Representation and Modeling for Image Analysis's Full Profile
Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others.For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.
Feb 01, 2005
to May 25, 2005
Days of the Week:
Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
- Level of Difficulty: Beginner
- Size: Massive Open Online Course
- Instructor: Prof. Polina Golland
- Cost: Free
- Institution: MIT OCW
- Topics: Computer Vision
About MIT OCW:
MIT OpenCourseWare (OCW) is a web-based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity.
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MIT OpenCourseWare (MIT OCW) is an initiative of the Massachusetts Institute of Technology (MIT) to put all of the educational materials from its undergraduate- and graduate-level courses online, partly free and openly available to anyone, anywhere.