Description:
Editorial Reviews
About the Author
Phillip Isola is Associate Professor of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
William T. Freeman is Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a research manager at Google Research in Cambridge, Massachusetts.
Reviews:
5.0 out of 5 stars Best book for CV and vision systems
Fantastic book for understand how images, vision, and computer vision works. The book should be read sequentially for best understanding but I prefer to always jump around as I’m always reading multiple books at once. It had some fantastic additions about data as well. Haven’t gotten to the more machine & deep learning systems yet but have heard great things
5.0 out of 5 stars Spectacular Book for Everyone.
This book is one of the first I have seen that both spends a lot of time on classical CV techniques and deep learning ones. In fact, people starting out in ML for the first time could honestly read this book instead of books devoted to ML. It's recent with all of the current developments, it's simple, and doesn't get too in the weeds, and I believe would be a great book for an introductory CV course in all universities.
5.0 out of 5 stars The book effectively bridges the gap left since the last major published textbook in the field
The field of computer vision has undergone rapid developments over the last decade, yet the most recent comprehensive textbooks such as "Computer Vision: Algorithms and Applications" by Szeliski, or Deep Learning by Ian Goodfellow (strictly speaking this is not a computer vision book) were published a decade ago. I immediately purchased this new book upon its release, particularly because of my admiration for the research work of one of its authors, Philip Isola. I personally have no connections with the authors but have read their publications and implement the methods in my work, especially in the image to image translation research field.My initial impression is that the book effectively bridges the gap left since the last major publication in the field. Although I haven't read the entire book yet, I read through several chapters the last several days that piqued my interest the most. The section on probabilistic models of images stood out to me as the highlight. I appreciate the author's progression from traditional pixel-based statistics through Gaussian models to today's cutting-edge Variational Autoencoders and Diffusion models. This coverage, especially of generative models, is unrivaled by any other textbook currently available.Another chapter that impressed me discusses radiance fields. This book possibly offers the best explanation of Neural Radiance Fields (NeRF) available, with particularly helpful illustrations that depict scenes in 2D 'flatland'. I do, however, hope for a future addition, perhaps an extra chapter that could explore 3D Gaussian Splatting in more depth.Overall, I highly recommend this book to graduate students and researchers in the field of computer vision. It offers extensive coverage of a wide range of topics and serves as an excellent introductory resource for those specializing in areas such as convolutional neural networks, generative models, or differential rendering.
5.0 out of 5 stars Great textbook
I have been using this as the main textbook for my computer vision class this semester and I am extremely happy! It tells a great story, is highly readable and is very easy to teach from. It's very up to date with the newest methods but also makes nice historical connections to older work, eg connecting Diffusion to the old Heeger and Bergen texture synthesis paper.Highly recommended.
1.0 out of 5 stars Poor quality binding.
Christmas gift disappointment. Poor quality of book binding and quality check should have seen that sections of pages weren't even bound to the book spine.
4.0 out of 5 stars Great Book Covering Classical to Modern Computer Vision
My initial thoughts of this book is that I love the vast variety of topics. I've only read the NeRF section so far and the content was great but it had a bunch of typos. All of the typos are just grammar issues so none of the content is incorrect. It just makes it hard to read in some parts. I'm looking forward to reading more of this book!
2.0 out of 5 stars Printing error?
Hardcover version: Missing content in Figure 1.1, page 2? That's pretty early to hit the first defect.
Excelente contenido
El contenido es muy bueno. Los temas van desde visión computacional clásica, hasta técnicas modernas basadas en aprendizaje profundo. Aborda los temas profundizando lo suficiente para comprender los conceptos involucrados.
Basic AI book
Good book for Basic AI
Nice book for Learning Computer Vision
I am presently teaching a course on Computer Vision, and this book covers almost all relevant topics along with recent advancements. Both classical and learning-based topics are covered well.
Conteúdo abrangente, excelente escolha de tópicos, ótimo acabamento
Antes de mais nada, gostaria de elogiar a entrega. Houve cuidado no transporte do produto, que chegou aqui dentro do prazo e sem avarias. Esse cuidado com a encomenda faz uma enorme diferença. Além disso, o livro é bonito e possui um ótimo acabamento. Conta com uma ampla e excelente escolha de tópicos, abrangendo desde os assuntos mais introdutórios até conceitos mais avançados. É riquíssimo em figuras e ilustrações, algo que considero um grande diferencial numa obra desta natureza. Estou satisfeito com esta aquisição e também recomendo a compra, sobretudo se você estiver interessado numa carreira acadêmica na área da inteligência computacional com foco em visão computacional. Eu ainda diria que vale a pena complementar este livro com outras obras influentes na área, incluindo "Deep Learning - Foundations and Concepts", de Christopher Bishop.
Damaged Item
Arrived damaged.
Visit the The MIT Press Store
Foundations of Computer Vision (Adaptive Computation and Machine Learning series)
AED61987
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Visit the The MIT Press Store
Foundations of Computer Vision (Adaptive Computation and Machine Learning series)
AED61987
Quantity:
Order today to get by 7-14 business days
This item qualifies for free delivery
Imported From: United States
At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.
BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.
Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.
If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.
Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.
All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.
While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.
Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.
BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.
All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.
All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.
Description:
Editorial Reviews
About the Author
Phillip Isola is Associate Professor of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
William T. Freeman is Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at MIT, where he is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a research manager at Google Research in Cambridge, Massachusetts.
Reviews:
5.0 out of 5 stars Best book for CV and vision systems
Fantastic book for understand how images, vision, and computer vision works. The book should be read sequentially for best understanding but I prefer to always jump around as I’m always reading multiple books at once. It had some fantastic additions about data as well. Haven’t gotten to the more machine & deep learning systems yet but have heard great things
5.0 out of 5 stars Spectacular Book for Everyone.
This book is one of the first I have seen that both spends a lot of time on classical CV techniques and deep learning ones. In fact, people starting out in ML for the first time could honestly read this book instead of books devoted to ML. It's recent with all of the current developments, it's simple, and doesn't get too in the weeds, and I believe would be a great book for an introductory CV course in all universities.
5.0 out of 5 stars The book effectively bridges the gap left since the last major published textbook in the field
The field of computer vision has undergone rapid developments over the last decade, yet the most recent comprehensive textbooks such as "Computer Vision: Algorithms and Applications" by Szeliski, or Deep Learning by Ian Goodfellow (strictly speaking this is not a computer vision book) were published a decade ago. I immediately purchased this new book upon its release, particularly because of my admiration for the research work of one of its authors, Philip Isola. I personally have no connections with the authors but have read their publications and implement the methods in my work, especially in the image to image translation research field.My initial impression is that the book effectively bridges the gap left since the last major publication in the field. Although I haven't read the entire book yet, I read through several chapters the last several days that piqued my interest the most. The section on probabilistic models of images stood out to me as the highlight. I appreciate the author's progression from traditional pixel-based statistics through Gaussian models to today's cutting-edge Variational Autoencoders and Diffusion models. This coverage, especially of generative models, is unrivaled by any other textbook currently available.Another chapter that impressed me discusses radiance fields. This book possibly offers the best explanation of Neural Radiance Fields (NeRF) available, with particularly helpful illustrations that depict scenes in 2D 'flatland'. I do, however, hope for a future addition, perhaps an extra chapter that could explore 3D Gaussian Splatting in more depth.Overall, I highly recommend this book to graduate students and researchers in the field of computer vision. It offers extensive coverage of a wide range of topics and serves as an excellent introductory resource for those specializing in areas such as convolutional neural networks, generative models, or differential rendering.
5.0 out of 5 stars Great textbook
I have been using this as the main textbook for my computer vision class this semester and I am extremely happy! It tells a great story, is highly readable and is very easy to teach from. It's very up to date with the newest methods but also makes nice historical connections to older work, eg connecting Diffusion to the old Heeger and Bergen texture synthesis paper.Highly recommended.
1.0 out of 5 stars Poor quality binding.
Christmas gift disappointment. Poor quality of book binding and quality check should have seen that sections of pages weren't even bound to the book spine.
4.0 out of 5 stars Great Book Covering Classical to Modern Computer Vision
My initial thoughts of this book is that I love the vast variety of topics. I've only read the NeRF section so far and the content was great but it had a bunch of typos. All of the typos are just grammar issues so none of the content is incorrect. It just makes it hard to read in some parts. I'm looking forward to reading more of this book!
2.0 out of 5 stars Printing error?
Hardcover version: Missing content in Figure 1.1, page 2? That's pretty early to hit the first defect.
Excelente contenido
El contenido es muy bueno. Los temas van desde visión computacional clásica, hasta técnicas modernas basadas en aprendizaje profundo. Aborda los temas profundizando lo suficiente para comprender los conceptos involucrados.
Basic AI book
Good book for Basic AI
Nice book for Learning Computer Vision
I am presently teaching a course on Computer Vision, and this book covers almost all relevant topics along with recent advancements. Both classical and learning-based topics are covered well.
Conteúdo abrangente, excelente escolha de tópicos, ótimo acabamento
Antes de mais nada, gostaria de elogiar a entrega. Houve cuidado no transporte do produto, que chegou aqui dentro do prazo e sem avarias. Esse cuidado com a encomenda faz uma enorme diferença. Além disso, o livro é bonito e possui um ótimo acabamento. Conta com uma ampla e excelente escolha de tópicos, abrangendo desde os assuntos mais introdutórios até conceitos mais avançados. É riquíssimo em figuras e ilustrações, algo que considero um grande diferencial numa obra desta natureza. Estou satisfeito com esta aquisição e também recomendo a compra, sobretudo se você estiver interessado numa carreira acadêmica na área da inteligência computacional com foco em visão computacional. Eu ainda diria que vale a pena complementar este livro com outras obras influentes na área, incluindo "Deep Learning - Foundations and Concepts", de Christopher Bishop.
Damaged Item
Arrived damaged.
Similar suggestions by Bolo
More from this brand
Similar items from “Intelligence & Semantics”
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