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

Announcements

1. (Jan 27) Course page is online.
2. (Jan 27) Syllabus is available.
3. (Jan 31) Slides for introduction are available.
4. (Feb 3) Slides for digital image fundamentals are available.
5. (Feb 9) Slides for binary image analysis are available.
6. (Feb 17) First part of the slides for linear filtering is available.
7. (Feb 18) Homework assignment 1 is available.
8. (Feb 21) Second part of the slides for linear filtering is available.
9. (Mar 3) Slides for edge detection are available.
10. (Mar 13) Slides for local feature detectors are available.
11. (Mar 21) Homework assignment 2 is available.
12. (Mar 21) Slides for color image processing are available.
13. (Mar 23) Slides for texture analysis are available.
14. (Apr 3) Slides for image segmentation are available.
15. (Apr 10) Homework assignment 3 is available.
16. (Apr 17) Slides for representation and description are available.
17. (Apr 25) First part of the slides for pattern recognition are available.
18. (Apr 28) Second part of the slides for pattern recognition are available.
19. (May 4) Slides for image classification and object recognition are available.
20. (May 9) Project description is available.
21. (May 12) Slides for image retrieval are available.

Personnel

Instructor:	Selim Aksoy (Office: EA 423, Email: )
TA:	Hüseyin Gökhan Akçay (Office: EA 130, Email: akcay[at]cs.bilkent.edu.tr)

Course Information

Schedule:	Mon 15:40-17:30, Thu 13:40-15:30 (EB 202)
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 ] (Jan 31, Feb 3)	Topics: Overview Example applications
Digital Image Fundamentals [ Slides: pps | pdf ] (Feb 7)	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: pps | pdf ] (Feb 10, 14)	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) ] (Feb 17, 21)	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 ] (Feb 28, Mar 3)	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 Hough transform demo
Local Feature Detectors [ Slides: pps | pdf ] (Mar 7, 10)	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 ] (Mar 14)	Topics: Color spaces and conversions Readings: SS Ch 6.1-6.5 GW Ch 6
Texture Analysis [ Slides: pps | pdf ] (Mar 17, 21)	Topics: Statistical approaches Structural approaches Readings: SS Ch 7 GW Sec 11.3.3
Image Segmentation [ Slides: pps | pdf ] (Mar 24, 28)	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. D. Comaniciu, P. Meer, "Mean shift: a robust approach toward feature space analysis" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(5):603-619, May 2002. X. Ren, J. Malik, "Learning a classification model for segmentation" (local copy), IEEE International Conference on Computer Vision, 2003. P. F. Felzenszwalb, D. P. Huttenlocher, "Efficient Graph-Based Image Segmentation" (local copy), International Journal of Computer Vision, 59(2):167-181, September 2004. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi, "TurboPixels: Fast Superpixels Using Geometric Flows" (local copy), IEEE Trans. on Pattern Analysis and Machine Intelligence, 31(12):2290-2297, December 2009. 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 EDISON: mean-shift segmentation by Rutgers Superpixel segmentation by Greg Mori Graph-based segmentation by Pedro Felzenszwalb Turbopixels algorithm for superpixel segmentation by Alex Levinshtein
Representation and Description [ Slides: pps | pdf ] (Apr 4, 7)	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 ] (Apr 18, 25)	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) ] (Apr 28, May 2, 5, 9, 12)	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

Exams

• Midterm exam will be held at EB 202 at 15:40-17:30 (class hours) on May 2, 2011. 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: March 9, 2011 as online submission)
2. Homework assignment 2: description | data (Due: April 8, 2011 as online submission)
3. Homework assignment 3: description | data (Due: May 3, 2011 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 that finds matches of known objects in new images.

• Project description is available.
• As indicated in the description, there is a deadline for data collection at 23:59 on May 14, 2011. Make sure you read the requirements for that deadline carefully, and submit the required data as a single archive file (e.g., zip, tar, rar) using the online form.
• 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 May 27, 2011. 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 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-Spring2011/index.html.
Last updated on May 12, 2011.