ELEC 631 - Advanced Digital Signal Processing: Machine Learning for Image Analysis - Spring 2013
This course will explore how modern machine learning techniques are revolutionizing image analysis and recognition.
Topics to be discussed include: Image representations (wavelets, scale space, etc.), image features and descriptors (SIFT, etc.), classification and recognition frameworks (bag-of-features, etc.), and applications in digital photography, Google Goggles, etc.
Duncan Hall 2028, 713-348-5132, firstname.lastname@example.org
Location: 1049 Duncan Hall
Time: Friday 2-4pm
This is a "reading course", meaning that students will read classic and recent papers and present to the rest of the class in a lively debate format. Students will also complete a major group project. The course is open to graduate students from any department with some background in signal processing, image processing, or machine learning.
Class grade will be based on:
- Class participation (15%)
- Paper presentations (30%)
- Paper summaries (prepared each week, 15%)
- Group project (40%)
Weekly Schedule (Tentative)
- 11 January 2013: Orientation
- 18 January 2013: Scale space
- 25 January 2013: Perona-Malik pre-attentive filters
- 11 January 2013: Lucas-Kanade feature point detector and tracker
- 1 February 2013: Canny edge detector and relatives
- 8 February 2013: SIFT
- 22 February 2013: SIFT relatives
- 1 March 2013: Spring Break (no class)
- 8 March 2013: Viola and Jones face detector
- 15 March 2013: Bag-of-words model for textures and natural scenes
- 22 March 2013: Object localization
- 29 March 2013: Midterm Recess (no class)
- 5 April 2013: Phototourism
- 12 April 2013: Manifold learning
- Date TBD: Guest lectures: Ashok Veeraraghavan on "TBD" and Peyman Milanfar on "Google Glass"
- Late April 2013: Group Project Presentations