Here are 66 books that Multiple View Geometry in Computer Vision fans have personally recommended if you like
Multiple View Geometry in Computer Vision.
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It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.
David Marr shaped the field of computer vision in its early days. His seminal book laid the structure for interpreting images and one which is still largely followed. He popularised notions of the primal sketch and his work on edge detection led to one of the most sophisticated approaches. His work and influence continue to endure despite his early death: we missed and miss him a lot.
Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions.
David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This…
It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.
This fine book is about learning the relationships between what is seen in an image, and what is known about the world. It’s a counterpart to our book on feature extraction and it shows you what can be achieved with the features. It’s not for those who shy from maths, as is the case for all of the books here. So that you can build the techniques, Simon’s book also includes a wide variety of algorithms to help you on your way.
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build…
It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.
Richard’s authoritative leading textbook excellently describes the whole field of computer vision. It starts with the sensor, moves to image formation followed by feature extraction and grouping, and then by vision analysis. It’s pragmatic too, with excellent descriptions of applications. And there is a ton of support material. This is a mega textbook describing the whole field of computer vision.
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are…
It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.
The advances of deep learning have been awesome, and fast. It’s been hard for the textbooks to keep up, so it’s good to include one that describes the advances and state of art very well. It seems appropriate that it’s edited by two leading researchers who are Roy – who described computer vision systems implementations in a long series of excellent books – and Matt, whose work on face recognition revolutionised and transformed the progress of face recognition in the 1990s. This book gives you an image of where we are now in computer vision, and where we are going.
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection.
This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as…
My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
Bishop’s book laid the mathematical groundwork for me, making it a solid foundation for anyone venturing into Generative AI.
I love how it covers Bayesian inference, graphical models, and machine learning fundamentals in a clear, approachable way. I also think, in my personal opinion, that reading my book after this one would be a natural progression to understand where AI is heading, building on the core concepts that Bishop established.
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models…
I’m a historian of Southern Africa who is fascinated by questions of visibility and invisibility. I love probing beneath the surface of the past. For example, why is thisperson famous and renowned, butthatperson isn’t? To me, recognition and reputation are interesting to scrutinize as social categories in their own right, rather than as factual statements. I’ve written two books focusing on the history of religious expression in Southern Africa, and my most recent book is a biography of the forgotten South African writer and politician Regina Gelana Twala.
This anthology of African women writers has been my personal lodestar in writing about Regina Twala, a forgotten African writer.
Busby (a pioneering editor and publisher of Ghanaian heritage) was one of the first to recognize that the canon of African writers was much bigger than famous men like Chinua Achebe and Wole Soyinka.
Her work taught me about a longstanding rich female literary tradition on the African continent – some of her earliest examples of women writers date to Ancient Egypt!
Busby recognizes that we can’t always look to the written page for evidence of this, given that many women writers were denied opportunities to publish their work.
So she broadens the focus of her anthology by paying attention to both “wordsandwriting,” thinking about female writers of novels, poetry, plays, non-fiction, and journalism.
Three decades after her pioneering anthology, Daughters of Africa, Margaret Busby curates an extraordinary collection of contemporary writing by 200 women writers of African descent, including Zadie Smith, Bernardine Evaristo and Chimamanda Ngozi Adichie.
A glorious portrayal of the richness and range of African women's voices, this major international book brings together their achievements across a wealth of genres. From Antigua to Zimbabwe and Angola to the USA, overlooked artists of the past join key figures, popular contemporaries and emerging writers in paying tribute to the heritage that unites them, the strong links that endure from generation to generation, and…
I’m the Head of Trend and Innovation Scouting for Nokia, and I’ve been with the company since the glory days of Nokia mobile phone world dominance. I know first-hand what happens when a company focuses exclusively on the technology, not the humans that use it, and how quickly that can lead to disaster. One of the lessons that I see repeated continuously in the field of innovation is that a huge amount of attention gets paid to the new technology, and not nearly enough on how the technology will interact with our existing systems, beliefs, attitudes, and culture. Learning from the mistakes is the best way to make sure that the future doesn’t repeat them!
While the term the “Metaverse” usually makes people think of a fully digital, immersive world, my own feeling is that technologies that bring digital information and entertainment into our physical world is a much more powerful and important arena. This leads us to the transformative and still-developing world of Augmented Reality.
David Rose of the MIT Media Lab has been working with Augmented Reality for more than a decade, and Supersight is an overview of what he's seen and what he’s learned in this time.
What I love about Supersight is that while David is clearly as excited about this topic as I am, he’s also a realist, and openly discusses issues and challenges with Augmented Reality. Perhaps most valuable are the 14 Augmented Reality Design Principles that he outlines – super realistic, super useful.
