Fundamentals of Computer Vision and Machine Learning

Course lecturer: prof. dr. eng. Constantin VERTAN
Laboratory assistant: prof. dr. eng. Corneliu FLOREA

This is a two-part course, structured, as the title suggests, into the presentation of the fundamentals of computer vision and machine learning. The first part of the course presents the basic challenges for computer vision systems, briefly establishes the fundamental low-level image processing operations and continues toward mid-level vision (corners, line detection, contour grouping, matching of key-points, image descriptors) and presents a transition application between medium-level and high-level-vision, namely content-based image retrieval. The end of part one discusses segmentation as basic requirement for deep convolutional neural networks that should be learned, thus establishing the transition to the second half of the course. Machine learning discusses the main approaches in machine learning for various visual-based applications, with a hand-on project dealing with the construction, training and testing of a deep covolutional structure that solve a human face related task.