• Course Info
• Lectures
• Exams
• Homework
• Project
• Grading
• Links

Announcements

1. (Sep 17) Course page is online.
2. (Sep 17) Syllabus is available.
3. (Sep 18) Slides for introduction are available.
4. (Sep 24) Slides for digital image fundamentals are available.
5. (Sep 27) Slides for binary image analysis are available.
6. (Oct 4) Slides for linear filtering are available.
7. (Oct 11) Homework assignment 1 is available.
8. (Oct 11) Slides for edge detection are available.
9. (Oct 18) Slides for local feature detectors are available.
10. (Oct 30) Slides for color image processing are available.
11. (Oct 30) Slides for texture analysis are available.
12. (Nov 8) Slides for image segmentation are available.
13. (Nov 10) Homework assignment 2 is available.
14. (Nov 12) Slides for representation and description are available.
15. (Nov 22) Slides for pattern recognition are available.
16. (Dec 10) Homework assignment 3 is available.
17. (Dec 11) Slides for image classification and object recognition are available.
18. (Dec 11) Slides for image retrieval are available.
19. (Dec 28) Project description is available.

Personnel

Instructor:	Selim Aksoy (Office: EA 422, Email: )
TA:	Hüseyin Gökhan Akçay (Office: EA 425, Email: akcay[at]cs.bilkent.edu.tr)

Course Information

Schedule:	Tue 13:40-15:30, Thu 15:40-17:30 (EB 102)
Office hours:	Selim Aksoy (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, image classification, and object recognition. 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.
Syllabus:	Make sure you read the syllabus for course details.

Texts

• L. G. Shapiro and G. C. Stockman, Computer Vision, Prentice Hall, 2001.
• R. Szeliski, Computer Vision: Algorithms and Applications, Springer 2010. (local copy)
• R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd edition, Prentice Hall, 2008.
• 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 ] (Sep 18, 20)	Topics: Overview Example applications
Digital Image Fundamentals [ Slides: pps | pdf ] (Sep 25, 27)	Topics: Acquisition, sampling, quantization Image enhancement Image formats Linear algebra and MATLAB review Readings: SS Ch 1, 2 GW Ch 1, 2, 3.1-3.4 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 Matlab code examples for histogram enhancement
Binary Image Analysis [ Slides: Part 1 (pps | pdf) | Part 2 ] (Oct 2, 4)	Topics: Pixels and neighborhoods Mathematical morphology Connected components analysis Automatic thresholding Readings: SS Ch 3.1-3.5, 3.8 GW Ch 2.5, 9.1-9.5, 10.3 References: M. Sezgin, B. Sankur, "Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation" (local copy), Journal of Electronic Imaging, 13(1):146-165, January 2004. Software: Matlab code examples for binary image analysis Mathematical morphology demos
Linear Filtering [ Slides: Part 1 (pps | pdf) | Part 2 (pps | pdf) ] (Oct 9, 11)	Topics: Spatial domain filtering Frequency domain filtering Image enhancement Readings: SS Ch 5.1-5.5, 5.10-5.11 GW Ch 3.5-3.8, 4 Software: Matlab code examples for filtering
Edge Detection [ Slides: pps | pdf ] (Oct 16, 18)	Topics: Edges, lines, arcs Hough transform Readings: SS Ch 5.6-5.8, 10.3-10.4 GW Ch 10.1-10.2 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
Local Feature Detectors [ Slides: pps | pdf ] (Oct 30, Nov 1)	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 N. Snavely, S. M. Seitz, R. Szeliski, "Photo tourism: Exploring photo collections in 3D" (local copy, web page), ACM Transactions on Graphics (SIGGRAPH Proceedings), 25(3):835-846, 2006. I. Simon, N. Snavely, S. M. Seitz, "Scene Summarization for Online Image Collections" (local copy, web page), IEEE International Conference on Computer Vision, October 14-20, 2007. 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 VLFeat open source library Aperture problem demonstration
Color Image Processing [ Slides: pps | pdf ] (Nov 6)	Topics: Color spaces and conversions Readings: SS Ch 6.1-6.5 GW Ch 6
Texture Analysis [ Slides: pps | pdf ] (Nov 8, 13)	Topics: Statistical approaches Structural approaches Readings: SS Ch 7 GW Sec 11.3.3
Image Segmentation [ Slides: pps | pdf ] (Nov 15, 20)	Topics: Histogram-based approaches Clustering-based approaches Region growing Split-and-merge Morphological approaches Graph-based approaches Readings: SS Ch 10.1 GW Ch 10.4-10.5 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. Y. Deng, B. S. Manjunath, "Unsupervised Segmentation of Color-Texture Regions in Images and Video" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(8):800-810, August 2001. 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 L. Vincent, P. Soille, "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(6):583-598, June 1991. C. Vachier, F. Meyer, "The Viscous Watershed Transform" (local copy), Journal of Mathematical Imaging and Vision, 22(2-3):251-267, 2005. 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
Representation and Description [ Slides: pps | pdf ] (Nov 22, 27)	Topics: Image representations and descriptors Region representations and descriptors Readings: SS Ch 10.2, 3.7 GW Ch 11 References: D. Zhang, G. Lu, "Review of Shape Representation and Description Techniques" (local copy), Pattern Recognition, 37(1):1-19, January 2004.
Pattern Recognition Overview [ Slides: Part 1 | Part 2 ] (Dec 4, 6)	Topics: Brief introduction to pattern recognition Readings: SS Ch 4 GW Ch 12.1-12.2 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
Case Studies [ Slides: Part 1 (pps | pdf) ] | Part 2 (pps | pdf) ] (Dec 11, 13, 18, 20, 27)	Topics: 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, 29(9):1575-1589, September 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 Visual object recognition tutorial by Leibe and Grauman at AAAI 2008 (local copy)

