Crack Detection and Classification
Using Support Vector Machine
SUBMITED TO:
PROF. SANJAY GOEL
SUBMITTED BY
SHREYA KHARE 08104774
AKSHAY BHANDARI 8104778
ANKITA JUHI 8104772
Abstract
Driven by the diffusion of multimedia systems and by the availability of
increasingly effective digital imaging tools, image-processing techniques have
been successfully applied to the analysis, restoration, archiving and preservation
of artwork.
Many paintings, especially old ones, suffer from breaks in the substrate, the
paint, or the varnish. These patterns are usually called cracks or craquelure and
can be caused by aging, drying, and mechanical factors. Age cracks can result
from non-uniform contraction in the canvas or wood-panel support of the
painting, which stresses the layers of the painting. Drying cracks are usually
caused by the evaporation of volatile paint components and the consequent
shrinkage of the paint. Finally, mechanical cracks result from painting
deformations due to external causes, e.g. vibrations and impacts.
The appearance of cracks on paintings deteriorates the perceived image quality.
However, one can use digital image processing techniques to detect and
eliminate the cracks on digitized paintings.
An integrated methodology for the detection and removal of cracks on
images of monuments and sculptures is being developed. The cracks are
detected followed by the removal of dark brush strokes which have been
misidentified as cracks. Finally, crack filling using filters are performed.
AIM:
To semi automate the process of crack detection and classification , along
with filling the cracks in the digital images of monuments and paintings.
This process will restore the destruction caused to cultural heritage.
MODULES OF THE PROJECT
1. Crack detection
Cracks usually have low luminance and thus can be considered as
local intensity minima with rather elongated structural characteristics.
Therefore, a crack detector can be applied on the luminance
component of an image and should be able to identify such minima. A
crack detection procedure based on the so-called top-hat transform.
In situations where the crack-like artifacts are of high luminance, as
in the case of scratches on photographs, negation of the luminance
component prior to the crack detection is not required, i.e. the crack
detection procedure can be applied directly on the luminance image.
The user can control the result of the crack detection procedure by
choosing appropriate values for the following parameters:
 The type of the structuring element B.
 The size of the structuring element B and the number n of
dilations
The top-hat transform generates a grayscale output image where
pixels with a large grey value are potential crack or crack-like
elements. Therefore, a thresholding operation on is required to
separate cracks from the rest of the image. The threshold can be
chosen by a trial and error procedure, i.e., by inspecting its effect on
the resulting crack map.
2. Crack classification
In some paintings, certain areas exist where brush strokes have almost
the same thickness and luminance features as cracks. The hair of a
person in a portrait could be such an area. Therefore, the top-hat
transform might misclassify
these dark brush strokes as cracks. Thus, in order to avoid any
undesirable alterations to the original image, it is very important to
separate these brush strokes from the actual cracks, before the
implementation of the crack filling procedure.
From the statistical analysis , it has been concluded that the classification
can be based on the following criterion based on the ‘Hue’ & ‘Saturation’
of the image:
H value : 00S value : 0.30.0This information is used to prepare the training set. The undecided
pixels are classified using Support Vector Machine.
SVM is a global classification model that generates nonoverlapping partitions and usually employs all attributes.
A support vector machine constructs a hyper plane or set of hyper
planes in a high- or infinite-dimensional space, which can be used
for classification. A good separation is achieved by the hyper plane
that has the largest distance to the nearest training data point of
any class, since in general the larger the margin the lower
the generalization error of the classifier.
3. Crack restoration
The cracks identified by the application as well as chosen by the end
user via the semi automatic interface of the application were removed
by the following ways:

Gaussian Filter
Gaussian filtering is done by convolving each point in the input
array with a Gaussian kernel and then summing them all to
produce the output array
The module convolves the source image with the specified
Gaussian kernel.

Median Filter
The median filter run through each element of the signal (in this
case the image) and replaces each pixel with the median of its
neighboring pixels (located in a square neighborhood around
the evaluated pixel).
The module smoothes an image using the median filter with a
particular size of aperture. Each channel of a multi-channel
image is processed independently.

Inpainting
Inpainting is the process of reconstructing lost or
deteriorated parts of images .The algorithm behind it is anisotropic
diffusion.
The function reconstructs the selected image area from the pixel
near the area boundary. The space-variant filter is in fact isotropic
but depends on the image content such that it approximates an
impulse function close to edges and other structures that should be
preserved in the image over the different levels of the resulting
scale-space. Anisotropic diffusion is normally implemented by
means of an approximation of the generalized diffusion equation:
each new image in the family is computed by applying this equation
to the previous image