Personnel

Instructor:	Selim Aksoy (Office: EA 423, Email: )
TA:	Hüseyin Gökhan Akçay (Office: EA 522, Email: akcay[at]cs.bilkent.edu.tr)

Course Information

Schedule:	Tue 11:40-12:30, Fri 8:40-10:30 (EB 103)
Office hours:	Selim Aksoy (Fri 10:40-12:00) Hüseyin Gökhan Akçay (TBD)
Catalog description:	Image acquisition, sampling and quantization. Spatial domain processing. Image enhancement. Texture analysis. Edge detection. Frequency domain processing. Color image processing. Mathematical morphology. Image segmentation and region representations. Statistical and structural scene descriptions. Applications.
Course emphasis and goals:	This course provides an introduction to image analysis and computer vision for undergraduates. We will start with low-level vision (early processing) techniques such as binary image analysis, filtering, edge detection and texture analysis. Then, we will cover mid-level vision topics such as image segmentation and feature extraction in detail. Finally, we will do case studies on several applications such as image retrieval and classification. The emphasis will be on feature extraction and image representations for recognition.
Prerequisites:	Good programming background, data structures, linear algebra, vector calculus, basics of signal processing. No prior knowledge of image processing or computer vision is assumed.

Texts

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, 2002.
L. G. Shapiro and G. C. Stockman, Computer Vision, Prentice Hall, 2001.
D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002.
D. H. Ballard and C. M. Brown, Computer Vision, Prentice Hall, 1982.

