A software system for laboratory experiments in image processing

Laboratory experiments for image processing courses are usually software implementations of processing algorithms, but students of image processing come from diverse backgrounds with widely differing software experience. To avoid learning overhead, the software system should be easy to learn and use, even for those with no exposure to mathematical programming languages or object-oriented programming. The class library for image processing (CLIP) supports users with knowledge of C, by providing three C++ types with small public interfaces, including natural and efficient operator overloading. CLIP programs are compact and fast. Experience in using the system in undergraduate and graduate teaching indicates that it supports subject matter learning with little distraction from language/system learning

A Web-Shareable Real-World Imaging Problem for Enhancing an Image-Processing Curriculum

A real-world laboratory exercise is presented for image processing and related curricula. The exercise is a traffic-monitoring problem in which a truck must be tracked as it moves across a bridge and its velocity measured. Sequential images are taken from a dedicated Web camera that views the Smart Composite Bridge on the University of Missouri-Rolla campus. The prototype bridge is a field laboratory for several interdisciplinary courses, including a Machine Vision elective. The Machine Vision image-processing elective uses the traffic-monitoring exercise to give students experience with processing complex images, tracking image markers, and applying theoretical orthographic concepts. The laboratory exercise uses an image sequence acquired during the springtime with multiple potential markers available on the truck for assignment flexibility. A wintertime image sequence with snowy conditions is also available for assignment flexibility. This paper discusses the bridge and camera resources, the traffic-monitoring laboratory exercise description, and the Machine Vision course implementation and evaluation. Two versions of the traffic-monitoring exercise, including two image sequences and orthographic MATLAB code, are available on the bridge Website

Ambiente de suporte ao ensino de processamento de imagens usando a linguagem Python

Orientador: Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad