The Active Segmentation platform for ImageJ (ASP/IJ)[2] was developed in the scope of GSOC 2016 - 2025. The plugin provides a general-purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. ImageJ [1] is a public-domain Java image processing program extensively used in life and material sciences. Recent years have experienced explosive development in the GPU-accelerated computing. The project will explore the existing parallel filtering framework and extend it towards TornadoVM framework [4]. TornadoVM is an open-source software technology that automatically accelerates Java programs on multi-core CPUs, GPUs, and FPGAs.
The project will explore some of established Java-based GPU computing frameworks [4,5,6,7] and port the convolution engine used by the ASP/IJ to parallel implementation.
The project idea: The project will extend the convolution engine used by the ASP/IJ to the TornadoVM framework.
Tasks
• Fix existing issues and bugs
• GPU computing frameworks evaluation and testing
• Implementation and profiling of selected convolution filters
Minimal set of deliverables
• Requirement specification – Prepared by the candidate after understanding the functionality.
• System Design – Detailed plan for development of the plugin and test cases.
• Implementation and testing – Details of implementation and testing of the platform.
Desired skills: Java, Machine learning
Mentors: Dimiter Prodanov (dimiterpp@gmail.com) IICT-BAS; Teodor Minev IICT -BAS (teodorminev98@gmail.com); Rikas Ilamdeen (rikasilamdeen@gmail.com)
References
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ImageJ: https://imagej.nih.gov/
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Active Segmentation: GitHub - sumit3203/ACTIVESEGMENTATION: Active Segmentation Project · GitHub
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Eclipse IDE https://www.eclipse.org/
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TornadoVM https://www.tornadovm.org/