Emmanuelle (Emma) Gouillart is a researcher and a scientific Python developer. She has a background in physics and materials science, and she has carried on scientific research and software development during the last years. She became a core contributor of Python’s popular image processing library scikit-image since a large part of her research relies on extracting quantitative data from image datasets. She has also made major contributions to the plotly data visualization package. She has been a co-organizer of the first Euroscipy conferences, and she enjoys very much discussing with Python users about image processing and visualization at conferences. Emma is the scientific director of Saint-Gobain Research Paris, the main R&D center of the industrial group Saint-Gobain, a world leader in materials and solutions for the construction sector.
@EGouillartInstitute / Company –
Saint-Gobain Research Paris
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.
Automatic image processing is a common task in many scientific and technological fields such as life sciences (with medical imaging), satellite imaging, etc. While machine learning is often used for efficient processing of such data sets, building a high-quality training set is an important task. Specialized software (such as rootpainter, ilastik) exist in different communities to build such training sets thanks to user annotations drawn on images.
In this talk, I will show how to use the open-source libraries plotly and dash to build custom interactive applications for interactive image annotation, and how to combine these tools with libraries such as scikit-image or machine learning/deep learning libraries for building a whole image processing pipeline.