  A fully automated technique for wood pith detection (APD), relying on the
concentric shape of the structure of wood ring slices, is introduced. The
method estimates the ring's local orientations using the 2D structure tensor
and finds the pith position, optimizing a cost function designed for this
problem. We also present a variant (APD-PCL), using the parallel coordinates
space, that enhances the method's effectiveness when there are no clear tree
ring patterns. Furthermore, refining previous work by Kurdthongmee, a YoloV8
net is trained for pith detection, producing a deep learning-based approach to
the same problem (APD-DL). All methods were tested on seven datasets, including
images captured under diverse conditions (controlled laboratory settings,
sawmill, and forest) and featuring various tree species (Pinus taeda, Douglas
fir, Abies alba, and Gleditsia triacanthos). All proposed approaches outperform
existing state-of-the-art methods and can be used in CPU-based real-time
applications. Additionally, we provide a novel dataset comprising images of
gymnosperm and angiosperm species. Dataset and source code are available at
http://github.com/hmarichal93/apd.
