The Extraction and Classification of Craquelure for Geographical and Conditional Based Analysis of Pictorial Art Mouhanned El-Youssef, Spike Bucklow, Roman Gr. Maev

Background Craquelure represents the unique crack formations on paintings that is related to the materials and methods employed by the artist Paintings from a particular geographical area tend to exhibit similar trends in their craquelure patterns Craquelure is of important interest to art conservators for they indicate the condition of the painting and areas potentially exhibiting mechanical stress

Background Image courtesy of Dr. Bucklow French Painting – Smooth crack lines – Curved – Large Islands

Background Image courtesy of Dr. Bucklow Flemish Painting – Jagged crack lines – Ordered and straight – Small Islands

Objectives To develop a craquelure analysis algorithm capable of classifying each craquelure pattern to their corresponding region of origins To establish a crack monitoring system that will monitor and track the growth of cracks over the course of a painting’s life

Agenda 1)Project Outline 2)Crack Extraction Techniques 3)Crack Hierarchal Representation 4)Feature Extraction/Dimension Reduction 5)Classification 6)Crack Monitoring System 7)Questions and Discussion

Project Outline

Crack Extraction Techniques Mathematical Morphology is used to extract crack lines from an image Mathematical Morphology consists of four operations: – Dilation – Erosion – Closing (Dilation Erosion) – Opening (Erosion Dilation)

Flash Video

Mathematical Morphology

Crack Extraction Techniques: Thresholding Global thresholding Segmented thresholding Offset Thresholding The greyscale image then needs to be thresholded to a binary image. Three general thresholding techniques:

Global Thresholding

Segmented Thresholding

Offset Thresholding

Comparison Global ThresholdSegmented ThresholdOffset Threshold

Crack Hierarchal Representation After crack extractions, a line following algorithm is applied to the image A sequence of numbers (chain code) is recorded based on the crack direction Edge points, node points and line lengths are determined in a hierarchal fashion

Crack Hierarchal Representation An illustration of the crack following algorithm is displayed below: Crack Following Algorithm: Each neighbouring pixel is assigned a number value The crack line above would display the following chain code:

Crack Hierarchal Representation After applying the crack following algorithm, our crack pattern is structured in terms of nodes, edges, lines and distances The green lines in the figure are crack lines that form an island (a closed loop). Red pixels represent nodes and yellow pixels represent edge points.

Feature Extraction The length of each individual line segment is calculated during the hierarchal representation of the crack pattern Various other features are extracted from the network A histogram is obtained by tallying the direction vectors in the chain code The number of islands is calculated within a crack network

Feature Extraction Overall features extracted are: Number of Islands Node density Edge density Directionality Histogram Distance between nodes Straight line to actual line ratio Line Jaggedness Lines per node Average line length

Feature Extraction Line size is calculated as: Straight line to actual line ratio: Line Significance:

Feature Extraction Flemish Craquelure Italian Craquelure Dutch Craquelure French Craquelure

Dimension Reduction Reducing the dimensions of a feature space is necessary before classification Principle Component Analysis (PCA) as well as Linear Discriminant Analysis (LDA) is used for feature reduction The Fisher ratio test is used to determine which variable best discriminates between classes

Classification After dimension reduction, the features were fed into a Linear Discriminatory Analysis (LDA) classifier for classification The results were found to be: Predicted Class ClassFlemishFrenchItalianDutch Actual Class Flemish67.0%0%12.5%20.5% French0%92.5%6.0%1.5% Italian22.5%10%59.0%8.5% Dutch34%0%6.0%60.0%

Crack Monitoring System The crack monitoring system is used to monitor the growth of cracks on the surface to determine the “health” of a painting. The monitoring system must be: Must be rotation invariant Must be scale invariant Must be robust

Crack Monitoring System Present1 year ago

Crack Monitoring System

Conclusions A framework for the classification of craquelure patterns has been established A health monitoring system can be used to detect and monitor the growth of cracks in paintings

Future Work Expand on the classification process by investigating alternative techniques Continue building a database of craquelure images for different types of paintings (ie. French, Dutch..) Refine the health monitoring algorithm and develop a set of acquisition constraints

Questions and Discussion

Refrences [1] Spike Bucklow, "The Description of Craquelure Patterns," Studies in Conservation, vol. 42, no. 3, pp , [2] Alexandra Moxley, "The Art of Fogery," The Undergraduate Scholar, pp. 1-10, [3] Spike Bucklow, "A Stylometric Analysis of Craquelure," Computers and the Humanities, vol. 31, pp , [4] Fazly Salleh Abas, "Analysis of Craquelure Patterns for Content-Based Retrieval," University of Southampton, Southampton, PhD Thesis [5] Irene Crisologo, Chrisopher Monterola, and Maricor Soriano, "Statistical Feature- based Craquelure Classification," International Journal of Modern Physics C, vol. 22, no. 11, pp , September [6] P. Willigen, A Mathematical Study on Craquelure and other Mechanical Damage in Paintings, E Klerk and H. Maaren, Eds. Dellf, The Netherlands: Delft Univeristy Press, [7] W. Stanley Taft and James W. Mayer, The Science of Paintings. New York, USA: Springer-Verlag, 2000.