CMPE 264:   Image Analysis and Computer Vision

Spring 2008

Description:

The topics covered by this course include digital image formation, image features and feature detection, structure from X, image segmentation, object recognition, motion analysis, and stereopsis. Students will also work in groups to solve some real world problems. Some potential project topics are automatic object recognition, human face detection, video surveillance, and content-based image retrieval.

Syllabus:

· Introduction

· Image and video acquisition

· Camera models

· Image features - edge, corners, lines, Hough Transform, deformable contours

· Camera calibration

· Camera motion estimation and 3D scene reconstruction

· Stereopsis

· 2D motion analysis - optical flow estimation, differential techniques

· Shape from X - reflection model, shape from shading, shape from texture, shape from defocusing and focusing

· Tracking - Kalman filtering, correlation-based tracking, change-based tracking, 2D layer tracking, tracking of articulated objects

· Image segmentation

· Object recognition - Feature, invariants, subspace method, face detection and recognition

Reference:

Computer Vision -- A modern approach, by David Forsyth and Jean Ponce, Prentice Hall, 2002.

Time and Place:

TuTh   2:00 p.m. - 3:45 p.m.,   Baskin Engineering 2, 192

Instructor:

Hai Tao (http://www.soe.ucsc.edu/~tao/)
Email:   tao@soe.ucsc.edu
Office:  Engineering 2,  Room 333

Evaluation: Coursework will be weighted as follows:

 Homework   25%
 Mid-term      30%
 Project          45%

Homework: Homework will be collected every next week on Thursday at the end of the class.

Homework Assignments:

Lecture notes:

Introduction,

Image acquisition and camera model,

An additional homework problem,

Image noise and filtering,

Image features: edges and corners,

Hough transform: lines and curves,

Model fitting and robust regression,

Camera calibration,

Epipolar geometry and the 8-point algorithm,

Tomasi-Kanade factorization,

Zhang's correspondence algorithm,

Rectification and depth computation,

Image motion analysis,

Optical flow,

Additional homework problems,

Hidden Markov model (HMM),

Object tracking and Kalman filtering,

Object recognition - interpretation tree,

Object recognition - appearance subspace,

Image segmentation,

Shape from shading (1),

Shape from shading (2),

Shape from texture,

Deformable contours,

Texture synthesis,

Some Project Topics:

3D reconstruction and camera motion analysis

Resolution enhancement

Multiple view tracking

Object tracking with occlusion

Face detection

Facial feature detection

Facial motion analysis and event detection

Image-based realistic facial animation

Content-based image retrieval

Image-based relighting

A marker-driven pointing device

Optical flow estimation

Shape from brightness functions

Computer vision links:

CMU Computer Vision Home Page

CVonline: Computer Vision Online Course Notes

USC Annotated Computer Vision Bibliography

PEIPA - the Pilot European Image Proecssing Archive