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Item #:
125561724
Foundations of Computer Vision (Adaptive Computation and Machine Learning series)
Item #:
125561724
ETB 17152
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What Stands Out
Comprehensive Coverage
This book provides an exhaustive overview of computer vision principles, ensuring that readers grasp both foundational and advanced topics effectively.
Practical Applications
With a strong focus on real-world applications, it equips readers with the necessary tools to implement vision systems in various industries.
Expert Insights
Authored by leading experts in the field, the text offers unique perspectives and cutting-edge research that enhances the learning experience for technology enthusiasts.
Product Details
| Publisher | The MIT Press |
| Publication date | April 16, 2024 |
| Language | English |
| Print length | 840 pages |
| ISBN-10 | 0262048973 |
| ISBN-13 | 978-0262048972 |
| Item Weight | 3.24 pounds (1.47 kg) |
| Dimensions | 8.27 x 1.42 x 9.29 inches (21 x 3.6 x 23.6 cm) |
Who Should Buy?
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Students
Ideal for computer science or engineering students seeking foundational knowledge in computer vision and machine learning techniques.
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Professionals
Suitable for industry professionals wanting to enhance their understanding of computer vision applications and algorithm development.
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Researchers
Perfect for researchers exploring the theoretical underpinnings and practical implementations of computer vision technologies and methodologies.
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Beginners
Not suitable for absolute beginners lacking prior knowledge of programming or machine learning concepts, as it assumes familiarity.
Product Description
Foundations of Computer Vision (Adaptive Computation and Machine Learning series)
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Intelligence & Semantics Editorial Review
**** "Foundations of Computer Vision" emerges as a noteworthy addition to the realm of computer vision literature, attracting commendation for its thorough exploration of both classical techniques and the latest deep learning innovations. Readers have praised the book for its clarity and structured approach, suggesting that it should be read sequentially for optimum understanding, though some prefer a more non-linear reading style to suit their diverse learning habits. This balance between theory and practical insights is seen as particularly beneficial for newcomers to machine learning, positioning the book as a prime candidate for introductory computer vision courses in universities. The text makes significant strides in covering rapid advancements in the field that have transpired over the past decade. Notably, the author’s treatment of topics like probabilistic modeling and generative models is highlighted, especially the section on Neural Radiance Fields (NeRF), which is described as possibly the best explanation currently available. Despite some minor criticisms regarding typographical errors, readers express enthusiasm about the content’s depth and usability for teaching. The book's historical context and connections to older works, such as linking Diffusion models to classic texture synthesis concepts, further enrich the reader's experience. Overall, "Foundations of Computer Vision" is highly recommended for graduate students and researchers. Its comprehensive coverage, engaging narrative, and up-to-date methodologies make it an essential resource for those delving into cutting-edge topics within computer vision, including convolutional neural networks and differential rendering. **
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Pros
- Comprehensive coverage of classical and deep learning computer vision techniques
- Highly readable and structured for teaching
- Engaging narrative with historical context
- Excellent explanations of complex topics like Neural Radiance Fields
- Recommended for both beginners and advanced learners
Cons
- Occasional typographical errors that can disrupt reading flow
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