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Course Description Course Name : Image and Vision Computing Course Code : CENG-537 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. MUSTAFA ORAL Learning Outcomes of the Course : identies the theoretical issues of CV: edge detection, texture, stereopsis, template matching etc. Classifies 2-D and 3-D image representation Operates image quantization methods Simulates 2-D image transformation methods Designs image improvements and analyzes methods using simulation media Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Digital images, sampling and quantization of images. Arithmetic operations, gray scale manipulations, distance measures, connectivity, image transforms, image enhancement, image restoration and image segmentation. Course Contents : Digital image fundamentals; representation, color concepts, image transform algorithms; 2D Fourier transform, 2D discrete cosine transform, image halftoning, quantization, image compression; Huffman coding, LZW compression, edge detection algorithms, image segmentation algorithms, shape description; polygonal approximation, moment descriptors, thinning, skeletons and mathematical morphology, motion analysis and principles of watermarking. Language of Instruction : English Work Place : Room 1   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Natural eyes visual properties. Reading the lecture notes Lectures and Demonstration 2 Image processing in MATLAB, 2-D and 3-D image represantation. Reading the lecture notes Lectures and Demonstration 3 Digital image characterizations. Reading the lecture notes Lectures and Demonstration 4 Image sampling and reconstructions. Reading the lecture notes Lectures and Demonstration 5 Digital image mathematical representation. Reading the lecture notes Lectures and Demonstration 6 Image quantization. Reading the lecture notes Lectures and Demonstration 7 2-D transformations; image convolution and fourier transforms. Reading the lecture notes Lectures and Demonstration 8 Midterm Exam Reading the lecture notes In class written exam 9 2-D transformations; sine and cosine image transformation. Reading the lecture notes Lectures and Demonstration 10 2-D transformations; FFT and filtering of image Reading the lecture notes Lectures and Demonstration 11 Image Improvements; image enhancement, image restoration. Reading the lecture notes Lectures and Demonstration 12 Image Improvements, geometrical modifications Reading the lecture notes Lectures and Demonstration 13 Image Analysis; morphological image processing, edge detection. Reading the lecture notes Lectures and Demonstration 14 Image Analysis; feature extraction, image segmantation. Reading the lecture notes Lectures and Demonstration 15 Application Presentation Reading the lecture notes, Prepare Applications Lectures and Demonstration 16/17 Final Exam Reading the lecture notes In class written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Digital Image Processing by R. Gonzalez and R. Woods, 3rd edition, Prentice Hall, 2008  Computer Vision and Image Processing: A Practical Approach Using CVIPtools , by S. Umbaugh, Prentice Hall, 1998.  Digital Image Processing, by K. Castleman, Prentice Hall, 1996.   Image Processing: The Fundamentals, by M. Petrou and P. Bosdogianni, John Wiley, 1999. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 50     Homeworks/Projects/Others 1 50 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. 5 2 Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. 5 3 Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them 5 4 Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. 4 5 Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. 3 6 Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering 3 7 works in multi disciplinary teams and takes a leading role and responsibility. 1 8 Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written 2 9 Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. 3 10 Describes social and environmental implications of engineering practice. 2 11 Considers social, scientific and ethical values in collection, interpretation and announcement of data. 4 12 Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. 5 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 13 3 39     Out of Class Study (Preliminary Work, Practice) 13 3 39 Assesment Related Works     Homeworks, Projects, Others 1 20 20     Mid-term Exams (Written, Oral, etc.) 1 20 20     Final Exam 1 30 30 Total Workload: 148 Total Workload / 25 (h): 5.92 ECTS Credit: 6