Name: Computer Vision
ISBN: 9781848829350

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 also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course…

What Hacker News says

bonoboTPJun 2025

The other main one is Szeliski's Computer Vision 2nd Ed from 2022 https://szeliski.org/Book/

Forsyth & Ponce is also good but somewhat old by now. And for 3d, the classic is still Hartley & Zisserman's Multiple View Geometry.

thread

FullStackDev1Apr 2023

I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way

Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here's a detailed learning plan, broken down into four stages:

Stage 1: Building a Strong Foundation

Mathematics:

Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang

Calculus: "Calculus: Early Transcendentals" by James Stewart

Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

Programming:

Python: "Python Crash Course" by Eric Matthes

Git: "Pro Git" by Scott Chacon and Ben Straub

Machine Learning & Data Science Basics:

"Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas

Stage 2: Learning Machine Learning and Deep Learning

Online Courses:

Coursera: "Machine Learning" by Andrew Ng

Coursera: "Deep Learning Specialization" by Andrew Ng

Fast.ai: "Practical Deep Learning for Coders"

Books:

"Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Stage 3: Expanding Knowledge of AI Subfields

Natural Language Processing (NLP):

"Speech and Language Processing" by Daniel Jurafsky and James H. Martin

"Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

Computer Vision:

"Computer Vision: Algorithms and Applications" by Richard Szeliski

"Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:

"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

"Deep Reinforcement Learning Hands-On" by Maxim Lapan

Stage 4: Staying Current and Gaining Practical Experience

Read research papers:

Subscribe to the arXiv mailing list in your areas of interest Regularly read papers from conferences like NeurIPS, ICML, and ACL Participate in online competitions:

Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio AIcrowd: Another platform for AI competitions Contribute to open-source projects:

TensorFlow, PyTorch, or other popular AI frameworks Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation Network with AI professionals:

Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/MachineLearning Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!

thread

randcrawJun 2021

Cool. Another book you'll want to check out is Richard Szeliski's free draft of "Computer Vision: Algorithms and Applications", second edition, https://szeliski.org/Book/

thread

maffydubJun 2017

I found Computer Vision: Algorithms and Applications really good. You can download it for free (for personal use) at http://szeliski.org/Book/.

thread

ntenenzAug 2014

Szeliski also has written a comprehensive Computer Vision book for those looking to learn the field. It's a fairly good one (and available for free online - http://szeliski.org/Book/).

thread

joshuakApr 2014

Sadly I've found that most sources prefer to be academically rigorous over quickly comprehensible. Of course it's good that people have done the academic work, but it can be tragically comical how obtuse an academic paper can make a simple concept.

However, one of the most recommended books on the subject is available online, so you might want to check that out.

Computer Vision: Algorithms and Applications by Richard Szeliski

http://szeliski.org/Book/

thread