CPE 631 (2009) / CPE 488
Machine Vision
Computer Engineering Department
King Mongkut's University of Technology, Thonburi
Course Information
Instructor:
Website:
Time:
Office Hours:
TA.:
Office Hours:
Suthep Madarasmi, Ph.D. (suthep@kmutt.ac.th)
www.cpe.kmutt.ac.th/~suthep/cpe631
Friday 17:00-19:00, CB 40805
Friday 16:00 – 17:00
Ms. Varin Chouvatut (varin@cpe.kmutt.ac.th)
Thursdays 13:30 – 14:30 at Sigma Lab.
Course Overview
To introduce students to the concepts of machine vision touching on areas of
computer graphics, image processing, artificial intelligence, biological vision,
neural networks, pattern recognition and robot vision. The course will be
project-oriented consisting of a lecture to introduce the subject matter followed
by discussions on computer vision applications and assignments to learn the
subject matter.
Computer vision can be viewed as the inverse problem of
computer graphics: the objective of computer graphics is to generate images
using a model of the world whereas the objective of computer vision is to arrive
at a description of the world using images. Image processing will be covered
extensively with topics such as edge finding, image enhancement, image
segmentation, and clustering. Linear, non-linear, and stochastic optimization
methods will be introduced for use in solving computer vision problems. The
inverse optics problem in computer vision will be discussed including stereo
vision, shape from shading, and other Shape from X algorithms. Finally, we will
discuss several algorithms for image understanding such as scene interpretation,
object recognition, and face recognition.
Assignments
1. Simple Thai OCR Competition: Correlation, edge detection, thinning, image
segmentation, and template matching. Undergraduates can form groups of 2
persons for the assignment. Graduates must do individually. (15%)
2. Term project will be on a computer vision topic of choice requiring:
 Proposal: 1-2 pages and 5 minutes presentation.
 Project: 20 minutes presentation for project work, 30 minutes for
literature review lecture.
Text
Jain R., R. Kasturi, and B.G. Schunck, Machine Vision, McGraw-Hill.
Course Topics
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Overview of Computer Vision
Course and Assignments Overview
Image Formation and Sensing
3-D Computer Graphics and Visual Realism
Digital Images: bw, grayscale, and color
Binary Image Processing: Low-level
Image Filtering and Edge Detection
Regions, Image Segmentation, Texture Segmentation
Blob Coloring
Contours and Boundary Detection
General Hough Technique and Applications
3-D Computer Graphics Models Revisited
Optics, Curves and Surface
Optimization: Pseudo-Inverse, Hough Technique.
Optimization: Energy Minimization
Gradient Descent, Image Cleanup
Back Propagation Neural Networks
Computing Optical Flow
Optimization: Bayesian Probability, Pixel Lattice
Gibbs Sampler and Simulated Annealing
Depth from Stereo Vision
Optimization: Genetic Algorithms
Optimal Material Consumption
Depth & Shape from X, Texture, Contour, Stereo
Calibrations, Structure from Motion, Object Tracking
Shape from Shading
Camera Pose Estimation and Augmented Reality
Object Recognition Models
Grading
Programming Assignment 1
Assignment 2 (Term Project)
Best 9 of 11 Quiz
15%
20%
65%