Digital Image Processing and Analysis

Course: Digital Image Processing and Analysis

Code: 3ФЕИТ05023

ECTS points: 6 ECTS

Number of classes per week: 3+0+0+3

Lecturer: Prof. Dr. Zoran Ivanovski

Course Goals (acquired competencies): Upon completion of the course the student will be able to: - understand image formation, acquisition and representation, the role of sampling and quantization in digital images, image transformations and human visual system; - understand contemporary methods of image processing, image enhancement, restoration, and color processing; - use and apply techniques for preprocessing, advanced techniques for image segmentation, and techniques form image representation and shape and texture description;  - consider and evaluate applicability of image processing and analysis techniques for practical problem solving;  - follow the advances and new discoveries in image processing and analysis and to assess its applicability in different areas;        - assess and evaluate the utilization of image processing/analysis systems as parts of information processing systems; - select and describe evaluation criteria for image processing/analysis systems.

Course Syllabus: Image digitalization and properties. Data structures form image processing and analysis.   Image preprocessing: transformation of luminance, geometric transformations, local processing, restoration. Advanced segmentation techniques: thresholding, border detection, border and area detection based on optimality criteria.  Shape representation and description: region identification, shape representation and description based on contours, region based shape representation and description, shape classes. Object recognition: knowledge representation, statistical and syntactic pattern recognition, optimization based recognition.  Mathematical morphology: basic principles and morphological transforms, topological processing. Textures: statistical texture description, syntactic texture description, hybrid methods, application of texture recognition. Motion analysis: differential methods, optical flow, motion analysis based on POI.

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