TB3 - IMAGE AND PATTERN RECOGNITION

Image and Pattern Recognition are mature but exciting and fast developing fields, which underpin developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The objective of this GP is to know the necessary mathematical and computational tools and master the concepts of geometrical characterization of shapes (signals, images, point pattern) to get basic knowledge about machine learning on images for real applications. At the end of this toolbox, the student will be able to manipulate the main aspects of modern geometry, use concepts and results to solve concrete problems, such as the extraction of geometrical, morphometrical and textural information in image analysis as well as the characterization, modeling and simulation of point patterns or spatial object distributions. He will also be able to use some basic machine learning techniques to answer image and pattern recognition applicative problems, such as the automatic detection of cancerous skin lesions in biomedical imaging. Important note: This toolbox will be used to validate a part of the Master of Science "Mathematical Imaging and Spatial Pattern Analysis" to which students can enroll in the third year of the ICM cycle to obtain a double degree. For more information, contact Johan DEBAYLE (debayle@emse.fr) This GP of 80h consists of three UPs:
• Mathematical Geometry (33h)
• Computational Geometry (24h)
• Applications (23h)

The first UP gives the mathematical tools for the extraction of geometrical, morphometrical and textural information from images. The second UP gives the computational tools to characterize, model and simulate point patterns or spatial objects distributions. The third UP is focused on one or two real applications of image and pattern recognition by using the previous mathematical and computational tools.

This GP is strongly consistent with the TB « Introduction to Image Processing », as well as with other pedagogic units around the digital sciences: TB High Performance Calculation, MAJ Data Science, TB Fundamentals of EF Calculations, DEFI Big Data...