The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSOC 2016 - 2021. 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 is a public-domain Java image processing program extensively used in life and material sciences. The program was designed with an open architecture that provides extensibility via plugins.
Weka (Waikato Environment for Knowledge Analysis) is a collection of machine learning algorithms for data mining tasks. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine-learning schemes. The algorithms can be applied directly to a dataset or called from your Java.
The project idea: The Weka library offers some advanced visualization and analysis functionality. The student will develop a new visualization and reporting panel within ASP/IJ, exposing the Weka visualization and analysis functions.
Tasks
● Fix existing issues and bugs
● SQL database design
● GUI implementation and integration
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.
Skill level: Intermediate
Required skills: Java, Machine learning.
Time commitment: Half-time (175 h)
Lead mentor: Dimiter Prodanov (dimiterpp@gmail.com), INCF Belgian Node
Project website:
- ImageJ: https://imagej.nih.gov/
- Weka Weka 3 - Data Mining with Open Source Machine Learning Software in Java
- Active Segmentation : GitHub - sumit3203/ACTIVESEGMENTATION: Active Segmentation Project
Backup mentors: Sumit Vohra, ZIB, Berlin, Germany; Teodor Vakarelsky; IICT -BAS
Tech keywords: Image Segmentation
