An implementation focused approach to teaching image processing and machine vision-from theory to beagleboard

The explosion of multimedia applications within embedded devices has ensured that Image Processing and Machine Vision has now become a mainstream subject within most Computer Science and Electronic Engineering curricula. Yet often there exists a disconnection between the rapid prototyping tools that are taught within the laboratory to demonstrate concepts and those that are used for actual deployment in a stand-alone product. This calls for an approach whereby students are exposed to multiple levels of abstraction, in order to align the skill sets of our students with the requirements and expectations of industry. This paper describes the development of a senior level undergraduate course that introduces machine vision and image processing algorithms and implementation topics within the larger context of embedded computing. The key focus is that the student appreciates the theoretical concepts but is also capable of implementing them on embedded processors for prototyping or production.