| MUET / Departments / Computer Systems & Software Engineering / Course of SW /Computer Vision |  |
COMPUTER VISION Theory (100) Practical (50) INTRODUCTION Background, History of Computer Vision, Images, Representation and Elements of Processing. DIGITAL IMAGE FUNDAMENTALS An Image model, Sampling and quantization, Basic relationships between pixels. RELATIONSHIPS BETWEEN PIXELS Neighbors of Pixels, Connectivity, Distance measures IMAGING GEOMETRY Translation, Scaling, Rotation, Perspective Transformation, Camera Model, Camera Calibration, Recovering Camera Parameters. EDGE DETECTION Types of Edge Detection, Three Stages in Edge Detection, Filtering Stage, Differentiation Stage, Detection Stage, Classes of Edge Detection, Gradient Operators, Facet Model, Laplacian of Guassian Operator, Properties of Gaussian, Scaling, Separability, Symmetry, canny’s Edge Detector REGION SEGMENTATION Definition of Region Segmentation, Simple Segmentation, Threshold and Histogram, Peakiness Test, Connected component Algorithm, Recursive Algorithm, Sequential Algorithm. IMAGE ENHANCEMENT Background, Image enhancement by Histogram technique. IMAGE RESTORATION, IMAGE ENCODING Introductions NOTE: PRACTICAL WILL BE BASED ON THEORY RECOMMENDED BOOKS [ 1 ] R. C Gonzalez and P. Wintz, “Digital Image Processing” [ 2 ] Roman Kuo, “Introduction to Digital Signal Processing” [ 3 ] Dr. Mubarak Ali Shah, “Computer Vision Course”.
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