Lars is currently working as a freelance and core developer for the image processing library scikit-image. With an education in electrical engineering and a focus in health and sensor technologies, he has been working as a research assistant on adaptive ultrasound imaging at the TU Dresden. As a student, he started contributing to the scientific Python ecosystem and discovered his interest for signal processing, Linux, and especially Python’s scientific ecosystem. He enjoys fine-tuning algorithms and discussing the finer points of designing an API.
Image data are used in many scientific fields such as astronomy, life sciences or material sciences. This tutorial will walk you through image processing with the scikit-image library, which is the numpy-native image processing library of the scientific python ecosystem.
The first hour of the tutorial will be accessible to beginners in image processing (some experience with numpy array is a pre-requisite), and will focus on some basic concepts of digital image manipulation and processing (filters, segmentation, measures). In the last half hour, we will focus on more advanced aspects and in particular Emma will speak about performance and acceleration of image processing.