Computer Vision: From 3D Reconstruction to Visual Recognition

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Description

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This course delivers a systematic overview of computer vision, emphasizing two key issues in modeling vision: space and meaning. We will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene.

About the Course

When a 3-dimensional world is projected onto a 2-dimensional image, such as the human retina or a photograph, reconstructing back the layout and contents of the real-world becomes an ill-posed problem that is extremely difficult to solve. Humans possess the remarkable ability to navigate and understand the visual world by solving the inversion problem goin…

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Didn't find what you were looking for? See also: Artificial Intelligence, 3D, Algorithms, Machine Learning, and Computer Science.

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.

This course delivers a systematic overview of computer vision, emphasizing two key issues in modeling vision: space and meaning. We will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene.

About the Course

When a 3-dimensional world is projected onto a 2-dimensional image, such as the human retina or a photograph, reconstructing back the layout and contents of the real-world becomes an ill-posed problem that is extremely difficult to solve. Humans possess the remarkable ability to navigate and understand the visual world by solving the inversion problem going from 2D to 3D. Computer Vision, a modern discipline of artificial intelligence, seeks to imitate such abilities of humans to recognize objects, navigate scenes, reconstruct layouts, and understand the geometric space and semantic meaning of the visual world. These abilities are critical in many applications including personal robotics, autonomous driving and exploration as well as photo organization, image or video retrieval and human-computer interaction. This course delivers a systematic overview of computer vision, comparable to an advanced graduate level class. We emphasize on two key issues in modeling vision: space and meaning. We begin by laying out the main problems vision needs to solve: mapping out the 3D structure of objects and scenes, recognizing objects, segmenting objects, recognizing meaning of scenes, understanding movements of humans, etc. Motivated by these important problems centered on the understanding of space and meaning, we will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene

About the Instructor(s)

Prof. Silvio Savareseis an Assistant Professor of Electrical and Computer Engineering at the University of Michigan, Ann Arbor. After earning his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005, he joined the University of Illinois at Urbana-Champaign from 2005 - 2008 as a Beckman Institute Fellow. He is recipient of a TWR Automotive Endowed Research Award in 2012, an NSF Career Award in 2011 and Google Research Award in 2010. In 2002 he was awarded the Walker von Brimer Award for outstanding research initiative. He served as workshops chair and area chair in CVPR 2010, and as area chair in ICCV 2011 and CVPR 2013.

Silvio Savarese has been active in promoting research in the field of object recognition and scene representation. He co-chaired and co-organized the 1st, 2nd and 3rd edition of the IEEE workshop on 3D Representation for Recognition (3dRR-07, 3dRR-09, 3dRR-11) in conjunction with the ICCV. He was editor of the Elsevier Journal in Computer Vision and Image Understanding, special issue on "3D Representation for Recognition" in 2009. He co-authored a book on 3D object and scene representation published by Morgan and Claypool in 2011. His work with his students has received several best paper awards including a best student paper award in the IEEE CORP workshop in conjunction with ICCV 2011 and the CETI Award at the 2010 FIATECH's Technology Conference. His research interests include computer vision, object recognition and scene understanding, shape representation and reconstruction, human activity recognition and visual psychophysics.

Prof. Fei-Fei Li is an assistant professor at the Computer Science Department, Stanford University. Her main research interest is in vision, particularly high-level visual recognition. In computer vision, Fei-Fei's interests span from object and natural scene categorization to human activity categorizations in both videos and still images. In human vision, she has studied the interaction of attention and natural scene and object recognition, and decoding the human brain fMRI activities involved in natural scene categorization by using pattern recognition algorithms. Fei-Fei graduated from Princeton University in 1999 with a physics degree. She received PhD in electrical engineering from the California Institute of Technology in 2005. From 2005 to August 2009, Fei-Fei was an assistant professor in the Electrical and Computer Engineering Department at University of Illinois Urbana-Champaign and Computer Science Department at Princeton University, respectively. Fei-Fei and her students have published in major scientific journals and top conferences, including winning a first prize in the first Semantic Visual Robot competition at AAAI 2007, a Best Paper Honorable Mention at IEEE CVPR 2010, and a first prize of the PASCAL VOC Action Challenge in 2012. Fei-Fei is a recipient of a Microsoft Research New Faculty award, a number of Google Research Awards, the Alfred Sloan Faculty Award and an NSF CAREER award. (Fei-Fei publishes using the name L. Fei-Fei.)

Course Syllabus

Part 0: Introduction - What is computer vision? Part 1: Visual understanding in 2D space pixels groups of pixels object and scene recognition video features Part 2: Perceiving and modeling the 3D space capturing a picture in 3D popping out a scene in 3D popping out an object in 3D mapping out a space Part 3: Coherent understanding of the scene and the 3D space object recognition in 3D space visual recognition in context Part 4: Functions and activities in the 3D scene event recognition in images action recognition in videos vision and language

Recommended Background

You should be able to program in at least one programming language (preferably Matlab, and/or C) and have a computer (Windows, Mac or Linux) with internet access. It also helps to have some previous exposure to basic concepts in linear algebra, probability, statistics. College level image processing, signal processing, and machine learning are a plus, but not a must.

Course Format

The class will consist of lecture videos, which are between 8 and 12 minutes in length. These contain 1-2 integrated quiz questions per video. There will also be standalone homeworks that are not part of video lectures, optional programming assignments, and a (not optional) final exam.

FAQ

  • Will I get a statement of accomplishment after completing this class?

    Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

Provided by:

University: Stanford University, University of Michigan

Instructor(s): Silvio Savarese, Assistant Professor & Fei-Fei Li, Assistant Professor

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