Visual Studio Code - Insider Extension:Jupyter Virtual envrionment: Python3.7.9-64-bit ('cv-env':conda)
Graphics Vision and Robotics and Ubiquitous Interface Systems
- Computer Vision
- MoSIG M2 2020-2021 Academic Year
- ENSIMAG Amphi D - Thursdays 9:45 to 12:45
- Professors: James L. Crowley and Edmond Boyer
- Teaching Assistant: Nachwa Aboubakr
These class notes can be found at http://crowley-coutaz.fr/jlc/Courses/2020/GVR.VO/GVR-VO.html
The original planned Class schedule. Here is a pointer the the ADE class reservation system Programming Teams (as of 15 October).
Lesson 1: Theory: Performance Evaluation for Recognition and Detection Course Introduction: James Crowley
- Course Organisation Computer Vision Theory: (Nachwa Aboubakr)
- Pattern Recognition and Machine Learning
- Performance Evaluation Evaluation Metrics Exercise Questions (exam questions from past years) Practical Instruction: Jupyter Notebooks, OpenCV, and FDDB.
- Using OpenCV and Keras in Python with Jupyter Notebooks
- Opening and displaying a face with the FDDB Data set
Programming Exercise 1: Displaying Faces from the FDDB data set Background Reading: The FDDB Data Base (Jain and Learned-Miller 2010)
Lesson 2: Visual Perception in Man and Machine (Recorded Lecture)
Computer Vision Theory (Recorded Lecture for Part 1)
- Albedo and Reflectance
- The Human Visual System
- Vergence, Version and Fixation
- Color Perception and Color Spaces Exercise Questions (exam questions from earlier years)
Practical Instruction (Recorded Lecture for Part 2)
- Sliding Window Face Detectors
- Programming Neural Networks in Keras
- Detecting Faces with a 3 Layer MLP in Keras
Programming Exercise 2: Face Detection with a Multi-Layer Percetron Evaluation Data for Exercise 2 (from folds 9 and 10 of FDDB) Background Reading: (Rowley and Kanade 87)
Lesson 3: Scale Space and Image Pyramids (Recorded Lecture) Computer Vision Theory
- Scale Space
- Gaussian function as a low-pass digital filter
- Scale Invariant Gaussian Pyramids
- Equivariance Properties of Scale Space
Practical Instruction:
- Constructing an Image pyramid with OpenCV
- Detecting Faces at multiple scales with a pyramid
Programming Exercise 3: Detecting Faces at multiple scales with a pyramid and a sliding window MLP
Background Reading: Face Detection with Half octave Pyramid (Ruiz 2008) (Crowley-Riff 2003)