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Intelligent Pattern Recognition of Moving Organisms

Our group is developing pattern recognition software to track worm movement automatically, replacing manual analysis for efficiency. Challenges include library limitations and algorithm design improvements. Solutions involve shifting to C# for GUI, optimizing algorithms, and considering OpenCL for future improvements.

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Intelligent Pattern Recognition of Moving Organisms

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  1. Group 03 Intelligent Pattern Recognition of Moving Organisms (Worm Tracking!)

  2. Our Group • Client: Dr. Pandey, Microfluidics Lab • User: Archana Parashar • Ryan Alley, Team Leader • Colin Ray • Laith Abbas • Shan Zhong • Shusheng Xu

  3. Problem Worms are used for data in research Currently, movement tracking and analysis done manually Too time-consuming for aggregate data Typical process Use-casefor software

  4. Progress • Primarily: algorithm design • At the same time: user interface • Algorithm: • Background removal • Worm isolation • Head/tail discretizing • (currently) spline-fitting • PICTURES MOFUCKAH

  5. Problems and Challenges Library limitations with OpenCV OpenCV documentation (imaginary) Language limitations in C++ Algorithm design! Runtime efficiency Changing Requirements

  6. Solutions and Approaches • Rewriting algorithms for greater data access • Language was shifted to C#; streamlines GUI design and prototyping process • Large standard libraries and ease in threading • Considering OpenCL

  7. Future Work • More features: • Collision—highly desireable feature • Data representation • Interface & worm selection • Finish and tune algorithms • Optimization (OpenCL?)

  8. Questions?

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