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.