1. CMPUT 617 (Topics in Computing Science): Advanced Image Analysis Nilanjan Ray Fall 2012 Computing Science University of Alberta

2. Overview Types of image processing/analysis and computer vision: – Low level – High level In this course, we will study some low level tasks in image processing/computer vision Emphasis will be given on graph algorithms to accomplish some of the low level tasks; we will also discuss / compare other types of algorithms

3. Low Level tasks For the lack of a proper definition, I will use some examples: – Edge detection – Segmentation (pixel labeling) – Object boundary delineation – Feature extraction (corners, SIFT, and many others) – Object tracking – Image registration – Optical flow/motion estimation – … Commercial successes – Photoshop – Various medical image processing/analysis software An example: Brain tumor image detection using symmetry

4. High level tasks Again for the lack of a definition, let’s look at some examples: – Visual recognition tasks Face detection/recognition Car detection/recognition Action/gesture recognition Image/video classification – Often the building blocks for a high level task are low level tasks – Commercial successes Face detection Kinect More to arrive… An example: Large lump detection using multiple kernel learning

5. Why graph algorithms? They are the youngest in the family! That’s why more attention to them! Actually, they offer excellent scalability, user interaction and performance. Images are defined on grids, those are themselves graphs, so it is natural to consider graph algorithms. From an ideological perspective, graph algorithms offer globally optimal solutions. However, note that not all the tasks (low or high level) are reducible/convertible to graph algorithms!