A Practical Digital Image Processing Course with morph.py - Zampirolli, Francisco de Assis; Borovina Josko, João Marcelo; Teubl, Fernando; Kurashima, Celso Setsuo (Anais do Simpósio Brasileiro de Educação em Computação (EDUCOMP), 2024)
Teaching Digital Imaging Processing (DIP) is challenging, primarily because of its mathematical and algorithms complexities. Despite the recent growth in the field, comprehensive resources are lacking to support DIP education. To address this gap, this paper introduces a practical course utilizing a Python library named morph.py designed for beginners and accessible on Google Colab. This interactive course employs illustrative examples and hands-on exercises to facilitate the learning of fundamental DIP concepts and operators. It begins with basic concepts (e.g., image representation) and progresses to more advanced topics, including image transformations and feature extraction. We conducted an exploratory case study in one group (N=15) and gathered their perception through a voluntary survey. Our quantitative analysis strongly supports our teaching method's effectiveness based on the morph.py library, which addresses the difficulties of teaching DIP to beginners.