EE5731 Visual Computing

Electrical and Computer Engineering, NUS





Description:

The goal of computer vision is to make computers work like human visual perception, namely, to understand and recognize the world through visual information, such as, images or videos. Human visual perception, after millions of years of evolution, is extremely good in understanding and recognizing objects or scenes. To have similar abilities to human visual perception (or beyond), computer vision scientists have been developing algorithms by relying on various visual information, and this course is about some of these algorithms. In case you are wondering why we should care about computer vision, consider this: if you think your visual perception system is important and beneficial, so is computer vision.


Textbooks: "Computer Vision: Models, Learning, and Inference", by S.J.D. Prince | "Multiple View Geometry", by R. Hartley and A. Zisserman | "Computer Vision: Modern Approach", by Forsyth and Ponce
Instructor: Robby T. Tan

Teaching Schedule:

The following schedule will not be strictly followed.


Date
Topic
Lecture Note
August 14 1. INTRODUCTION + IMAGE FORMATION

Reading: Additional Resources: 		Lecture note 1

August 21 2. FACE DETECTION

Reading: 		Lecture note 2
August 28 3. IMAGE FEATURES: HOG

Reading:
  • "Computer Vision" textbook Chapter 13: Image Preprocessing and Feature Extraction, particularly Sec. 13.1 (per-pixel transformations), and Sec. 13.3.3 (Histogram of Oriented Gradients)
Additional Resources: 		Lecture note 3
September 4 4. IMAGE FEATURES: SIFT

Reading: Additional Resources: 		Lecture note 4
September 11 5. IMAGE STITCHING

Reading:
  • "Computer Vision" textbook: Chapter 14: Sec. 14.3 (Homogeneous Coordinates),
  • "Computer Vision" textbook: Chapter 15, Sec. 15.1 (2D transformation models), Sec. 15.5 (transformation between images), Sec. 15.6 (robust learning of transformations), Sec. 15.7 (applications)
  • RANSAC: wikipedia
Additional resources:
  • Homography: wikipedia
  • Automatic Panoramic Image Stitching Using Invariant Features: pdf
  • Image Alignment and Stitching: A Tutorial: pdf

Lecture note 5
Assignment 1
September 18 6. CAMERA GEOMETRY

Reading:
  • "Multiple View Geometry" textbook: Chapter 6 (Camera Models): Sect.6.1 (finite cameras), Chapter 7 (Computation of the Camera Matrix): Sect. 7.1 (basic equations)
  • "Multiple View Geometry" textbook: Chapter 9 (Epipolar Geometry and Fundamental Matrix): Sect. 9.1 (Epipolar Geometry), Sect. 9.2 (The Fundamental Matrix)
Additional resources:
  • "Computer Vision" textbook chapter 14 (Pinhole Camera): sect. 14.1 (the pinhole camera), sect. 14.4 (learning extrinsic parameters), sect. 14.5 (learning intrinsic parameters)


Lecture note 6
September 26 RECESS WEEK

FINAL EXAM





Syllabus: