M2R - MSc in Informatics

Option: Graphics Vision and Robotics

Computer Vision 

Professors: James L. Crowley, Edmond Boyer, Peter Sturm

Lesson 1:   Projective Geometry (Edmond Boyer)

Lesson 2:  Visual Perception in Man and Machine (James Crowley)

Lesson 3: Detecting and Tracking Objects with Color (James Crowley)

• Probabilistic Detection of Color
• Bayesian Tracking of Blobs
  
• The Kalman Filter
  

30 october 2014  Fall Vacation (Toussaint) 

Lesson 4:  Describing Images with Contrast Contours  (James Crowley)

• Describing Images with Contrast
• Edge Detection
  
• Image smoothing with Binomial Filters
  
• The Hough Transform
  

Lesson 5: Describing Local Appearance with Gaussian Derivatives  (James Crowley)

• Beyond Edges - Describing Local Appearance
• Image Description Using Gaussian Derivatives
• Designing Gaussian low-pass digital filters
• Using the Gaussian to compute image derivatives

Lesson 6: Scale Invariant Pyramids, HoG, SIFT and Haar   (James Crowley) 

Midterm Exam (50% of final grade)

Lesson 8:   Calibration and Image Mosaics (Peter Sturm)

Lesson 9: Structure from Motion (Peter Sturm)

Lesson 10: Structure from Motion (Peter Sturm)

Lesson 11: Shape Models (Edmond Boyer)

Lesson 12: Articulated Motion (Edmond Boyer)

Exercise Problem Sets (Do these at home or in a group)

Past Exams