Exams

• Midterm exam will be held at EB 102 during 15:40-17:30 (class hours) on November 29, 2012. 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

1. Homework assignment 1: description | data (Due: October 31, 2012 as online submission)
2. Homework assignment 2: description | data (Due: November 28, 2012 as online submission)
3. Homework assignment 3: description | data (Due: December 28, 2012 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 an object recognition system based on clustering of segments represented using the bag-of-words model.

• Project description and data 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 23:59 on January 13, 2013. No late submission is allowed for project reports.
• The reports are expected to be around 6 pages and must follow the IEEE Computer Society two-column format as described in their templates. Try to follow the format as closely as possible.

Grading Policy

Homework:	35%
Quiz:	10%
Exam:	25%
Project:	25%
Class participation:	5%

Related Links

• Previous semesters for CS 484
  • Spring 2012
  • Spring 2011
  • Spring 2010
  • Spring 2009
  • Spring 2008
  • Spring 2007
• Shapiro and Stockman book
  • Book's website at UW
  • Book's website at MSU
• Szeliski book
  • Book's website
• Gonzales and Woods book
  • Book's website
• Forsyth and Ponce book
  • Book's website
• Ballard and Brown book
  • Book's website
• Software resources
• Matlab tutorials
  • Matlab Primer v2
  • Matlab Tutorial by Indiana University
  • Matlab Tutorial by the University of New Hampshire
  • Matlab Tutorial by MIT
  • Matlab Tutorial by the University of Washington
  • Matlab Tutorial by Gonzales and Woods
  • MathWorks - Matlab Tutorial
  • MathWorks - Student Center
  • MathWorks - Image Processing Toolbox (function list)
• Data resources
• Other image analysis / computer vision courses
• Others
  • Industrial applications of computer vision

Please send questions/suggestions to .

URI for this page is http://www.cs.bilkent.edu.tr/~saksoy/courses/cs484-Fall2012/index.html.
Last updated on December 30, 2012.