Analysis, Recognition and Classification of Images by Artificial Intelligence Methods

Major: Software Engineering
Code of subject: 8.121.00.M.025
Credits: 3.00
Department: Software
Lecturer: professor Melnyk Roman Andriyovych
Semester: 4 семестр
Mode of study: денна
Learning outcomes: Develop and evaluate strategies of software design; substantiate, analyse and assess design solutions from the perspective of the final product quality, resource restrictions and other factors. Analyse, assess and systematically apply modern software and hardware platforms for solving complex problems in software
Required prior and related subjects: Corequisites: Data Mining
Summary of the subject: Technologies for retrieving images and their application. Classification of visual images. Software for forming and processing visual images. Image reproduction. Noise models. Noise filtering. Filtering in the frequency domain. Shape and color features. Color conversion, smoothing and contrasting. Segmentation of color. Models of lossless and lossy compression. Compression standards. Wavelets and Fourier functions. Key algorithms: skeleton, contours. 3-D images and their description. Segmentation of images. Methods of segmentation analysis: advantages and disadvantages. Decomposition of graphs. Boundaries, thresholds, expanding areas. The hierarchical decomposition. Indexing and searching. Class features extraction methods. Clustering features. Storing and searching in databases. Pattern recognition. Basic and advanced features. Criteria for comparison, similarity functions. Neural network structures: learning and searching. Classification of medical images. Fingerprints and facial features. Modern identification systems. Robotics, multimedia and computer vision application in robotics and
Assessment methods and criteria: Current control (45%): written reports on laboratory work (30%), independent work (15%) Final control (55%, exam), testing (50%), oral examination - (5%)
Recommended books: 1. Методичний посібник «Методи та алгоритми опрацювання зображень». Видавництво «Львівська політехніка», 2017 р. 220 стор. 2. Методичні вказівки до виконання лабораторних робіт. 3. Завдання до виконання самостійних пошукових робіт 4. Д'яконов В.Р, Авраменкова И.А. Обработка сигналов и изображений : специальный справочник. - СПб. : Питер, 2002. - 608 с. 5. Digital Image processing. R. C. Gonzalez, R.E.Wood . Prentice-Hall, Inc. Upper Saddle River, New Jersey, 2002 6. Face Recognition. Edited by Milos Oravec, In-teh, 2010.