Lecture Schedule

Chapters	Contents
Introduction [ Slides: pps \| pdf ] (Jan 30)	Topics: Overview Example applications
Digital Image Fundamentals [ Slides: pps \| pdf ] (Feb 2, 6)	Topics: Acquisition, sampling, quantization Image enhancement Image formats Linear algebra and MATLAB review Readings: GW Ch 1, 2, 3.1-3.4 SS Ch 1, 2 References: R. C. Gonzales, R. E. Woods, "Review material on linear algebra, probability, and linear systems" [ Slides: pps \| pdf ], 2002. Software: Matlab code examples for processing images
Binary Image Analysis [ Slides: pps \| pdf ] (Feb 9, 13, 16)	Topics: Pixels and neighborhoods Mathematical morphology Connected components analysis Automatic thresholding Readings: GW Ch 2.5, 9.1-9.5, 10.3 SS Ch 3.1-3.5, 3.8 Software: Matlab code examples for binary image analysis
Linear Filtering [ Slides: Part 1 (pps \| pdf) \| Part 2 (pps \| pdf) ] (Feb 20, 23)	Topics: Spatial domain filtering Frequency domain filtering Image enhancement Readings: GW Ch 3.5-3.8, 4 SS Ch 5.1-5.5, 5.10-5.11 Software: Matlab code examples for filtering
Edge Detection [ Slides: pps \| pdf ] (Feb 27, Mar 6, 9)	Topics: Edges, lines, arcs Hough transform Readings: GW Ch 10.1-10.2 SS Ch 5.6-5.8, 10.3-10.4 References: A. Fitzgibbon, M. Pilu, R. B. Fisher, "Direct Least Square Fitting of Ellipses" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(5):476-480, May 1999. Y. Li, L. G. Shapiro, "Object Recognition for Content-Based Image Retrieval" (local copy), Lecture Notes in Computer Science, 2004. S. Kiranyaz, M. Ferreira, M. Gabbouj, "Automatic Object Extraction Over Multiscale Edge Field for Multimedia Retrieval" (local copy), IEEE Trans. on Image Processing, 15(12):3759-3772, December 2006. M. S. Costa, L. G. Shapiro, "3D Object Recognition and Pose with Relational Indexing" (local copy), Computer Vision and Image Understanding, 79(3):364-407, September 2000. Software: Edge detection demo by Yi Li Line and arc finding demo by Yi Li Building recognition demo by Yi Li
Pattern Recognition Overview [ Slides: Part 1 \| Part 2 \| Part 3 ] (Mar 13)	Topics: Brief introduction to pattern recognition K-means and hierarchical clustering Readings: GW Ch 12.1-12.2 SS Ch 4 References: A. K. Jain, R. P. W. Duin, J. Mao, "Statistical Pattern Recognition: A Review" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(1):4-37, January 2000. Software: K-means clustering demo by Yi Li
Local Feature Detectors [ Slides: pps \| pdf ] (Mar 16, 20)	Topics: Corners and other interest points Invariants References: C. Schmid, R. Mohr, C. Bauckhage, "Evaluation of Interest Point Detectors" (local copy), International Journal of Computer Vision, 37(2):151-172, June 2000. K. Mikolajczyk, C. Schmid, "Scale & Affine Invariant Interest Point Detectors" (local copy), International Journal of Computer Vision, 60(1):63-86, October 2004. D. G. Lowe, "Distinctive Image Features From Scale-Invariant Keypoints" (local copy), International Journal of Computer Vision, 60(2):91-110, November 2004. K. Mikolajczyk, C. Schmid, "A Performance Evaluation of Local Descriptors" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(10):1615-1630, October 2005. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool, "A Comparison of Affine Region Detectors" (local copy), International Journal of Computer Vision, 65(1-2):43-72, November 2005. Local invariant features tutorial by Schmid and Lowe at CVPR 2003 Software: Affine covariant features by University of Oxford SIFT keypoint detector by David Lowe Affine interest point detectors by INRIA Rhone-Alpes Affine invariant region detector by Timor Kadir
Color Image Processing [ Slides: pps \| pdf ] (Mar 23)	Topics: Color spaces and conversions Readings: GW Ch 6 SS Ch 6.1-6.5
Texture Analysis [ Slides: pps \| pdf ] (Mar 27, 30)	Topics: Statistical approaches Structural approaches Readings: GW Sec 11.3.3 SS Ch 7
Image Segmentation [ Slides: pps \| pdf ] (Apr 3, 6)	Topics: Histogram-based approaches Clustering-based approaches Region growing Split-and-merge Morphological approaches Graph-based approaches Readings: GW Ch 10.4-10.5 SS Ch 10.1 References: J. Shi, J. Malik, "Normalized Cuts and Image Segmentation" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8):888-905, August 2000. C. Carson, S. Belongie, H. Greenspan, J. Malik, "Blobworld: Image Segmentation Using Expectation-Maximization and Its Applications to Image Querying" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(8):1026-1038, August 2002. P. Soundararajan, S. Sarkar, "An In-Depth Study of Graph Partitioning Measures for Perceptual Organization" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(6):642-660, June 2003. Image segmentation research at UC Santa Barbara Graph-based image segmentation tutorial by Shi, Fowlkes, Martin, Sharon at CVPR 2004 Software: Berkeley segmentation engine Normalized cuts segmentation by Jianbo Shi JSEG: segmentation of color-texture regions by UC Santa Barbara Edgeflow segmentation by UC Santa Barbara
Spring Break (Apr 9-13)	No class
Representation and Description [ Slides: pps \| pdf ] (Apr 17, 20, 24)	Topics: Image representations and descriptors Region representations and descriptors Readings: GW Ch 11 SS Ch 10.2, 3.7 References: D. Zhang, G. Lu, "Review of Shape Representation and Description Techniques" (local copy), Pattern Recognition, 37(1):1-19, January 2004.
Case Studies [ Slides: Part 1 (pps \| pdf) \| Part 2 (pps \| pdf) ] (Apr 27, May 1, 4, 8, 11)	Topics: Pattern recognition Image retrieval Image classification Object recognition References: Y. Liu, D. Zhang, G. Lu, W.-Y. Ma, "A Survey of Content-Based Image Retrieval with High-Level Semantics" (local copy), Pattern Recognition, 40(1):262-282, January 2007. X. S. Zhou, T. S. Huang, "Relevance Feedback in Image Retrieval: A Comprehensive Review" (local copy), Multimedia Systems, 8(6):536-544, April 2003. S. Antani, R. Kasturi, R. Jain, "A Survey on the Use of Pattern Recognition Methods for Abstraction, Indexing and Retrieval of Images and Video" (local copy), Pattern Recognition, 35(4):945-965, April 2002. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, "Content-Based Image Retrieval at the End of the Early Years" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(12):1349-1380, December 2000. Y. Rui, T. S. Huang, S.-F. Chang, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues" (local copy), Journal of Visual Communication and Image Representation, 10(1):39-62, March 1999. Y. Rubner, C. Tomasi, L. J. Guibas, "The Earth Mover's Distance as a Metric for Image Retrieval" (local copy), International Journal of Computer Vision, 40(2):99-121, November 2000. A. Vailaya, M. A. T. Figueiredo, A. K. Jain, H.-J. Zhang, "Image Classification for Content-Based Indexing" (local copy), IEEE Trans. on Image Processing, 10(1):117-130, January 2001. L. Fei-Fei, P. Perona, "A Bayesian Hierarchical Model for Learning Natural Scene Categories" (local copy), IEEE Conference on Computer Vision and Pattern Recognition, 2:524-531, June 20-25, 2005. P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, T. Tuytelaars, "A Thousand Words in a Scene" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, to appear, 2007. D. Gokalp, S. Aksoy, "Scene Classification Using Bag-of-Regions Representations", IEEE Conference on Computer Vision and Pattern Recognition, Beyond Patches Workshop, June 23, 2007. M. R. Boutell, J. Luo, C. M. Brown, "Factor Graphs for Region-Based Whole-Scene Classification" (local copy), IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Semantic Learning Applications in Multimedia, June 17, 2006. Y. Li, J. A. Bilmes, L. G. Shapiro, "Object Class Recognition using Images of Abstract Regions" (local copy), IAPR International Conference on Pattern Recognition, 1:40-43, August 23-26, 2004. Y. Li, L. G. Shapiro, J. A. Bilmes, "A Generative/Discriminative Learning Algorithm for Image Classification" (local copy), IEEE International Conference on Computer Vision, 2:1605-1612, October 17-21, 2005. Recognizing and learning object categories tutorial by Fei-Fei, Fergus and Torralba at ICCV 2005

