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5 Best books on Computer Vision
May 21, 2023 | Author: Maria Lin
Here is my list of 5 most interesting books about Computer Vision:
1. Computer Vision: Algorithms and Applications
The book serves as an excellent introductory resource for various important sub-fields within computer vision. It stands out as one of the more recent publications that delve into recent advancements. However, due to its broad coverage and high-level approach, it may not be suitable as a standalone learning material, lacking practical implementation details and problem-solving guidance. In my opinion, the book is best utilized by skimming through it to familiarize oneself with key concepts and using the references provided as a starting point for further exploration. Overall, the writing quality is commendable, offering clarity and insightful perspectives. There are instances where the book seems to reiterate ideas from highly cited papers authored by others.
2. Deep Learning for Computer Vision
This book is highly recommended for beginners in the field of deep learning who want to learn computer vision (CV) techniques. The author does an excellent job of explaining these techniques; however, there is a lack of practical implementation in several areas. While the book raises awareness about various topics, it would have been beneficial to have sample code accompanying each concept to facilitate a better understanding. Despite mentioning OpenCV as a useful tool, the book fails to provide guidance on using it with video. Moreover, the code in the book resembles a challenging scenario where you inherit source code from a former employee who did not include any comments. Entire models are presented without any explanation of their functionality or a step-by-step breakdown of how they work.
3. Computer Vision with the OpenCV Library
Despite the advancements and abundance of online documentation and newer books on OpenCV in the past five years, this particular guide remains the most valuable and beneficial resource. No other book offers the same comprehensive coverage and depth as this one. Although the writing style can be overly casual at times, it has slightly bothered me due to the authors' nonchalant treatment of certain crucial details that I find challenging. Nevertheless, there is simply no superior alternative, nor anything that comes close. I suppose I should appreciate that it doesn't suffer from excessive and incomprehensible formality. Once again, this remains the top reference for OpenCV currently available.
4. Multiple View Geometry in Computer Vision
This book provides an extensive and rigorous explanation of the underlying theory. It begins by covering fundamental concepts in 2D and 3D geometry. The author then proceeds to elucidate the principles of single-view geometry, illustrating how cameras map images in 3D space onto 2D images. Two-view geometry is subsequently explored, delving into the epipolar geometry between two cameras and projective reconstruction based on image correspondences. Part three of the book extends these concepts to three cameras, introducing the concept of trifocal geometry. The final section of the book extends the algorithms discussed to scenarios involving N views. Despite its simple and straightforward structure, this book tackles complex material, offering a comprehensive understanding of the subject matter.
5. OpenCV with Python By Example
This book can be likened to fast food, offering numerous applications and code examples but providing limited explanations. It attempts to cover a wide range of topics without delving into detailed explanations. While the author may possess extensive knowledge and skills in OpenCV as a programmer, their intention seems to be more about presenting code samples rather than guiding readers in learning OpenCV from scratch. It feels as if the author simply throws the code samples at you, suggesting that you follow them without providing sufficient context. Some understanding of Python is required to comprehend the content. However, the book shines in its emphasis on intuitive explanations and the inclusion of real-life examples, which are its standout features.
See also: Top 10 eBook Organizers
1. Computer Vision: Algorithms and Applications
The book serves as an excellent introductory resource for various important sub-fields within computer vision. It stands out as one of the more recent publications that delve into recent advancements. However, due to its broad coverage and high-level approach, it may not be suitable as a standalone learning material, lacking practical implementation details and problem-solving guidance. In my opinion, the book is best utilized by skimming through it to familiarize oneself with key concepts and using the references provided as a starting point for further exploration. Overall, the writing quality is commendable, offering clarity and insightful perspectives. There are instances where the book seems to reiterate ideas from highly cited papers authored by others.
2. Deep Learning for Computer Vision
This book is highly recommended for beginners in the field of deep learning who want to learn computer vision (CV) techniques. The author does an excellent job of explaining these techniques; however, there is a lack of practical implementation in several areas. While the book raises awareness about various topics, it would have been beneficial to have sample code accompanying each concept to facilitate a better understanding. Despite mentioning OpenCV as a useful tool, the book fails to provide guidance on using it with video. Moreover, the code in the book resembles a challenging scenario where you inherit source code from a former employee who did not include any comments. Entire models are presented without any explanation of their functionality or a step-by-step breakdown of how they work.
3. Computer Vision with the OpenCV Library
Despite the advancements and abundance of online documentation and newer books on OpenCV in the past five years, this particular guide remains the most valuable and beneficial resource. No other book offers the same comprehensive coverage and depth as this one. Although the writing style can be overly casual at times, it has slightly bothered me due to the authors' nonchalant treatment of certain crucial details that I find challenging. Nevertheless, there is simply no superior alternative, nor anything that comes close. I suppose I should appreciate that it doesn't suffer from excessive and incomprehensible formality. Once again, this remains the top reference for OpenCV currently available.
4. Multiple View Geometry in Computer Vision
This book provides an extensive and rigorous explanation of the underlying theory. It begins by covering fundamental concepts in 2D and 3D geometry. The author then proceeds to elucidate the principles of single-view geometry, illustrating how cameras map images in 3D space onto 2D images. Two-view geometry is subsequently explored, delving into the epipolar geometry between two cameras and projective reconstruction based on image correspondences. Part three of the book extends these concepts to three cameras, introducing the concept of trifocal geometry. The final section of the book extends the algorithms discussed to scenarios involving N views. Despite its simple and straightforward structure, this book tackles complex material, offering a comprehensive understanding of the subject matter.
5. OpenCV with Python By Example
This book can be likened to fast food, offering numerous applications and code examples but providing limited explanations. It attempts to cover a wide range of topics without delving into detailed explanations. While the author may possess extensive knowledge and skills in OpenCV as a programmer, their intention seems to be more about presenting code samples rather than guiding readers in learning OpenCV from scratch. It feels as if the author simply throws the code samples at you, suggesting that you follow them without providing sufficient context. Some understanding of Python is required to comprehend the content. However, the book shines in its emphasis on intuitive explanations and the inclusion of real-life examples, which are its standout features.
See also: Top 10 eBook Organizers
