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Implementation and discussion of the Pith Estimation on Rough Log End Images using Local Fourier Spectrum Analysis method

Henry Marichal, Diego Passarella, Gregory Randall

Abstract

In this article, we analyze and propose a Python implementation of the method "Pith Estimation on Rough Log End images using Local Fourier Spectrum Analysis", by Rudolf Schraml and Andreas Uhl. The algorithm is tested over two datasets.

Implementation and discussion of the Pith Estimation on Rough Log End Images using Local Fourier Spectrum Analysis method

Abstract

In this article, we analyze and propose a Python implementation of the method "Pith Estimation on Rough Log End images using Local Fourier Spectrum Analysis", by Rudolf Schraml and Andreas Uhl. The algorithm is tested over two datasets.
Paper Structure (3 sections, 1 figure, 1 algorithm)

This paper contains 3 sections, 1 figure, 1 algorithm.

Table of Contents

  1. Introduction
  2. Method
  3. Pipeline

Figures (1)

  • Figure 1: (a) RGB image of disk F02d from UruDendro UruDendro dataset, (b) The whole structure, called spider web, is formed by a center (which corresponds to the slice pith), rays and the rings (concentric curves). In the scheme, the rings are circles, but in practice, they can be (strongly) deformed as long as they don't intersect another ring.