Course Name: Computer Vision Course Code: IT444

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1 Course Name: Computer Vision Course Code: IT444 I. Basic Course Information Major or minor element of program: Major Department offering the course:information Technology Department Academic level:400 Level Semester in which course is offered: First (Fall) Semester Course pre-requisite(s): Digital Signal Processing [IT 341] Credit Hours:3 Contact Hours Through: Lecture Tutorial * Practical * Total * 1.5 hours for either Tutorial or Practical Approval date of course specification: September 2014 II. Overall Aims of Course The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include identifying the 3D shape of an environment, determining how things are moving, and recognizing familiar people and objects, all through analysis of images and video. This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. III. Program ILOs covered by course Program Intended Learning Outcomes (By Code) Knowledge & Intellectual Professional Understanding General K1,K11,K17,K19 I9,I15,I19 P12,P13,P14,P19 G2,G6,G8 Computer Vision 1

2 IV. Intended Learning Outcomes of Course (ILOs) a. Knowledge and Understanding K.1 Define what computer vision is. K.2 List low-level image analysis methods, including image formation, edge detection, feature detection, and image segmentation. K.3 Define Image transformations (e.g., warping, morphing, and mosaics) for image synthesis. K.4 Recognize methods for reconstructing three-dimensional scene information using techniques such as depth from stereo, structure from motion, and shape from shading techniques such as depth from stereo, structure from motion, and shape from shading. K.5 Recognize motion and video analysis principles K.6 List three-dimensional object recognition principles. b. Intellectual/Cognitive I.1 Enhance the ability to choose the appropriate technique for a given problem. I.2 Construct the detailed computational procedures for computer vision. I.3 Develop computer vision software techniques. I.4 Analyze and test computer vision methods in real world applications. c. Practical/Professional P.1 Use different binary image analysis and segmetation techniques. P.2 Demonstrate various image processing procedures for feature detection and image synthesis. P.3 Demonstrate techniques to recover or reconstruct physical properties from one or more images. P.4 Use methods for reconstructing three-dimensional scene information using techniques such as depth from stereo, structure from motion, and shape from shading. P.5 Use different object recognition techniques. d. General and Transferable G.1 Improve oral communication skills through project presentations. G.2 Improve team working skills. G.3 Learn by reading advanced textbooks and research literature in computer vision domain. Computer Vision 2

3 V. Course MatrixContents Main Topics / Chapters Duration (Weeks) Course ILOs Covered by Topic (By ILO Code) K & U I.S. P.S. G.S. 1- Introduction, images and imaging 1 K1 P3 2- Binary Image Analysis 2 K1 I1 P1,P3 G3 3- Filtering and smoothing 2 K1,K2 I1 P1,P2,P3 G3 4- Edge Detection 1 K1,K2 I1 P1,P3 G3 5- Color, Hough transform 1 K3 I1,I2 P3 G3 6-3D geometry, G3 1 K3 I1,I2 P1,P2,P3 calibration 7- Stereo 1 K1,K2,K3 I1,I2 P1,P2,P3 G3 8- Optical Flow, Motion 1 K3,K4 I1,I2 P1,P3 G3 9- Structure from G3 1 K4 I1,I2 P1,P2,P3 motion, shape from X 10- Recognition, Appearance-based matching, Eigenfaces 1 [K4,K5] [I3,I4] [P4,P5] [G3] 11- Final projects, summary 1 [K5,K6] [I3,I4] [P4,P5] [G1,G2,G3] Net Teaching Weeks 13 VI. Course Weekly Detailed Topics / hours / ILOs Contact Hours Week Total Sub-Topics Theoretical Practical No. Hours Hours Hours * 1 Introduction, images and imaging Binary Image Analysis Binary Image Analysis Filtering and smoothing Filtering and smoothing Edge Detection Color, Hough transform D geometry, calibration Stereo Optical Flow, Motion Structure from motion, shape from X Recognition, Appearance-based matching, Eigenfaces Final projects, summary Final Exam Total Teaching Hours * No Practical/Tutorial during the first week of the semester Computer Vision 3

4 VII. Teaching and Learning s Teaching/Learning Selected Faculty of Computers and Information K & U Course ILOs Covered by (By ILO Code) Intellectual Professional General Lectures & Seminars x K1:K6 I1,I2 G1 Tutorials Computer lab x I3,I4 P1:P5 G1,G2 Sessions Practical lab Work Reading Materials x K1 I4 P3,P5 G3 Web-site Searches x K1 I4 P3,P5 G2,G3 Research & Reporting Problem Solving / Problem-based Learning x I2 P5 G1,G2 Projects Independent Work x K1 I2 P1:P3 G3 Group Work x I3,I4 P4,P5 G1,G2 Case Studies Presentations Simulation Analysis Others (Specify): VIII. Assessment s, Schedule and Grade Distribution Assessment Selected Course ILOs Covered by (By ILO Code) K & U I.S. P.S. G.S. Assessment Weight / Percentage Week No. Midterm K1:K3 x Exam I1,I2 10% 7 Final Exam x K1:K6 I1,I2 60% 14 Quizzes x K1,K2 4% 5 Course Work x I1,I2 G3 6% 8 Report Writing x I4 G1,G2,G3 5% 9 Case Study Analysis Oral Presentations Practical x I3,I4 P1:P5 G2 5% 10 Group Project x G1,G3 `10% 12 Individual Project Others (Specify): Computer Vision 4

5 IX. List of References Essential Text Books Computer Vision: A Modern Approach, by David A. Forsyth & Jean Ponce, Prentice Hall, Ed. 1, Course notes Lecture Slides Introductory Techniques for 3-D Computer Vision, by Emanuele Trucco, Alessandro Verri, Prentice-Hall, A Guided Tour of Computer Vision, by V. S. Nalwa, Addison- Recommended books Wesley, Multiple View Geometry, by Richard Hartley, Andrew Zisserman, Cambridge University Press, Pattern Classification and Scene Analysis, by Richard O. Duda, Periodicals, Web sites, etc X. Facilities required for teaching and learning List the facilities required Computers and matlab program Course coordinator:prof. Reda Abd el-wahab Head of Department:Prof. Reda Abd el-wahab Date: September 2014 Peter E. Hart, John Wiley & Sons, None Computer Vision 5