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

Yang, Haizhao; Lu, Jianfeng; Brown, William P.; Daubechies, Ingrid; Ying, Lexing

doi:10.1109/msp.2015.2406882

Cited by 38 publications

(27 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though adaptive time-series analysis is the main motivation of MIMF and MMD discussed in this paper, these models and algorithms can be naturally extended to higher dimensional spaces. Applications include atomic crystal images in physics [4,5], art investigation [8,9], geology [10,11,12], imaging [13], etc. In higher dimensional spaces, the computational efficiency is a crucial issue.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Multiresolution mode decomposition for adaptive time series analysis

Yang

2021

Applied and Computational Harmonic Analysis

Self Cite

View full text Add to dashboard Cite

This paper proposes the multiresolution mode decomposition (MMD) as a novel model for adaptive time series analysis. The main conceptual innovation is the introduction of the multiresolution intrinsic mode function (MIMF) of the formto model nonlinear and non-stationary data with time-dependent amplitudes, frequencies, and waveforms. The multiresolution expansion coefficients {a n }, {b n }, and the shape function series {s cn (t)} and {s sn (t)} provide innovative features for adaptive time series analysis. For complex signals that are a superposition of several MIMFs with well-differentiated phase functions φ(t), a new recursive scheme based on Gauss-Seidel iteration and diffeomorphisms is proposed to identify these MIMFs, their multiresolution expansion coefficients, and shape function series. Numerical examples from synthetic data and natural phenomena are given to demonstrate the power of this new method.

show abstract

Section: Resultsmentioning

confidence: 99%

“…which might be large, since in general s k ≈ s P k is not true. The deviation of s P k from s k comes from the influence of κ k in (8). After the folding map in (9), we hope that {κ k (2π mod (v , 1))} 1≤ ≤L behave like i.i.d.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Multiresolution mode decomposition for adaptive time series analysis

Yang

2021

Applied and Computational Harmonic Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has been shown in [6] that the twodimensional synchrosqueezed transforms [24], [25] are powerful tools in estimating the phase function N φ(x).…”

Section: Related Work and State-of-the-artmentioning

confidence: 99%

Removal of Canvas Patterns in Digital Acquisitions of Paintings

Cornelis

Yang

Goodfriend

et al. 2017

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

Abstract-We address the removal of canvas artifacts from high-resolution digital photographs and X-ray images of paintings on canvas. Both imaging modalities are common investigative tools in art history and art conservation. Canvas artifacts manifest themselves very differently according to the acquisition modality; they can hamper the visual reading of the painting by art experts, for instance, in preparing a restoration campaign.Digital removal of canvas is desirable for restorers when the painting on canvas they are preparing to restore has acquired over the years a much more salient texture.We propose a new algorithm that combines a cartoon-

show abstract

“…This ground layer varies in thickness according to the weave threads of the canvas, affecting the X-ray absorption. This makes the fabric pattern be showed in radiographies of the painting [6]. Until a few years ago, art experts used X-rays to analyze only the composition of the visible and hidden paint layers on the canvas.…”

Section: Introductionmentioning

confidence: 99%

“…Other recent research has explored techniques that are more sophisticated, such as autocorrelation and pattern recognition algorithms [18], synchrosqueezed transforms [6] or machine learning models [7]. They try to develop maps of thread patterns that are more detailed.…”

Section: Introductionmentioning

confidence: 99%

On the power spectral density applied to the analysis of old canvases

Simois

Murillo-Fuentes

2018

Signal Processing

View full text Add to dashboard Cite

A routine task for art historians is painting diagnostics, such as dating or attribution. Signal processing of the X-ray image of a canvas provides useful information about its fabric. However, previous methods may fail when very old and deteriorated artworks or simply canvases of small size are studied. We present a new framework to analyze and further characterize the paintings from their radiographs. First, we start from a general analysis of lattices and provide new unifying results about the theoretical spectra of weaves. Then, we use these results to infer the main structure of the fabric, like the type of weave and the thread densities. We propose a practical estimation of these theoretical results from paintings with the averaged power spectral density (PSD), which provides a more robust tool. Furthermore, we found that the PSD provides a fingerprint that characterizes the whole canvas. We search and discuss some distinctive features we may find in that fingerprint. We apply these results to several masterpieces of the 17th and 18th centuries from the Museo Nacional del Prado to show that this approach yields accurate results in thread counting and is very useful for paintings comparison, even in situations where previous methods fail.

show abstract

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

Cited by 38 publications

References 16 publications

Multiresolution mode decomposition for adaptive time series analysis

Multiresolution mode decomposition for adaptive time series analysis

Removal of Canvas Patterns in Digital Acquisitions of Paintings

On the power spectral density applied to the analysis of old canvases

Contact Info

Product

Resources

About