Ballan Luca's Home Page. Mathematical Foundations of Computer Graphics and Vision L.
Ballan, J. C. Bazin [web] This course will provide in-depth coverage of some fundamental mathematical tools that are widely used in current state of the art techniques in computer graphics and vision. [PDF] Variational Methods I [PDF] Variational Methods II [PDF] Variational Methods III [PDF] The Geometry of SO(n) & SE(n) [PDF] Metrics on SO(3) and Inverse Kinematics [PDF] Inverse Kinematics II & Motion Capture. 870 Grounding Object Recognition and Scene Understanding, Fall 2011. Overview This class will cover current approaches to object recognition and scene understanding in computer vision and its relation to other disciplines.
The goal of this class is to provide an in depth presentation of computer vision techniques for recognition of objects, scenes, materials, actions, ... but by putting them in the framework of concrete tasks. The class is addressed to students from any discipline, not just vision, interested in learning about computer vision techniques that can be applied to their research. We will cover state of the art object recognition and scene understanding techniques and how they relate to robotics, language, computer graphics, crowd sourcing, human-computer interaction, etc. For students in computer vision, this class will allow exploring new tasks and scene representations, beyond labeling objects in images for the sake of it.
236861 - Numerical Geometry Of Images, Winter2012-2013 - Announcements. Schedule of Classes Course Detail. This is an introductory course on 3D computer vision.Through the study of the geometry of rigid motions and perspective projections we will explore ways for computers to infer three-dimensional properties of the environment from collections of images.
In particular, the course will concentrate on estimating 3D shape and motion. NOTE: Course description on registrar website is updated. No "symbolic and iconic representations", no "neural networks" in this course. There are no formal prerequisites. However, a solid background in linear algebra and basic probability and stochastic processes is highly recommended. The grade is based on the quality of the projects. Students that are not wishing to participate in a project can opt for a final exam, which will be conducted in class (closed books). In either case, 10% of the grade will be based on class participation, questions answered and questions asked. Most of the projects will involve processing real data. Code. IPOL: Image Processing On Line. CVonline wiki. Joseph Mundy: Model-Based Computer Vision. Computer Vision Models. Peter's Functions for Computer Vision.
To use these functions you will need MATLAB and the MATLAB Image Processing Toolbox.
You may also want to refer to the MATLAB documentation and the Image Processing Toolbox documentation Octave Alternatively you can use Octave which is a very good open source alternative to MATLAB. Almost all the functions on this page run under Octave. See my Notes on using Octave. An advantage of using Octave is that you can run it on your Android device. MATLAB/Octave compatibility of individual function is indicated as follows Runs under MATLAB and Octave.
I receive so many mail messages regarding this site that I have difficulty responding to them all. Please report any bugs and/or suggest enhancements to Acknowledgement: Much of this site was developed while I was with the School of Computer Science & Software Engineering The University of Western Australia I thank them for continuing to host this site. Cheers, Peter Kovesi. CS 525 Reading List. CPSC 525: Course Outline and Reading List Instructor: David LoweJanuary-April 2014 Course home page: Textbook: While most of the course is based on original research papers, we will also consult the following textbook by Richard Szeliski.
It is available for free on-line, or can be purchased in printed form. Computer Vision: Algorithms and Applications by Richard Szeliski The following is a tentative list of topics and readings for the course. Introduction The first class will provide an overview of the computer vision field and its applications. UCI's Projects in CV. [Lectures] [Matlab] [Project Submission] [Project Videos] [Project1] [Project2] [Project3] [Project4] [Project5] Administrivia News: No class on Thursady, April 14.
