Learn basics of Computer Vision
Computer Vision relates to the understanding of videos and digital images. It is an interdisciplinary field with a focus on how computers handle images. LiveEdu.tv is a great platform to start learning and improve your computer vision skills with a section dedicated to Computer vision tutorial and resources. Here you can watch how computer vision experts work and use it in your career.
Computer Vision Introduction
Computer Vision relates to the understanding of videos and digital images. Computer Vision is an interdisciplinary field which focuses on how computers handle images or videos. To understand how Computer Vision works, engineers try to understand how the human eyes work.
There are tasks which need to be taken care of in Computer Vision. They include methods for processing, acquiring, analyzing and finally understanding the digital images. Then data are transformed into symbolic or numerical information for easy handling by computers. The image processing enables engineers and scientists to develop a model that can later be used in different fields including medicine, aircraft, sports, etc.
Computer vision is closely connected with Artificial Intelligence as it is used by robots to sense the world around them.
Computer vision is directly connected to how we understand our world around us. We all know how our eyes work, but that doesn’t mean that we are free from any optical illusion. Computer vision tries to cross that boundary or at least match the capabilities of our natural eyes. Computer vision has application over multiple fields including augmented reality, gesture analysis, face recognition, biometrics, agriculture, augmented reality, robotics, security, and many more.
Computer vision is a great field to start a career as it has real-world applications.
History of Computer Vision
Computer Vision history started in the 1960s in universities that were pioneering artificial intelligence. The aim is to mimic the human eye and enable the computers to see the world just like humans. In 1966, scientists and engineers started to gain progress in the field of Computer Vision, and it is believed that they were able to attach a camera to the computer which represented the world quite closely.
Computer vision is different from Digital image processing because it tries to understand the world in 3D rather than simple images.In the 1970s, a lot of-of key algorithms for computer vision were released including the “extraction of edges” from images, non-polyhedra, and polyhedral modeling, labeling of lines, motion estimation, etc.
The next decade saw great improvements in the field of computer vision. The progress came with the help of rigorous mathematical analysis and quantitative aspects. Different process including texture, focus, shading, the concept of scale-space, etc. came into light. The growth continued in the 1990’s when the old topics got more attention.
1990’s also saw the advent of the statistical learning techniques. These techniques are used in recognizing faces. Later on, Computer vision saw higher interaction with different fields such as computer graphics.
The most recent works include the feature-based methods, machine learning techniques, complex optimization frameworks, etc.
Computer Vision Tools
Just like any subfield of artificial intelligence, computer vision is huge and requires tools to work effectively and efficiently. These tools will help you gain an advantage and make work easy. Let us list the best computer vision tools out there. We will only focus on open source tools because they are easy to acquire.
- OpenCV OpenCV is a computer vision library that falls under the BSD license. It offers C, C++, Java and Python interfaces which support Linux, Mac, Windows, iOS, OS, and Android. The library is designed for maximum efficiency and offers great value when used to developer real-time applications.
- SimpleCV SimpleCV is an open-source framework for computer vision applications. SimpleCV can also be used to use the OpenCV library and other powerful libraries available. With this, you can try out computer vision without diving deep into learning the file formats, bit depths, buffer management, etc.
- VLFeat VLFeat is yet another open-source library. It offers popular computer vision algorithms and a clear understanding of extraction and matching.
- AForge.NET: If you use C# and .NET and want to utilize it in the fields of Artificial Intelligence and Computer Vision, AForge.NET is for you.
- BoofCV: BoofCV is an open source Java library. It can be used to develop robotics and computer vision applications. It offers high performance and easy to use.
- Matlab Matlab offers functions, algorithms and apps for simulating and designing the computer vision
Education Ecosystem Computer Vision Project Creators
If you are wondering where to get started to learn Computer Vision, then our recommendation will be to watch Computer Vision Project Creators on Education Ecosystem. Let’s list the top 5 Computer Vision Project Creators on Education Ecosystem.
Computer Vision Best Books
There are plenty of Computer Vision books online. The best way to start learning Computer Vision is to invest in the books. So, why the wait? Let’s go through the best books for learning Computer Vision. These books are categorized into Beginner, Intermediate and Advanced. So pick the book that best suits you.
This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text.
This book Addresses the need for a concise and accessible introduction to the complex field of computer vision, this text reinforces its presentation of the essential topics with class-tested exercises. The coverage includes an historical overview of the technology.
The author describes classical computer vision algorithms used on a regular basis in Hollywood (such as blue screen matting, structure from motion, optical flow, and feature tracking) and exciting recent developments that form the basis for future effects (such as natural image matting, multi-image compositing, image retargeting, and view synthesis).
In this book, you’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory.
This book will teach you how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open source framework written in Python. Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images.
This book 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.
Since the automobile first rolled off the assembly line in River Rouge, Michigan, cars in America have offered independence, mobility, and adventure. Now, profound changes are coming to our roads. Technological advancements are progressing at a rapid pace and fully self-driving cars will be here sooner than we think.
This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications.
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.
Computer Vision Projects
The best way to learn is to get yourself involved in Projects. Let’s look at some of the best Computer Vision projects that you can follow. You can also find Computer Vision projects on Education Ecosystem. If you are interested in finding, check the Education Ecosystem machine learning Project Creators section for more information.
Computer Vision Community
The community of computer vision is big. There are plenty of websites to find a community to be part of. Let’s list some of them below.
- Computer Vision Foundation
Computer Vision Foundation is a non-profit organization that supports computer vision.
- Computer Vision Common Lounge
A great place to find paper summaries, discuss and share anything new regarding computer vision.
- Education Ecosystem
Here you can find all the awesome Project Creators who love to share their knowledge about Machine learning.
Computer Vision Gurus
Jim parker is a professor of computer science, a professor of drama and a professor of art with 30 years career. He taught game design and had designed a number of games which include acclaimed booze cruise in 2007-2008. He is also a writer of fiction in the slipstream genre.
Prof. Ram Nevatia is a professor of Computer Science and Electrical Engineering at the University of Southern California. He is also the founding director of the Institute for Robotics, and Intelligent Systems of the USC Viterbi School of Engineering IRIS is engaged in a number of research projects relating to computer vision robotics and intelligent agent research. Most importantly he has 25 years experience in Computer Vision.
Prof. Wolfgang Heidrich is a professor of computer science and the Director of KAUST Visual Computing Center. His research interest lies in computational imaging and display.
David Lowe is a Computer Scientist working for Google as a Senior Researcher. He is a researcher in computer vision and the author of the patented scale-invariant feature transform, one of the most popular algorithms in the detection of image features.
Gerhard X Ritter is a professor in the Department of Mathematics, Department of Computer and Information Science and Engineering. He is an expert in the following areas: computational neuroscience, computer vision and medical image computing, image and signal analysis and machine learning.
Computer Vision Conferences
As Machine Learning is a trending topic in the market, there are many conferences out there that you can attend. Let’s list some of the best data visualization conferences out there.