This document outlines the assessment criteria, course objectives, learning outcomes, and contents for the Machine Vision course STB 48503. The assessment is 60% coursework including assignments, quizzes, tests, and a mini project, and 40% final exam. The course objectives are for students to gain knowledge of machine vision systems and applications, learn to process and extract information from digital images, and understand concepts and algorithms. The learning outcomes include understanding basic machine vision concepts, image processing techniques, binary vision algorithms, and evaluating image analysis applications. The contents include topics like image acquisition, digital image processing techniques, segmentation, and image analysis with a case study. Practical labs cover Matlab software, GUI, digital image processing, pre

1. Machine Vision
STB 48503
Lecturer: Mohd Fauzi Alias

2. Assessment Criteria
STB 48503 (Elective Major)
Course Work
(60%)
Practical Report
10%
Assignment/Quiz
10%
Test
10%
Mini Project
-Report
Report
-Model
30%
=10%
=20%
Final Exam
(40%)

3. Course Objectives
Students should gain knowledge on the application areas,
restrictions, and structure of machine vision systems.
Students should be able to operate on digital images:
p
g
g
extract basic visual information from images.
Students should be able to assembly and inspection tasks
traditionally performed by human operators.
To provide a good understanding of concepts, algorithms
and their applications in machine vision
vision.

4. Course Objectives
Students should gain knowledge on the application areas,
restrictions, and structure of machine vision systems.
Students should be able to operate on digital images:
p
g
g
extract basic visual information from images.
Students should be able to assembly and inspection tasks
traditionally performed by human operators.
To provide a good understanding of concepts, algorithms
and their applications in machine vision
vision.

5. Learning Outcomes
On the completion of the subject, students should
be able to:
Understand the basic concepts of machine vision
Understand the fundamental techniques in image
p
processing
g
Understand the concept of the binary vision algorithms.
Evaluate a practical image analysis application

6. Contents
Chap
Contents
1
An Introduction to computer vision
2
Image Acquisition and Representation
-Sampling and Quantization, Data acquisition method
3
Digital Image Processing
- Fundamental of DIP, Neighborhood Operations, Mathematical
Morphology, Filtering, Histogram, Skeleton,
Morphology Filtering Histogram Skeleton Chain Code
4
The Segmentation
g
y
g
g
g
-Region and boundary based segmentation, Thresholding, Clustering,
Region Growing, Edge detection
5
Image Analysis & Case Study
-Template matching, Classification, Pattern Recognition

7. Practical Lab
Lab
Title
1
Introduction to Matlab Software
2
Introduction to Matlab GUI (Graphical user interface)
3
Introduction to DIP using MATLAB
4
Image Analysis (Pre-processing Image)
5
Object Counting