ECE516 (ECE516H1S): Intelligent Image Processing

Labs and authentic direct mentorship

The most important part of this course is the labs which offer authentic direct mentorship with a high degree of involvement from the professor and other leading experts in imaging, sensing, meta-sensing, and human machine learning.

Our goal for the undergraduates is to help you get into grad school at MIT (Prof. Mann's alma mater) or Stanford, or to build the skills you need to found a great startup or be the world's leader in your chosen field, and for graduate students, finding a great thesis topic.

Lab topics:

  1. Fourier transform, wavelet transform, and chirplet transform;
  2. Machine learning for computer vision: Radar Vision and LEM neural network (world's first transform with machine learning built-in);
  3. Biosignals and biosensing. In this lab we can build an ultrasound system to image the heart. [Analysis of Seismocardiographic Signals Using Polynomial Chirplet Transform...].
  4. Brain-Computer Interfaces (InteraXon company co-founded by Mann and his students));
  5. Fluid User Interfaces: Build a musical physiotherapy machine based on an array of ultrasonic lock-in amplifiers for phase-coherent sonar;
  6. See and photograph sound waves, radio waves, and light waves using your lock-in amplifier.
  7. Passive vision: Many courses on computer vision fail to teach the fundamental concepts of what sensing is and does. We'll begin with fundamental principles by exploring first a 1-pixel camera and 1-pixel display, quantigraphic (quantifiable) sensing, and meta-sensing.
  8. Understanding 3 phase motors and electric vehicles;
  9. Build your own autonomous e-vehicle...
  10. Complex-Valued Signal Generators
  11. Build a signal generator that produces a complex-valued output. You will fundamentally understand the difference between positive and negative frequencies and be able to explain that difference to a 5-year old child! In later labs you will use this signal generator as the foundation upon which to build autonomous electric vehicles!
  12. Phase-coherent detection for active computer vision:

Lab schedule:

Lab 1. What is a camera?
       .Pinhole camera (effect of aperture size),
       .Mathematical models tan(arctan())...,
       .Lens=optional part of lab.
        (easy to make from household items).

Lab 2. (Chapter 4 of text) Comparametric Equations (HDR = High Dynamic Range)
       .Photocell experiment or use laptop computer camera, webcam, or the like,
       .Comparagrams and comparagraphs,
       .Optional: compositing of images; CCRF.

Lab 3. (Chapter 5 of text) Long-exposure photogaphy, CEMENT, Superposimetrics.

Lab 4. (Chapter 6 of text)
       .Orbits (image stitching),
       .Type I, Type II, and Type III panoramas,
       .VR, AR, XR 'Vironment maps.

Lab 5.  Active vision, radar, sonar, etc.
        .SWIM, sonar (audio) with external microphone or speaker.
Optional additional topics (depending on student interest):

Course instructor: Prof. Steve Mann

TAs: Zhao Lu, Jacky Lau, and Samir Khaki