ImageLab: Simplifying Image Processing Exploration for Novices and Experts Alike
Sahan Dissanayaka, Oshan Mudanayaka, Thilina Halloluwa, Chameera De Silva
TL;DR
ImageLab tackles the barrier between image processing theory and practice by providing an interactive, block-based environment that hides programming complexity behind draggable operators. It builds on OpenCV through a Java backend and a Blockly/JS frontend, with a rule engine that enforces valid operator sequences to aid novices while still serving expert users. The paper contributes a complete system design and implementation, a broad operator toolkit with real-time previews, and a usability study showing positive reception from both schoolchildren and university students. Together, these elements demonstrate ImageLab's potential to democratize image processing education and provide a practical sandbox for practitioners.
Abstract
Image processing holds immense potential for societal benefit, yet its full potential is often accessible only to tech-savvy experts. Bridging this knowledge gap and providing accessible tools for users of all backgrounds remains an unexplored frontier. This paper introduces "ImageLab," a novel tool designed to democratize image processing, catering to both novices and experts by prioritizing interactive learning over theoretical complexity. ImageLab not only serves as a valuable educational resource but also offers a practical testing environment for seasoned practitioners. Through a comprehensive evaluation of ImageLab's features, we demonstrate its effectiveness through a user study done for a focused group of school children and university students which enables us to get positive feedback on the tool. Our work represents a significant stride toward enhancing image processing education and practice, making it more inclusive and approachable for all.