Concise Computer Vision: An Introdu...
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"Many textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the …
Book Summary
Concise Computer Vision by Reinhard Klette offers a streamlined introduction to the fundamentals of computer vision. Designed for a third- or fourth-year undergraduate course, it covers essential algorithms and mathematical concepts without overwhelming detail. Each chapter includes classroom-tested programming exercises and review questions, making it an ideal textbook for students seeking a practical, accessible overview of this complex field.
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About Concise Computer Vision: An Introdu...
Many computer vision textbooks are dense and intimidating, but Concise Computer Vision provides a focused, accessible alternative. Aimed at third- and fourth-year undergraduates in computer science or engineering, this textbook distills the core topics of computer vision into a manageable format. It emphasizes the role of key algorithms and mathematical concepts, ensuring students grasp the fundamentals without getting lost in excessive detail. The book is classroom-tested and includes programming exercises and review questions at the end of each chapter, reinforcing learning through practice. Whether used in a course or for self-study, this concise guide offers a solid foundation in computer vision, preparing readers for more advanced study or practical application in the field.
Who should read Concise Computer Vision: An Introdu...?
This book is ideal for undergraduate students in computer science or engineering taking an introductory computer vision course. It suits instructors seeking a concise, classroom-ready textbook with exercises. Self-learners with basic programming and math background will find it accessible. Professionals needing a quick refresher on fundamentals will benefit. It's not for advanced researchers or those seeking in-depth coverage of specialized topics.
Pros & Cons
Pros
- ✓ Concise and accessible, avoiding overwhelming detail
- ✓ Classroom-tested with programming exercises and review questions
- ✓ Covers essential algorithms and mathematical concepts
- ✓ Ideal for a one-semester introductory course
- ✓ Written by an experienced author in the field
Cons
- ✗ May lack depth for advanced study or research
- ✗ Limited coverage of cutting-edge deep learning techniques
- ✗ Assumes prior knowledge of programming and linear algebra
- ✗ Not suitable for complete beginners without math background
- ✗ Few real-world case studies or applications
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Frequently asked questions
What prior knowledge is required?
Readers should have basic programming skills (e.g., Python or C++) and familiarity with linear algebra and calculus.
Does the book cover deep learning?
It focuses on fundamental algorithms and mathematical concepts, with limited coverage of deep learning, which is better suited for advanced texts.
Are there programming exercises?
Yes, each chapter includes classroom-tested programming exercises to reinforce concepts.
Is this suitable for self-study?
Yes, with its concise explanations and review questions, it works well for self-learners with the necessary background.
What level is this book aimed at?
It is designed for third- or fourth-year undergraduate students in computer science or engineering.
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