Chapters

Introduction

[ Slides: pps | pdf ]

(Jan 30)

Topics:

Overview
Example applications

Digital Image Fundamentals

[ Slides: pps | pdf ]

(Feb 2, 6)

Topics:

Acquisition, sampling, quantization
Image enhancement
Image formats
Linear algebra and MATLAB review

Readings:

GW Ch 1, 2, 3.1-3.4
SS Ch 1, 2

References:

R. C. Gonzales, R. E. Woods, "Review material on linear algebra, probability, and linear systems" [ Slides: pps | pdf ], 2002.

Software:

Matlab code examples for processing images

Binary Image Analysis

[ Slides: pps | pdf ]

(Feb 9, 13, 16)

Topics:

Pixels and neighborhoods
Mathematical morphology
Connected components analysis
Automatic thresholding

Readings:

GW Ch 2.5, 9.1-9.5, 10.3
SS Ch 3.1-3.5, 3.8

Software:

Matlab code examples for binary image analysis

Linear Filtering

[ Slides: Part 1 (pps | pdf) | Part 2 (pps | pdf) ]

(Feb 20, 23)

Topics:

Spatial domain filtering
Frequency domain filtering
Image enhancement

Readings:

GW Ch 3.5-3.8, 4
SS Ch 5.1-5.5, 5.10-5.11

Software:

Matlab code examples for filtering

Edge Detection

[ Slides: pps | pdf ]

(Feb 27, Mar 6, 9)

Topics:

Edges, lines, arcs
Hough transform

Readings:

GW Ch 10.1-10.2
SS Ch 5.6-5.8, 10.3-10.4

References:

A. Fitzgibbon, M. Pilu, R. B. Fisher, "Direct Least Square Fitting of Ellipses" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(5):476-480, May 1999.
Y. Li, L. G. Shapiro, "Object Recognition for Content-Based Image Retrieval" (local copy), Lecture Notes in Computer Science, 2004.
S. Kiranyaz, M. Ferreira, M. Gabbouj, "Automatic Object Extraction Over Multiscale Edge Field for Multimedia Retrieval" (local copy), IEEE Trans. on Image Processing, 15(12):3759-3772, December 2006.
M. S. Costa, L. G. Shapiro, "3D Object Recognition and Pose with Relational Indexing" (local copy), Computer Vision and Image Understanding, 79(3):364-407, September 2000.

