**CSc 471 Computer Vision- Fall 2018**

**Instructor:** Professor Zhigang Zhu

**http://www-cs.engr.ccny.cuny.edu/~zhu**

**Section: EF Code: 28430 Credits**: **3.0**

**Class Meet Time**: Monday 2:00
- 4:30 PM , **Room**: NAC
6/314

**Office Hours**:
Thursday 2:00 - 4:00 pm,
**Room**: NAC 8/211

**City
College of New York**

**August 27 (Monday), 2018**. First
class meet of this course.

**September
22, 2018**. Grading
for Assignment 1.

**October
21, 2018**. Grading
for Assignments 1 and 2.

Computer vision has a rich history of
fundamental work on stereo and visual motion, which has dealt
with the problems of 3D reconstruction from multiple images, and
structure from motion from video sequences. Recently, in
addition to these traditional problems, the stereo and motion
information presented in multiple images, a video sequence, or
stereo and motion with images of expanded fields of view,
is also being used to solve several other interesting problems,
for example, large-scale scene modeling, video interaction
(including face and gesture interaction), panoramic video
mosaicing, omnidirectional vision-based navigation, and video
surveillance. This is sometimes summarized as video computing.
Computer vision is playing an important and somewhat different
role in solving these problems in video computing than the
original image analysis approach in the early days of vision
research.The course "**Computer Vision**" will include
advanced topics in video computing as well as fundamentals in
stereo and motion.

**Part I. Computer Vision Basics**

I-1. Introduction: What, Why and How (pptx
slides) -08/27

I-2. Image Formation: Digital Image Basics (pptx
slides) (**Assignment 1**)-
09/05 (Wednesday)

I-3. Image Enhancement: point operations, histograms and
neighborhood operations (slides)
(**Assignment 2**)
(** **Lecture
notes in PDF:I-3 and I-4) -09/17

I-4. Edge Detection: basics, advanced, and Hough Transform (slides)
- 09/24

Part II. 3D Computer Vision

II-1. Camera Models (slides)
(lecture
notes in PDF) (**Assignment 3**)
- 10/01

(Geometric Projection of a Camera) -

(Camera Parameters) -

(Camera Models Revisited) -

II-2. Camera Calibration (slides)
(lecture
notes in PDF )
-10/15

(Problem Definition:
the Tools You Must Know),

(Direct Approach: Divide and
Conquer),

(Projective Matrix Approach:
All in One ),

II-3. Stereo Vision (slides)
(lecture
notes in PDF) (**Assignment 4**)
-10/22, 10/29

(Problem Definition)
& **Project
Discussions**,

(Epipolar Geometry),

(Correspondence Problem &
Reconstruction Problem) ,

II-4. Visual Motion - (slides)
(lecture
notes) - 10/29, 11/05

(The Motion Field of Rigid Motion)

(Optical Flow Approach &
Feature-based Approach) **
**

**Part III. Advanced Topics and Projects
**

III-1. Advanced Topics:
Panoramic Cameras (slides)
and Panoramic Stereo (slides)
(Exam Review) - 11/12

III-2. Midterm Exam -11/19

III-3. Advanced Topics: Video Interaction
and Facial Computing (slides)
- 11/26

III-4. Project Discussions and Exam Discussions - 12/03

III-5. Student Group Project Presentations
- 12/10

**Main Textbook: **

* In the form of Lecture Notes and Slides,
provided by the instructor (above).
*

**Reference Textbook:**

- “Computer Vision – A Modern Approach” , David A. Forsyth, Jean Ponce, Prentice Hall, 2003 (ISBN: 0130851981 , 693 pages).
- “Three Dimensional Computer Vision: A Geometric Viewpoint” , Olivier Faugeras, The MIT Press, November 19, 1993 (ISBN: 0262061589 , 695 pages)

**Supplements:**

Online References and additional readings when necessary.

The course will accommodate senior undergraduate
students with background in computer science and computer
engineering. Students who take the course for credits will be
required to **finish 4 assignments (40%), one midterm exam
(40%), and one programming project (20%, including
submit a report and give a small presentation to the class at
the end of the semester)**. The topics of the projects will
be given in the middle of the semester and will be related to
the material presented in the lectures.

Students are required to have a good preparation in both mathematics (linear algebra/numerical analysis) and advanced programming.

Copyright @ Zhigang Zhu , Fall 2018