Updating the Content of a Computer Vision Course for Students from the STEM Programs

The use of image acquisition, processing and recognition techniques already has a history in terms of practical applications. The current and future development of industrial applications favors the development of such techniques based on increasing their performance. Considering this trend, it is necessary to adapt the contents of the artificial vision courses with an emphasis on the practical applicability with the high performance of the new methods and techniques in the field. At the Faculty of Automation, Computers and Electronics, from the University of Craiova, Romania, such a course is offered to students from the undergraduate programs in Multimedia Systems Engineering, Applied Electronics, and Mechatronics and Robotics, respectively. The course includes chapters dedicated to digital image acquisition, image processing, image segmentation, image descriptors, image classification and recognition, and applications. This paper will present how to upgrade the chapter related to applications, referring to the practical application developed by a group of doctoral students from our faculty. Methodologies for the development of reliable and complex computer vision applications which are used in a manufacturing environment are generally presented. The principles for the V-model development methodology and the Agile methodology are parts of this presentation. Students will receive the basic knowledge needed for comparing the advantages and disadvantages of these established methods in the manufacturing industry. In practice, these methods should measure the reliability of the system, what percentage of the functional requirements is achieved and at what quality, the behavior of the hardware and software components, and the behavior of the system when integrated into the plant environment. For validating the concept, the results obtained using the V-model and Agile methodologies into a computer vision automated inspection application for engine blocks from an automotive production plant will be exemplified.