Optimization of Curvi-Linear Tracing Applied to Solar Physics and Biophysics

Abstract

We developed an automated pattern recognition code that is particularly well suited to extract one-dimensional curvi-linear features from two-dimensional digital images. A former version of this {\sl Oriented Coronal CUrved Loop Tracing (OCCULT)} code was applied to spacecraft images of magnetic loops in the solar corona, recorded with the NASA spacecraft {\sl Transition Region And Coronal Explorer (TRACE)} in extreme ultra-violet wavelengths. Here we apply an advanced version of this code ({\sl OCCULT-2}) also to similar images from the {\sl Solar Dynamics Observatory (SDO)}, to chromospheric H-$\alpha$ images obtained with the {\sl Swedish Solar Telescope (SST)}, and to microscopy images of microtubule filaments in live cells in biophysics. We provide a full analytical description of the code, optimize the control parameters, and compare the automated tracing with visual/manual methods. The traced structures differ by up to 16 orders of magnitude in size, which demonstrates the universality of the tracing algorithm.