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DIGITAL IMAGE PROCESSING GUIDE IN JUPYTER NOTEBOOK AS A SUPPORT MATERIAL FOR A PROJECT-BASED LEARNING METHOD: CASE STUDY IN LICENSE PLATE DETECTION;
LUIZ EDUARDO PITA MERC?S ALMEIDA, JOS? MAUR?CIO R DE SOUZA NETO, HELON DAVID DE MAC?DO BRAZ;
With the advances in Convolutional Neural Networks and the expansion of image and
video data, Computer Vision becomes an important area on Artificial Intelligence. Teaching of
Digital Image Processing (DIP) as a basic knowledge for Computer Vision expands the horizon
of applications and it is an important investment for engineering and computing schools. This
work objective is to create a support guide for DIP classes, and for Project-Based Learning
(PBL) methods in this area. Using modern technologies such as Python programming
language, Jupyter Notebook environment, and OpenCV library, this guide explores a branch
of DIP techniques, trying to solve the problem of automatic license plate detection. The guide
is composed of eleven notebooks that progressively create the final solution for the plate
detection problem based on literature and on DIP reference books. As preliminary results, the
projected guide forms a representative material of a DIP discipline, of basic algorithms for the
proposed problem presented on literature, and of a real-world license plate detection solution,
becoming ideal to be used as a PBL project.
Digital Image Processing. Guide. Project-Based Learning.