Software:

Pattern Recognition Overview

[ Slides: Part 1 | Part 2 | Part 3 ]

(Mar 13)

Topics:

Brief introduction to pattern recognition
K-means and hierarchical clustering

Readings:

GW Ch 12.1-12.2
SS Ch 4

References:

A. K. Jain, R. P. W. Duin, J. Mao, "Statistical Pattern Recognition: A Review" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(1):4-37, January 2000.

Software:

K-means clustering demo by Yi Li

Local Feature Detectors

[ Slides: pps | pdf ]

(Mar 16, 20)

Topics:

Corners and other interest points
Invariants

References:

C. Schmid, R. Mohr, C. Bauckhage, "Evaluation of Interest Point Detectors" (local copy), International Journal of Computer Vision, 37(2):151-172, June 2000.
K. Mikolajczyk, C. Schmid, "Scale & Affine Invariant Interest Point Detectors" (local copy), International Journal of Computer Vision, 60(1):63-86, October 2004.
D. G. Lowe, "Distinctive Image Features From Scale-Invariant Keypoints" (local copy), International Journal of Computer Vision, 60(2):91-110, November 2004.
K. Mikolajczyk, C. Schmid, "A Performance Evaluation of Local Descriptors" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(10):1615-1630, October 2005.
K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool, "A Comparison of Affine Region Detectors" (local copy), International Journal of Computer Vision, 65(1-2):43-72, November 2005.
Local invariant features tutorial by Schmid and Lowe at CVPR 2003

Software:

Color Image Processing

[ Slides: pps | pdf ]

(Mar 23)

Topics:

Color spaces and conversions

Readings:

GW Ch 6
SS Ch 6.1-6.5

Texture Analysis

[ Slides: pps | pdf ]

(Mar 27, 30)

Topics:

Statistical approaches
Structural approaches

Readings:

GW Sec 11.3.3
SS Ch 7

Image Segmentation

[ Slides: pps | pdf ]

(Apr 3, 6)

Topics:

Histogram-based approaches
Clustering-based approaches
Region growing
Split-and-merge
Morphological approaches
Graph-based approaches

Readings:

GW Ch 10.4-10.5
SS Ch 10.1

References:

J. Shi, J. Malik, "Normalized Cuts and Image Segmentation" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8):888-905, August 2000.
C. Carson, S. Belongie, H. Greenspan, J. Malik, "Blobworld: Image Segmentation Using Expectation-Maximization and Its Applications to Image Querying" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(8):1026-1038, August 2002.
P. Soundararajan, S. Sarkar, "An In-Depth Study of Graph Partitioning Measures for Perceptual Organization" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(6):642-660, June 2003.
Image segmentation research at UC Santa Barbara
Graph-based image segmentation tutorial by Shi, Fowlkes, Martin, Sharon at CVPR 2004

Software:

Spring Break

(Apr 9-13)

No class

Representation and Description

[ Slides: pps | pdf ]

(Apr 17, 20, 24)

Topics:

Image representations and descriptors
Region representations and descriptors

Readings:

GW Ch 11
SS Ch 10.2, 3.7

References:

D. Zhang, G. Lu, "Review of Shape Representation and Description Techniques" (local copy), Pattern Recognition, 37(1):1-19, January 2004.

Case Studies

[ Slides: Part 1 (pps | pdf) | Part 2 (pps | pdf) ]

(Apr 27, May 1, 4, 8, 11)

Topics:

Pattern recognition
Image retrieval
Image classification
Object recognition

References:

