Computer Vision: The Fundamentals

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Description

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In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities and more.

About the Course

Computer vision seeks to develop algorithms that replicate one of the most amazing capabilities of the human brain - inferring properties of the external world purely by means of the light reflected from various objects to the eyes. We can determine how far away these objects are, how they are oriented with respect to us, and in relationship to various other objects. We reliably guess their colors and textures, and we can recognize…

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Didn't find what you were looking for? See also: Computer Hardware, Two Dimensional Design (2D Design), Neural Networks, Python, and Microsoft Visio.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities and more.

About the Course

Computer vision seeks to develop algorithms that replicate one of the most amazing capabilities of the human brain - inferring properties of the external world purely by means of the light reflected from various objects to the eyes. We can determine how far away these objects are, how they are oriented with respect to us, and in relationship to various other objects. We reliably guess their colors and textures, and we can recognize them - this is a chair, this is my dog Fido, this is a picture of Bill Clinton smiling. We can segment out regions of space corresponding to particular objects and track them over time, such as a basketball player weaving through the court.

In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities, automated monitoring of activities, segmenting out organs or tissues in biological images, and sensing for control of robots. We will build this up from fundamentals - an understanding of the geometry and radiometry of image formation, core image processing operations, as well as tools from statistical machine learning. On completing this course a student would understand the key ideas behind the leading techniques for the main problems of computer vision - reconstruction, recognition and segmentation - and have a sense of what computers today can or cannot do.

About the Instructor(s)

Jitendra Malik is Arthur J. Chick Professor of Electrical Engineering and Computer Science at UC Berkeley, where he has been on the faculty since 1986. He is also on the faculty of the department of Bioengineering, and in the Cognitive Science and Vision Science groups.

Professor Malik's research group has worked on many different topics in computer vision, computational modeling of human vision, computer graphics and the analysis of biological images, resulting in more than 150 research papers and 30 PhD dissertations. Several well-known concepts and algorithms arose in this research, such as anisotropic diffusion, normalized cuts, high dynamic range imaging, and shape contexts. According to Google Scholar, six of his papers have received more than a thousand citations each, and he is one of ISI's Highly Cited Researchers in Engineering.

Professor Malik received the gold medal for the best graduating student in Electrical Engineering in 1980 as well as the Distinguished Alumnus Award in 2008 from IIT Kanpur. He received his PhD degree from Stanford University in 1985, a Presidential Young Investigator Award in 1989, and at UC Berkeley he was selected for the Diane S. McEntyre Award for Excellence in Teaching in 2000 and a Miller Research Professorship in 2001. He is a Fellow of the ACM and IEEE, and a member of the National Academy of Engineering.

Recommended Background

Linear algebra, calculus, and probability at a level expected of a junior or senior undergraduate in science, engineering or mathematics. Programming experience and basic algorithms. Machine learning background would be helpful but not strictly necessary, as pointers will be provided for self-study.

Suggested Readings

None is required, as the lectures will be mainly self-contained. For more details and useful additional perspective, you could look at (1) Forsyth & Ponce, Computer Vision: A Modern Approach (2nd Edition) (2) Szeliski, Computer Vision: Algorithms and Applications (Texts in Computer Science).

Provided by:

University: University of California, Berkeley

Instructor(s): Jitendra Malik

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