Whole-painting canvas analysis using high- and low-level features

Abstract

Weave analysis of artist canvas examines x-ray images taken of the paintings. Algorithms assume an underlying regularity of the canvas weave over short distances and exploits shortspace spectral analysis to determine the fundamental frequency of the horizontal and vertical thread regularity. However, many paintings are too large to be covered by a single x-ray. Feature point analysis exploits brushstrokes and composition to merge several x-ray images into a single one, taking into account and removing both spatial and amplitude distortions. Certain master artists used low-quality canvas that is more irregular than the norm. A theoretical study of quasiperiodic signals shows that while the expected spectrum is a peak that broadens as period irregularity increases, sample function spectra have distinct peaks having an envelope equal to the expected spectrum.

Automatic Thread-Level Canvas Analysis: A machine-learning approach to analyzing the canvas of paintings

“…Recent canvas analysis results have challenged this belief by finding a canvas match between all three Triumph-paintings but were inconclusive because they were unable to perform thread-level canvas analysis. We obtained digital versions of radiographs (scanned at 600 dpi, 500 dpi, and 1,200 dpi for Triumph of Pan, Triumph of Bacchus, and Triumph of Silenus, respectively) and stitched them into whole-painting radiographs using algorithms described in [20]. Thereafter we manually annotated a total of 11,954 thread crossings in these radiographs and trained our thread-crossing detector on these manually annotated positive examples (negative examples were sampled randomly from the same radiographs).…”

Quantitative Canvas Weave Analysis Using 2-D Synchrosqueezed Transforms: Application of time-frequency analysis to art investigation

“…In their pioneering work [4], Johnson et al developed an algorithm for canvas thread-counting based on windowed Fourier transforms (wFT); further developments in [5], [6] extract more information, such as thread angles and weave patterns. Successful applications to paintings of art historical interest include works by van Gogh [7], [8], Diego Velázquez [9], Johannes Vermeer [10], among others [11]- [15].…”