After reading this, you’ll have a very grounded idea of the capabilities and potential of…
For thousands of years, human vision has been largely unchanged by evolution.
We’re about to get a software update.
Today, Apple, Google, Microsoft, Facebook, Snap, Samsung, and a host of startups are racing to radically change the way we see. The building blocks are already falling into place: cloud computing and 5G networks, AI computer vision algorithms, smart glasses and VR headsets, and mixed reality games like Pokémon GO. But what’s coming next is a fundamental shift in how we experience the world and interact…
Saying just the right words in just the right way can cause a box of electronics to behave however you want it to behave… that’s an idea that has captivated me ever since I first played around with a computer at Radio Shack back in 1979. I’m always on the lookout for compelling ways to convey the topic to people who are open-minded, but maybe turned off by things that are overly technical. I teach computer science and study artificial intelligence as a way of expanding what we can get computers to do on our behalf.
The fields of Psychology, Economics, and Biology are well-known for offering interesting and informative introductory courses that provide a doorway into the area for budding scientists but also essential background knowledge appropriate for any educated person.
In Computer Science, we don't really do things that way. I wanted to offer a new kind of Computer Science introductory course that laid out the coolest ideas we have to offer along with compelling descriptions of why they matter.
I ended up using this book as the required reading in the class I built because it tells a personal, moving story while taking the reader from the nuts and bolts of bits and bytes all the way up to cutting-edge ideas surrounding artificial intelligence. It's a great read! Plus, it's short so I thought I could get my students to actually finish it.
Most people are baffled by how computers work and assume that they will never understand them. What they don't realize,and what Daniel Hillis's short book brilliantly demonstrates,is that computers'seemingly complex operations can be broken down into a few simple parts that perform the same simple procedures over and over again. Computer wizard Hillis offers an easy-to-follow explanation of how data is processed that makes the operations of a computer seem as straightforward as those of a bicycle.Avoiding technobabble or discussions of advanced hardware, the lucid explanations and colourful anecdotes in The Pattern on the Stone go straight to the heart…
I have been working with computers for decades now – having started with programmable handheld calculators and working my way up and down through mainframes, mini- and micro-computers. I always thought there is an art to writing software, and that good software can be read and admired. Maintainability, readability, and testability are some core needs for software, and after going through many programming paradigms, I feel that functional programming (FP) is the way to go – and several modern web frameworks agree. JavaScript (and now, TypeScript) are essential to web development, and I wanted to show how FP could be successfully used with those languages, and thus my book.
This thin but quite fulfilling book is a compact yet profound exploration of proven program correctness, probably one of the first texts to focus on that topic.
Dijkstra's emphasis on mathematical rigor and disciplined thinking reshapes how programmers approach problem-solving. The author imparts essential principles of program construction, advocating for clarity and precision in code, and leading to cultivating a disciplined mindset, and fostering a deep appreciation for the art and science of programming.
Despite its age, all its knowledge is fully valid, and I’d give the book to every programmer so they can reach a higher level of quality.
Clean wraps, corners square, lay flat. No creases to the spine, or hinge. Small frayed spot at the top of the spine. No previous owner's name, no other marks in text. Well kept copy.
I’m a professor of computer science at Oregon State University. My research focus is on programming languages, but I also work on computer science education and outreach. I grew up in Germany and moved to the United States in 2000. Since computer science is a fairly new and not widely understood discipline, I am interested in explaining its core ideas to the general public. I believe that in order to attract a more diverse set of people to the field we should emphasize that coding is only a small part of computer science.
This book provides a brief introduction to the concept of algorithms before discussing the limitations of computation. Specifically, Harel explains undecidable problems (that is, problems for which no algorithm exists) and infeasible problems (that is, problems for which only algorithms are known that have an exponential runtime). I like this book (and its splendid title) because of its focus on the limitations of computation. Harel does a marvelous job in explaining two difficult topics about computation. The understanding of any scientific discipline requires the understanding of its limits, and the limits of computation are as significant as they are surprising.
Computers are incredible. They are one of the most important inventions of the 20th century, dramatically and irrevocably changing the way we live. That is the good news. The bad news is that there are still major limitations to computers, serious problems that not even the most powerful computers can solve. The consequences of such limitations can be serious. Too often these limits get overlooked, in the quest for bigger, better, and more powerful computers. In Computers Ltd., David Harel, best-selling author of Algorithmics, explains and illustrates one of the most fundamental, yet under-exposed facets of computers - their inherent…
