  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.