Y. Liu, D. Zhang, G. Lu, W.-Y. Ma, "A Survey of Content-Based Image Retrieval with High-Level Semantics" (local copy), Pattern Recognition, 40(1):262-282, January 2007.
X. S. Zhou, T. S. Huang, "Relevance Feedback in Image Retrieval: A Comprehensive Review" (local copy), Multimedia Systems, 8(6):536-544, April 2003.
S. Antani, R. Kasturi, R. Jain, "A Survey on the Use of Pattern Recognition Methods for Abstraction, Indexing and Retrieval of Images and Video" (local copy), Pattern Recognition, 35(4):945-965, April 2002.
A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, "Content-Based Image Retrieval at the End of the Early Years" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(12):1349-1380, December 2000.
Y. Rui, T. S. Huang, S.-F. Chang, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues" (local copy), Journal of Visual Communication and Image Representation, 10(1):39-62, March 1999.
Y. Rubner, C. Tomasi, L. J. Guibas, "The Earth Mover's Distance as a Metric for Image Retrieval" (local copy), International Journal of Computer Vision, 40(2):99-121, November 2000.
A. Vailaya, M. A. T. Figueiredo, A. K. Jain, H.-J. Zhang, "Image Classification for Content-Based Indexing" (local copy), IEEE Trans. on Image Processing, 10(1):117-130, January 2001.
L. Fei-Fei, P. Perona, "A Bayesian Hierarchical Model for Learning Natural Scene Categories" (local copy), IEEE Conference on Computer Vision and Pattern Recognition, 2:524-531, June 20-25, 2005.
P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, T. Tuytelaars, "A Thousand Words in a Scene" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, to appear, 2007.
D. Gokalp, S. Aksoy, "Scene Classification Using Bag-of-Regions Representations", IEEE Conference on Computer Vision and Pattern Recognition, Beyond Patches Workshop, June 23, 2007.
M. R. Boutell, J. Luo, C. M. Brown, "Factor Graphs for Region-Based Whole-Scene Classification" (local copy), IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Semantic Learning Applications in Multimedia, June 17, 2006.
Y. Li, J. A. Bilmes, L. G. Shapiro, "Object Class Recognition using Images of Abstract Regions" (local copy), IAPR International Conference on Pattern Recognition, 1:40-43, August 23-26, 2004.
Y. Li, L. G. Shapiro, J. A. Bilmes, "A Generative/Discriminative Learning Algorithm for Image Classification" (local copy), IEEE International Conference on Computer Vision, 2:1605-1612, October 17-21, 2005.
Recognizing and learning object categories tutorial by Fei-Fei, Fergus and Torralba at ICCV 2005

Exams

Midterm exam will be held at EB 201 at 18:30-20:30 on May 1, 2007. The exam will cover all topics from the beginning of the semester until the end of the representation and description chapter. You are allowed to bring the lecture notes (slides).
There will be at least two in-class quizzes.

Homework

Homework assignment 1: description | data (Due: February 27, 2007 as online submission)
Homework assignment 2: description | data | software (Due: March 29, 2007 as online submission)
Homework assignment 3: description | data | software (Due: April 13, 2007 as online submission)
Homework assignment 4: description | data | software (Due: May 7, 2007 as online submission)

Please make sure you fully understand the late submission policy and the honor code for assignments in the syllabus. Cheating and plagiarism on homework assignments will be punished according to the regulations of the University as described in the Bilkent University Policy on Academic Honesty / Öğrenci Disiplin İlke ve Kuralları.

Project

The goal of the project is to develop a content-based image retrieval system that supports queries by example. The similarity between the query and an image in the database will be computed based on global image features, individual region features, and combinations of regions.

Project description | data | software are available.
You must submit the final report and the developed code as a single archive file (e.g., zip, tar, rar) using the online form by May 25, 2007.
Presentations will be made on May 27, 2007 (exact time will be announced later).
Page limit for the final report is 6 pages for two person teams and 8 pages for three person teams (due to increased number of tasks) in IEEE two-column format as shown in the example and the format definition table and glossary. You can use IEEE's Word template or LaTeX template (conference mode). Try to follow the format as closely as possible.

Project teams:

Gokberk Cinbis, Volkan Dinc
Yasar Kemal Alp, Ibrahim Emir Atalay, Gorkem Saygili
Ates Akaydin, Baris Koc
Alp Artar, Bahadir Erkan, Mustafa Emre Kazdagli
Eren Algan, Abdullah Bulbul, Onur Kucuktunc
Yunus Emre Barun, Emre Candan, Ilker Onur Kaya
Dogus Ertemur, Efe Karasabun, Seyhun Sariyildiz
Bahadir Kemaloglu
Ali Sengul

Grading Policy

Homework:	40%
Quiz:	10%
Exam:	20%
Project:	25%
Class participation:	5%

Announcements

Personnel

Course Information

Texts

Lecture Schedule

Chapters

Contents

Introduction

Digital Image Fundamentals

Binary Image Analysis

Linear Filtering

Edge Detection

Pattern Recognition Overview

Local Feature Detectors

Color Image Processing

Texture Analysis

Image Segmentation

Spring Break

Representation and Description

Case Studies

Exams

Homework

Project

Grading Policy

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