Here are 100 books that Computer Vision fans have personally recommended if you like
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.
Adding perspective puzzled artists in the fourteenth century; analysing perspective is integral to applied computer vision. You might have seen Hawkeye in action: the principles by which it works are explained superbly within this book. It was the first of its kind to set this analysis in a lucid and compelling format. Richard and Andrew’s text will be on researchers’ bookshelves for many years for its bedrock description of how we analyse three-dimensional scenes.
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has…
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…
As a fantasy author, I have always been drawn to magic and alternative universes as a way of exploring the issues that seem unsolvable here on earth. We are so entrenched in our own ways of seeing the world that it can be hard to imagine looking at things from a different perspective, but twisting these ideas sideways and adding a dash of magic can remind us that everything exists in shades of grey. Teenagers are learning to think abstractly and explore their own relationships with ethics, and I have vivid memories of being drawn to moral ambiguity at that age.
At the beginning, readers are drawn into an exciting world where humans battle to save their desolate planet from an alien race. But in typical Brandon Sanderson fashion, good and evil are not clear-cut, and the conflict soon grows far more complex.
As the series progresses, the main character, Spensa, discovers that the battle for her planet is not as straightforward as she had once thought, and that the aliens she had fought were neither uniform nor uniformly evil. When she infiltrates an alien homeland in Book 2, Spensa is forced to question her original assumptions about who deserves to be saved—not just humans, but maybe also some alien races and even artificially intelligent beings.
Spensa's world has been under attack for hundreds of years. An alien race called the Krell leads onslaught after onslaught from the sky in a never-ending campaign to destroy humankind. Humanity's only defense is to take to their ships and fight the enemy in the skies. Pilots have become the heroes of what's left of the human race.
Spensa has always dreamed of being one of them; of soaring above Earth and proving her bravery. But her fate is intertwined with her father's - a pilot who was killed years ago when he abruptly deserted his team, placing Spensa's chances…
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…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
The book is a well-curated collection of the essential mathematical concepts that form ML. You may experience a cultural shock jumping to this book from the previous one, because the writing in this book is a bit formal. However, it is the missing but necessary piece for building solid foundations for practical ML. You will find it more valuable combining the intuition behind ML that you gained previously. And the explanations in the book are succinct and from the ML perspectives. For instance, partial derivatives are explained in terms of neural network weight optimization. I wish the concepts in Linear Algebra, Vector Calculus, and Probability courses back in college were introduced this way so I understand better how they are applied.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these…
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.
In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods out there. Researchers 20 years ago used to do a lot of impressive work with a lot less computing power.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
