Practical Machine Vision Systems

LEVEL : INTERMEDIATE    HRDF : CLAIMABLE

   Trainer: Dr. Eric Ho Tatt Wei 

 

WHEN


5 - 6 APRIL 2021

 

WHERE


MS TEAMS

 

TIME


9.00AM - 5.00PM

 

FEE

RM1,250 FOR PROFESSIONALS 

10% Discount for Early Bird (until 10 Mar 2021) / Group / Students



COURSE SUMMARY

INTRODUCTION

Machine vision systems are an integral part of Industry 4.0 because imaging is now a crucial component of automated inspection systems and robotic systems. The design of high-performance machine vision systems requires much more than applying the latest algorithms or installing the highest specification camera. There are several key modules of a machine vision system namely the image sensor, optical assembly, electronic acquisition, computing and storage systems, preprocessing and classification algorithms and the imaging and software strategy. Optimum system design requires that all modules be jointly optimized and not unnecessarily overdesigned.   

In systems design, poor specifications in one module often leads to performance-limitations that invalidate the high-performance specifications of other modules. Oftentimes, by choosing the correct specifications for one module, one can significantly simplify the design and specifications of other modules at significant cost savings with no degradation and sometimes improvements in performance. Courses, textbooks and research literature rarely cover on systems-level perspectives and tradeoffs and tend to assume ideal data acquisition scenarios and do not consider how individual machine vision modules can interact with or limit the performance of other modules.   

This course focuses on systems-level design concepts to meet the competency gap. We first introduce important considerations in the specifications of key modules in a complete machine vision system. With a good understanding of individual modules, we go on to discuss how to assess and identify the performance-limiting modules, how modules interact and affect the performance of other modules and how to balance and tradeoff the specifications between modules to achieve cost or performance improvements. Participants’ of this course will learn systems-level thinking for the design and selection of specifications in a complete machine vision system.   

This course addresses the critical need in Malaysian industries to upgrade automated assembly lines and inspection equipment with machine vision capabilities to expand the scope of complex automated tasks that can be performed. The course aims to help engineering employees in Malaysian companies bridge the knowledge and competency gap by imparting broad systems level thinking in the design selection of end-to-end machine vision system. The goal is to develop engineers that can reason about the competing tradeoffs between various hardware and software choices to select or design a cost and performance optimal machine vision system. This will build competitiveness in product pricing and performance for Malaysian automated test equipment manufacturers and potentially accelerate the adoption of automation in manufacturing lines. 

COURSE CONTENT
Module 1
  • Machine vision systems components.
  • Case study of machine vision applications in Industry 4.0.  

Module 2

  • Image sensors specifications.
  • Color & hyperspectral information and white balance.
  • Evaluating image sensor performance.

  • Optical system specifications.

  • Limitations of optical lenses – aberrations, aperture, mounting .

  • Evaluating optical system performance.

Module 3

  • Data acquisition & storage specifications.
  • Data links & rates, storage formats and compression.
  • Electronics systems for real-time vision processing.
  • Evaluating electronic acquisition system performance.  
  • Machine vision software specifications.
  • Choosing between Commercial API, Open source libraries or Custom development.
  • Benchmarking electronics and algorithms bottlenecks.  

Module 4

  • Image preprocessing.
  • Non-linear filters and denoising.
  • Computational imaging and super-resolution.
  • Best practices for image preprocessing.
  • Machine vision algorithms.      
  • Complex feature descriptors (edge and blob features). 
  • Support vector machines classifiers.
  • Deep convolutional neural networks. 
  • Guidelines for selecting machine vision algorithms.
Module 5   

  • Imaging strategies.
  • Image features and invariants.
  • Object versus Imaging system scale and resolution.
  • Perspective versus orthographic views.
  • Single imager, multi or moving imager and light-field systems.
  • Guidelines for selecting imaging strategies.  
  • System-level tradeoffs and optimizations  - Case studies


OBJECTIVES

Upon completion of this course, participants will be able to:

  • Evaluate and select specifications for all components of a machine vision system namely the image sensors, optical system, acquisition electronics, image pre-processing and classification algorithms and software platform. 
  • Develop system level strategies to integrate or assemble complete machine vision systems (hardware and software) for Industry 4.0 automation. 


WHO SHOULD JOIN US TO :

  • Engineers, Researchers & Consultants.





OUR TRAINERS



1. Dr. Eric Ho Tatt Wei

Dr. Eric Ho Tatt Wei received his MS and PhD degrees in Electrical Engineering from Stanford University in Silicon Valley, USA specializing in computer hardware and VLSI systems, As part of his PhD research, he developed real-time systems for fruit flies for biological research to conduct automated inspection and guide robotic manipulation. He is currently pursuing applications of deep neural network technology to network analysis on MRI brain images.


COUNTDOWN

DaysHoursMinutesSeconds

REGISTRATION FEES

      Professional       

myr1,250

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

Early bird/ group/ student

myr1,125

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

OUR LOCATION

Centre for Advanced & Professional Education (CAPE)

 Level 16, Menara 2, Menara Kembar Bank Rakyat, 50470, Jalan Travers, Kuala Lumpur.

CALL US

+605 - 368 7558

DROP US AN EMAIL

cape@utp.edu.my