Description

This is a learning platform where vision systems such as intelligent transportation, biometrics and industrial control are applied. Registered students can present their ideas, raise questions, and share related knowledge in the forum. At the same time, the Project Leader puts updates in the "News" section to allow students to keep in touch with machine vision trends and modern approaches.

The platform comprises:

• Self-learning guide (in the form of an e-book): The self-learning content covers the following fundamental machine vision concepts in detail: image formation, colour, edge detection, segmentation and thresholding, matching in 2D and recognition.
• Quick study guide (in the form of an e-book): The idea behind the quick study guide is to provide the fundamental machine vision concepts to the students to boost their interest in machine vision and provide a quick reference.
• Two case studies, tailor-made for students about:
  • biometrics (face recognition), which provides an introduction, videos of different face recognition applications and a simple face-recognition program (written in Matlab) for testing purposes; and
  • an industrial application (printed circuit board drilling inspection), which provides the industry background and essential guides for constructing solutions for the problems raised.
• Guided laboratory exercises (two sets): These exercises give students – especially students of "Enterprise Engineering & Management" (EEM) and “Logistics Engineering & Management" (LEM) – a better understanding of unfamiliar machine vision concepts and operations, using:
  • LabView, which aims to increase the students' sense of machine vision operations and the requ ired image-processing steps; and 
  • NeuroCheck, which focuses mainly on simulations of industrial machine vision applications.
• Guided experience-based machine vision C++ code writing exercises These exercises have six "check points" in order to let students experience writing machine-vision programs in C++ language, which is a widely used programming language. 
• An interaction forum, including posting questions and answers, knowledge-sharing and discussions, which can help to solve students' difficulties.

Click on the following link http://machinevision.myftp.biz for access to this platform.

Intended educational value

• This platform involves a combination of scientific methods and tools in vision technology aimed at analysing, designing and re-engineering the business and industrial processes to improve the competitiveness and smoothness of modern enterprises. 
• Both business application and engineering students can make use of the learning platform to master and customize selected machine vision techniques for industrial application, which will assist them in carrying out project and research work in related fields after graduation. 
• This learning package relates in particular to three ISE degree courses: BEng (Hons) in Industrial and Systems Engineering (ISE); BSc (Hons) in Enterprise Engineering with Management (EEM); and BSc (Hons) in Logistics Engineering and Management (LEM).

Example of application

The self-learning module on machine vision provides an opportunity for ISE, EEM and LEM students to enhance their knowledge of, and skills in, the fundamental concepts for machine vision, the development processes of a machine vision program system and different machine vision applications.

All ISE students can study the materials at their own pace. The quick study guide is highly recommended for those who want to know what machine vision is without studying everything in the platform. Once they complete the quick study guide, they can choose to do the guided laboratory exercises or go through the self-study guide; and, if they are really interested in machine vision, they can work on the case studies as well. For example, ISE offers courses on “Instrumentation & Product Testing“ (ISE204) and “Mechatronics for Products“ (ISE309), and students are strongly encouraged to make use of the machine vision self-learning platform after completing either of these subjects. With the background knowledge gained in ISE204 and ISE309, the students will understand how traditional measurement operations can be assisted by machine vision and how machine vision can accelerate manufacturing processes and improve product functions – which can help them to explore beyond the syllabus of the subjects mentioned.

In addition, there are some other subjects for which the learning platform is relevant, viz. “System Modelling and Application“ (ISE320), “Logistics Facility Design“ (ISE374), “Intelligent Decision Support Systems“ (ISE427), “Production Logistics“ (ISE448) and “Industrial Decision Support Systems“ (ISE528). These courses are available for students oriented to engineering and business applications to learn the related theory. The platform is now suitable for both these types of students to gain extra knowledge in their leisure time. For example, machine vision is clearly an appropriate technique and facility for improving warehouse and automated storage systems; the case studies cover how to apply the vision system to material-handling equipment for sorting, grouping, automation and solving existing limitations; and the vision system can also be applied for improving safety in a production logistics environment, warehouses and material-handling. Also, engineering-oriented students will like to design and develop visual systems as they, for example, have enthusiasm for mathematical work and are familiar with the data acquisition techniques, graphical programming skills and advanced theories behind vision information sensing, processing and interpretation. The learning platform provides laboratory and programming exercises for them, which cater for different groups of students.  

Evaluation/Comments

The results of a survey showed that student respondents held very positive attitudes to the platform, as follows:

• Over 85% were satisfied with it.
• More than two-thirds thought that they knew more about machine vision after visiting it.
• More than 75% said it is suitable for learning machine vision.
• Over 70% said it is attractive and interesting enough to motivate learning.
• 75% considered it to be user-friendly.
   

Contact Information

Machine Vision Group
Department of Industrial and Systems Engineering
The Hong Kong Polytechnic University

Dr. W.H. IP (mfwhip@inet.polyu.edu.hk)
Dr. C.Y. CHAN (mfcychan@inet.polyu.edu.hk)
Prof. K.L. YUNG (mfklyung@inet.polyu.edu.hk)
Mr. C.H. WU (jack.wu@polyu.edu.hk)