Emmanuelle Gouillart

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.

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.

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.

Other people: Lars Grüter