School of Engineering and Technology, (SET)

AT74.06 : Pattern Recognition and Image Processing 3(2-3)

Rationale:

The field of image processing has grown considerably with increased applications in diverse areas as manufacturing, biology, space and medical. Continuous improvements in speed of digital computers, algorithmic development and requirement of a high tech environment makes this field a very active area for academic and industrial research.

Catalog Description:

Introduction. Image Acquisition and Preprocessing. Image Analysis Techniques. Image Transforms. Object Recognition and Image Understanding. Advanced Research Areas in Machine Vision.

Pre-requisite(s):

None

Course Outline:

I.             Introduction
1.      Human Vision vs. Machine Vision
2.      Scene Constraints
3.      Optics

II.          Image Acquisition and Preprocessing
1.      Sensors for Image Acquisition
2.      Camera Interfaces and Video Standards
3.      Image Sampling and Quantization
4.      Image Preprocessing: Point, Global and Neighbourhood Operations
5.      Image Filters
6.      Edge Detection Techniques

III.       Image Analysis Techniques
1.      Image Segmentation
2.      Edge Based and Region Based Segmentation
3.      Edge Linking and Boundary Detection
4.      Matching
5.      Image Feature Extraction
6.      Mathematical Morphology

IV.       Image Transforms
1.      Continuous Image Mathematical Characterization
2.      Discrete Image Mathematical Characterization
3.      Discrete Fourier Transform
4.      Other Image Transforms

V.          Object Recognition and Image Understanding
1.      Knowledge representation
2.      Pattern Classification
3.      Neural Nets

VI.       Advanced Research Areas in Machine Vision
1.      Geometry for 3D Vision
2.      3D Objects Representation and Modeling Techniques
3.       Machine Vision: Industrial Application
4.      Robot Vision

Semester: Jan2021

___________________________________________

Class12 class13 class14

Class4_CV interface vdo subtract

Class9-1

Class10 gaussian bilateral images

class3-1_new

Laboratory Sessions:

Lab 1: Introduction to Python

Lab 2: Tensorflow and Keras
Lab 3: Simple ANN
Lab 4: CNN1
Lab 5: CNN2
Lab 6: Object Detection

Website:

esl.ait.ac.th

References:

Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, 2^nd ed., 2009
G. A. Awcock and R. Thomas: Applied Image Processing, McGraw-Hill, 1996.

L.J. Galbiati: Machine Vision and Digital Image Processing Fundamentals, Prentice Hall, NJ, 1990.
R.C

A.K. Jain: Fundamentals of Digital Image Processing, Prentice Hall, NJ, 1989.

M. Sonka, V.Hlavac, R. Boyle: Image Processing, Analysis, and Machine Vision, PWS Publishing, NJ, 1999.

D. Vernon: Machine Vision, Prentice Hall, NJ, 1991.

Journals/Magazines/Websites:

International Journal of Computer Vision
Machine Vision and Application - An International Journal
Pattern Recognition
Sensor Review

Grading System: The Final Grade will be computed according to the following weight

distribution: Mid Exam 20% Final Exam 25%; Lab and Assignments 15% Presentation 5% Project 35%. Open book exams are given for mid semester and final exam.

Instructor: Dr. Mongkol Ekpanyapong.