best for graduate students, researchers and engineers who work on geometric processing, manifold learning or discrete representations because it consolidates constructive methods, digital geometry foundations and concrete algorithms in one place. The review highlights thorough coverage of both foundational topics and cross-disciplinary applications in image processing and computer vision.\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eComprehensive coverage:\u003c\/strong\u003e The book presents a full tour from basic geometry to advanced topics so readers can follow modern methods used in computing sciences.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDigital curves and surfaces:\u003c\/strong\u003e Detailed sections on digital curves, surfaces and manifolds explain discrete representations and practical algorithms for processing.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAlgorithm focus:\u003c\/strong\u003e Concrete algorithms and constructive methods are provided so practitioners can apply theory to implementable solutions.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCross-disciplinary applications:\u003c\/strong\u003e Chapters link geometric methods to image processing, computer vision and computer graphics for real-world relevance.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eData science connections:\u003c\/strong\u003e Coverage of manifold learning, R-tree structures and cloud data shows how geometric ideas scale to BigData and wireless networks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho It's For\u003c\/h2\u003e\n\u003cp\u003eGraduate students and researchers in computer science, computational geometry or machine learning will find the book most valuable because it combines theoretical depth with algorithmic detail. Practitioners building systems that require discrete geometric processing, such as 3D reconstruction, image analysis or spatial indexing, will appreciate the explicit methods and references.\u003c\/p\u003e\n\u003cp\u003eReaders looking for an introductory, hand-holding textbook or a short tutorial on applied machine learning should look elsewhere, since this volume emphasizes theory and constructive algorithms over gentle introductions and does not serve as a broad primer for general data science beginners.\u003c\/p\u003e\n\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThorough presentation of both digital and discrete geometry fundamentals supports advanced study and research.\u003c\/li\u003e\n\u003cli\u003ePractical algorithms and constructive methods make the material useful for implementers, not just theorists.\u003c\/li\u003e\n\u003cli\u003eExplicit links to applications in image processing, computer vision and cloud data help connect theory to practice.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe book is dense and assumes background knowledge, so it can be challenging for readers without prior geometry or topology experience.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eDigital and Discrete Geometry: Theory and Algorithms\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthor\u003c\/td\u003e\n\u003ctd\u003eLi M. Chen\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCoverage\u003c\/td\u003e\n\u003ctd\u003eBasic geometry, digital curves, surfaces, manifolds, discretely represented objects\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTopics included\u003c\/td\u003e\n\u003ctd\u003eGeometric computation, processing, manifold learning, R-tree and cloud data\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eApplications\u003c\/td\u003e\n\u003ctd\u003eImage processing, computer vision, computer graphics, algebraic topology\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntended audience\u003c\/td\u003e\n\u003ctd\u003eResearchers, graduate students, engineers in computing sciences\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch2\u003eOur Verdict\u003c\/h2\u003e\n\u003cp\u003eDigital and Discrete Geometry is a solid, research-minded textbook that earns a recommendation for anyone needing algorithmic foundations and discrete methods in geometric computing. It delivers substantial value for specialists and implementers who want to bridge theory and application, though newcomers should be prepared for a steep learning curve.\u003c\/p\u003e\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDoes this book include practical algorithms?\u003c\/strong\u003e\u003cbr\u003eYes. The author investigates constructive methods and presents detailed algorithms suitable for implementation in geometric processing tasks.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs it suitable for beginners?\u003c\/strong\u003e\u003cbr\u003eThe book assumes prior knowledge in geometry or topology and is better suited to graduate-level readers rather than complete beginners.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat application areas does it cover?\u003c\/strong\u003e\u003cbr\u003eIt covers image processing, computer vision, computer graphics, manifold learning, cloud data and spatial indexing like R-tree for networked data.\u003c\/p\u003e","brand":"Li M. 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