Lib4U

‎"Behind every stack of books there is a flood of knowledge."

Computer Vision

 

titlefig

Intro

Pset 0: out 8/27, due 9/7

Pset 0 images

Part 2 code example solution

Solutions given in class on 9/15

Tues 9/1

Features

Linear filters : F&P Chapter 7 sections 7.1, 7.2, 7.5, 7.6

[T&V Chapter 4]

[S&S Chapter 5.3]

Linear filters I

Thurs 9/3

Matlab intro

Matlab tutorial (guest lecture, Yong Jae Lee)

Tues 9/8

Linear filters, edges: F&P Ch 8

[S&S Chapter 3]

Linear filters 2

Pset 1: out 9/8, due 9/21

Pset 1 images

Seam carving page with video

Class results

Thurs 9/10

Edges: F&P Ch 8

Binary images: [S&S Chapter 3]

Edge detection and binary image analysis

Tues 9/15

Texture: F&P 9.1 and 9.3

A Statistical Approach to Texture Classification from Single Images, by ManikVarma and AndrewZisserman, International Journal of Computer Vision, 2005.

When is Scene Identification Just Texture Recognition? by Laura Walker Renninger andJitendra Malik, Vision Research, 2004.

Alyosha Efros’s Texture Synthesis page, with links to non-parametric sampling method and image quilting

Texture

Thurs 9/17

Grouping and Fitting

Segmentation: F&P Ch 14

k-means applet demo

Normalized Cuts and Image Segmentation, byJianbo Shi and JitendraMalik, PAMI 2000.

Ncuts Matlab code

Contour and Texture Analysis for Image Segmentation, by Malik et al. IJCV 2001.

Segmentation, clustering

Tues 9/22

Hough transform: F&P 15.1

[S&S pp. 304-310]

Excerpt from Ballard & Brown

Hough Transform demo

Hough, voting

Pset 2: out 9/22, due 10/5

Solutions given out in class 10/13

Thurs 9/24

Deformable contours:

[T&V p. 108-113]

[S&S p. 489-495]

Deformable contours

Tues 9/29

Background modeling and background subtraction

Read F&P 14.3, and

Stauffer & Grimson paper:Adaptive Background Mixture Models for Real-Time Tracking, CVPR 1999.

Background models(guest lecture by BirgiTamersoy)

Thurs 10/1

Cameras and Multiple views

Fundamentals of image formation

Read F&P Chapter 1

Image formation

(guest lecture by JaechulKim)

Tues 10/6

Fitting and multiple views: alignment and image warping

Alignment, warping

 

Thurs 10/8

Robust fitting

Midterm review

F&P Section 15.5, 15.5.2

Ransac

 Tues 10/13

Midterm exam

 Pset 3: out 10/13, due 10/27

Class results are posted here

Solutions given in class 11/3

Thurs 10/15

Midterm solutions given in class

 Tues 10/20

Multiple views

Epipolar geometry and stereo vision

F&P sections 10.1.1-10.1.2

F&P sections 11.1-11.3

[T&V selected sections]

Epipolar geometry applet

Epipolar geometry,stereo

 Thurs 10/22

Stereopsis, calibration

Video view interpolation,Zitnick et al.

Microphone arrays as generalized cameras for integrated audio visual processing, O’Donovan and Duraiswami

Body tracking, Demirdjianet al.

Fundamental matrix song

Stereopsis, calibration

 Tues 10/27

Local invariant features: detection and description

Selected pages from:

Ch 3: Visual Recognition: Local Features: Detection and Description K. Grauman and B.Leibe [p. 23-39]

Local Invariant Feature Detectors: A Survey, T.Tuytelaars and K. Mikolajczyk, 2008.  [p. 178-188, 0.216-220, p. 254-255]

Distinctive Image Features from Scale-InvariantKeypoints, David Lowe, IJCV 2004.

SIFT demo software from David Lowe

Oxford group’s software for interest point detection and descriptors

VLFeat SIFT library from Andrea Vedaldi (C, and includes Matlab interfaces)

Invariant local features

 Thurs 10/29

Recognition

Image indexing and bag-of-words models

Ch 5: Visual Recognition: Visual Vocabularies.  K. Grauman and B. Leibe [p. 62-69]

Blackboard: bag of words model

Video Google: A Text Retrieval Approach to Object Matching in Videos, by J. Sivic and A.Zisserman, 2003.

Indexing, bag-of-words

Tues 11/3

Intro to recognition issues;

Model-based recognition with alignment and voting

F&P Sections 18.1, 18.3, 18.5

Pset3 solutions given out in class.

Astrometry.net

Object recognition from local scale-invariant features, David Lowe, 1999.

Intro to recognition problem, Alignment-based approach

Thurs 11/5

Part-based models and spatial cues from local features

Ch 7: Visual Recognition: Part-based Models.  K. Grauman and B. Leibe.  [p. 83-97]

Implicit shape model,Leibe et al., 2004.

Pyramid match kernel, Grauman & Darrell, 2005.

Spatial pyramid match kernel, Lazebnik et al. 2006.

LIBPMK : pyramid match toolkit

Part-based models and spatial cues for categories

Pset 4: out 11/5, due 11/24

Solutions given in class 12/1

Tues 11/10

(Face) detection via classification on appearance windows

F&P 22.1-22.2, 22.3.1-22.3.2

Rapid Object Detection using a Boosted Cascade of Simple Features, by P. Viola and M. Jones, 2001.

OpenCV Library, includes code for Viola-Jones face detector

Automated Visual Recognition of Individual African Penguins, byBurghardt et al., 2004.

Face detection

Thurs 11/12

Support vector machines for object classification

F&P 22.5

Histograms of Oriented Gradients for Human Detection, Dalal & Triggs, 2005.  Code

LIBSVM library for support vector machines

Learning Gender with Support Faces,Moghaddam & Yang, 2002.

Classification with support vector machines

Tues 11/17

Shape matching

Face transformer, University of St. Andrews

Breaking a visual CAPTCHA, Mori & Malik

Matching with shape contextscode, Belongie et al.

Shape matching

Thurs 11/19

Motion and Tracking

Motion and optical flow

T&V 8.3, 8.4

Optical flow

Tues 11/24

Tracking: linear dynamics

F&P 17.1-17.2.3, 17.3.1

Censusing bats, Infrared thermal video analysis of bats, Betke et al.

Tracking

Pset 5: out 11/24, due 12/4*

Tues 12/1

Tracking wrapup

Tracking people by learning their appearance,Ramanan et al.

Condensation: Conditional Density Propagation for Visual Tracking, Isard and Blake; videos

Tracking, recap

Thurs 12/3

Exam review

12/14 Mon

Final exam 2-5 PM in JGB 2.218

Source:

http://www.cs.utexas.edu/~grauman/courses/fall2009/schedule.htm

 

 

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