2022-08-30 –, HS 120
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.
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 goal of the tutorial is to give confidence to beginners in image processing to get started processing their images with scikit-image, to understand some basic concepts of image processing and to understand how to find help and documentation to go further after the tutorial. For more advanced users, a last part will focus on performance aspects.
The first hour of the tutorial will be accessible to beginners in image processing. Some experience with manipulating numpy arrays is a pre-requisite. We will first explain how to manipulate image pixels as elements of numpy arrays, and how to make basic transformation of images through numpy arrays. Then we will move to some basic concepts of digital image processing:
- image histogram and contrast
- image filtering: transformations of an image resulting in a new image of similar size (for example, thresholding, edge enhancement, etc.)
- image segmentation: partitioning an image into several regions (objects)
- measures on binary images
This part will be hands-on with several exercises, and we will show how to use the scikit-image documentation to find relevant information.
In the last half hour, Emma will focus on more advanced aspects and in particular will speak about performance and acceleration of image processing.
Getting started with image processing using scikit-image: make the most out of your image data
Project Homepage / Git: Domains:Image Processing
Expected audience expertise: Domain:none
Expected audience expertise: Python:some
Public link to supporting material:
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.
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.