2023-08-15 –, HS 120
This tutorial explores scikit-image, the numpy-native library in the scientific python ecosystem, for visual data analysis and manipulation.
Designed for beginners and advanced users, it empowers image analysis skills and offers insights into scikit-image documentation.
It covers basic concepts like image histogram, contrast, filtering, segmentation, and descriptors through practical exercises.
The tutorial concludes with advanced performance optimization techniques.
Familiarity with numpy arrays is essential as it the underlying data representation.
Manipulating and analiyzing visual data is key in many scientific fields such as astronomy, life sciences or material sciences. This tutorial explores the scikit-image library, the numpy-native library in the
scientific python echosistem for image processing.
The tutorial aims to empower beginners to analyze images using scikit-image. It facilitates understanding of fundamental image processing concepts, and guides participants in how to find help and documentation to go further after the tutorial. For more advanced users, a last part will focus on performance aspects.
Basic familiarity with manipulating numpy arrays is required, as we will begin by manipulating image pixels as elements within numpy arrays and conducting fundamental image transformations using these arrays.
Next, we'll transition to fundamental image processing concepts, offering practical exercises and guidance on navigating the scikit-image documentation. These concepts might include:
- 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)
- image descriptors
The last part is devoted to advanced topics, particularly focusing on image processing performance and acceleration.
material: https://github.com/glemaitre/euroscipy-2023-scikit-image
Getting started with image processing using scikit-image: make the most out of your image data
Category [Scientific Applications] –Other
Expected audience expertise: Domain –none
Expected audience expertise: Python –some
Category [High Performance Computing] –Parallel Computing
Category [Community, Education, and Outreach] –Learning and Teaching Scientific Python
Category [Machine and Deep Learning] –Supervised Learning
Category [Data Science and Visualization] –Data Analysis and Data Engineering
Project Homepage / Git –
