1 / 5

CMPUT 617 (Topics in Computing Science): Advanced Image Analysis

This course focuses on advanced image analysis and computer vision, emphasizing both low-level and high-level tasks. Low-level tasks include edge detection, segmentation, feature extraction, and object tracking, utilizing graph algorithms for enhanced performance and scalability. High-level tasks involve visual recognition, face detection, and action recognition, often building on low-level techniques. The course will also discuss commercial applications, such as medical image processing and software like Photoshop. Graph algorithms will be analyzed for their potential to provide globally optimal solutions in image processing.

sonel
Download Presentation

CMPUT 617 (Topics in Computing Science): Advanced Image Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related