# Representation and Modeling for Image Analysis

Example object representations. (Images by Prof. Polina Golland.)

### Instructor(s)

Prof. Polina Golland

### MIT Course Number

6.881

### As Taught In

Spring 2005

### Level

Graduate

###

Some Description
Instructor(s) | Prof.
As Taught In | Spring 2002
Course Number | 2.24
Level | Undergraduate/Graduate
Features | Lecture Notes, Student Work

## Course Description

## Course Features

- Selected lecture notes
- Projects and examples

## Course Description

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.

## Related Content

## Course Collections

See related courses in the following collections:

### Find Courses by Topic

- Systems Engineering > Computational Modeling and Simulation
- Electrical Engineering > Signal Processing
- Computer Science > Graphics and Visualization

Polina Golland. 6.881 Representation and Modeling for Image Analysis. Spring 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.

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