Dask-image: Distributed Image Processing for Large Data
Dask-image: Distributed Image Processing for Large Data


This talk introduces dask-image, a python library for distributed image processing. Targeted towards applications involving large array data too big to fit in memory, dask-image is built on top of numpy, scipy, and dask allowing easy scalability and portability from your laptop to the supercomputing cluster. It is of broad interest to a diverse range of scientific fields including astronomy, geosciences, microscopy, and climate sciences. We will provide a general overview of the dask-image library, then discuss mixing and matching with your own custom functions, and present a practical case study of a python image processing pipeline.


Genevieve Buckley is a scientist and programmer based in Melbourne Australia. She builds software tools for scientific discovery. Her interests include deep learning, automated analysis, and contributing to open source projects. She has a wealth of professional experience with image processing and analysis, spanning x-ray imaging, fluorescence microscopy, and electron beam microscopy. She is a maintainer for the dask-image project.

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