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Progress report

This report evaluates a parameter optimization strategy for a virtual camera system used in finger detection. The research identifies that optimal parameters represent 18% to 20% of all parameter combinations within a specific range. It emphasizes that finding these optimal parameters is quick, taking only a few minutes, yet notes limitations such as slow automatic search capabilities of existing TouchLib methods and lack of effectiveness under non-uniform lighting conditions. Potential improvements are proposed, including the integration of GPU acceleration for better performance and improved denoising methods.

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Progress report

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Presentation Transcript

  1. Progress report By shooting

  2. Problem with parameter optimization • Multi-optimal parameter • Optimal parameters are 18%~20% of all parameter combination through specific range • Only take few minutes for people to find an optimal parameter • Automatic search not fast enough

  3. Modified TouchLib • Disadvantage of TouchLib • Denoise method is not stable • Not suitable for non-uniform lighting condition • Without tracking and smooth mechanism

  4. Finger detection framework • Virtual camera • Intuitive in vision • Reduce matching problem • Unified finger size among different position • Detection module can be exchange • Upgrade performance (by implementing on GPU) IR cam Virtual Camera Detection module IR cam

  5. Fid 1 IR cam Detection module matching IR cam Detection module Fid 2 Fid 1 Same finger? Fid 2

  6. Parameter optimization • Enumeration • Parameter combination 6 (step) *6 (step) *6 (step) *6 (step) = 1296 • 244/1296 = 18.83% • Particle filter • Parameter combination • 50 (sample)*10 (iteration) = 500 • 108/500 = 21.6%

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