Inspection of welded object based on the shape from shading image processing methodAmir Movafeghi1, Effat Yahaghi2, Noureddin Mohammadzadeh1 and Berouz Rokrok1 and Nasser Rastkhah1

1Nuclear Safety and Radiological Protection Department, Nuclear Science and Technology Research Institute, Tehran, Iran.

Email: amovafeghi@aeoi.org.ir 2Department of Physics, Imam Khomeini International University, Qazvin, Iran. Email: yahaghi@sci.ikiu.ac.ir

Paper ID: 106

AbstractAbstractAbstractAbstract

Industrial radiography is one of the most important NDT methods to detect weld defects

such as porosity, pinhole and crack. All these defects can be detected automatically and

the weld defect can be characterized by the interpretation of radiographic images. If the

provided images of the industrial radiography have not been clear, the detection of defect

can be difficult. The defect detection probability depends on radiographic film quality and

interpreter’s experience and abilities. The radiographic images are sometimes noisy and

have low quality. Thus, there is a necessity for some methods for increasing the image

quality for better detection of the defects. The use of image processing techniques is

available to achieve this aim. This paper introduces a method of shape from shading

based on a single image and puts forward its implementation. It then presents the

improved measures of the original algorithm according to the defects of the computed

results. Shape-from-shading (SFS) is a classic problem in computer vision. The goal is to

derive a 3D image from 1D or 2D images. The human vision system can guess both the

shape of the surface and the direction of the incident light for an image of a surface. It is

notable that the human eye is used to see objects in three dimensions and can also

detect depth. Thus, in this research, the SFS method is applied on two-dimensional

digitized radiographic images and three-dimensional images are extracted. Experts’

opinions have also been used for evaluation of the results. Experts’ view say that the SFS

method is useful in the detection of welding defects and the combination of image

processing techniques, and also the SFS method can produce clear radiography image,

which can be used effectively in the detailed analysis of weld images. The results of

comments are indicated that using the SFS method is useful and the detection of defects

is improved by this method in weld radiography.

Keywords: Keywords: Keywords: Keywords: industrial radiography; image processing; shape from shading; weld defect;

radiography interpreter.

1111. Introduction. Introduction. Introduction. Introduction

Radiography is one of the most important non-destructive methods (NDT) to detect the

welding defects on a film viewer or a high contrast monitor in digital radiography. [1].

Defects identifications has its own difficulties arising from some factors that reducing

image quality ,e.g. streaks, fog, and spots. Therefore, the radiographic images are not

always easy to interpret. Image processing methods can analyze the radiography images

and help for better interpretation. The brightness and contrast of radiography images can

be changed by different the mathematical algorithm [2-3].

One of a classic problem in computer vision is shapeshapeshapeshape----fromfromfromfrom----shadingshadingshadingshading (SFS)(SFS)(SFS)(SFS). The goal is how

the shape of a three dimensional object may be recovered from shading in a two-

dimensional image of the object. SFS is one of the important problems in machine vision.

Although this important subfield is now in its second decade, but the different methods

are introduced for reconstruction of 3D image. In some approaches, a Lambertian

reflectance model for the surface is assumed, and also that the surface is lit with a

single distant light source. It allows computation of a shaded image for any given surface

and light source direction. The SFS problem is the inverse of this image synthesis

process; for finding the shape of the 3D surface and the light source direction for an

image [4-5].

2222. Methods . Methods . Methods . Methods

2.1 2.1 2.1 2.1 Radiography Radiography Radiography Radiography imagesimagesimagesimages

Radiographic experiments were conducted on the welded objects using Kodak AA-400

film, a gamma source of Ir-192, and an X-ray machine , (300 kVolt Pantak-Seifert, Type

Eresco 65 MF2). X-ray energy was set at 200 kVolt. The radiographs were converted to

digital images format using a film digitizer. The radiographs were scanned with a

Microtek 1000 XL scanner [6]. The scanner was calibrated using ‘density calibration film’

to convert gray levels to optical density for every scan. Fig. 1 shows the example of a

digitized radiograph of the object which was used for further digital processing.

2222....2222 ShapeShapeShapeShape fromfromfromfrom shadingshadingshadingshading andandandand waveletwaveletwaveletwavelet denoisingdenoisingdenoisingdenoising

The goal of shape from shading is to derive a 3D scene description from one or more 2D

images. The reconstructed image by SFS approach is expressed by these parameters:

The depth, Z(x, y), surface normal vector, (nx, ny, nz), surface gradients, p, q, surface

slant, φ and tilt, θ. The depth is the relative with distance from the X-ray source to surface

points, or the relative surface height above the x-y plane. The surface normal is the

orientation of a vector perpendicular to the tangential plane on the object surface. The

surface gradient in the x-direction, p=(∂Z(x,y))/∂x and in the y-direction, q =(∂Z(x,y))/∂y;

are the rate of change of depth in the x and y directions. The surface slant φ , and tilt θ,

are related to the surface normal N=(nx, ny, nz)= (l sinφ cosθ, l sinφ sinθ, l cosφ ), where l

is the magnitude of the surface normal.

Computing the image brightness; it can be rewritten as follows;

R�,� � ρN�n � ρN��,�, ��

������(1)

where Rs,ρ is the image brightness and ρ is a constant value and is defended as Albedo

value and is depended to material. For each pixel, Rs,ρ is calculated and SFS image are

reconstructed [6, 7].

Figure 1: a typical example of

a) the original radiographic images

and the reconstructed images by

b) denoising wavelet and c) SFS algorithm

For denoising of radiography image, the wavelet transform approach is implemented.

Signal denoising is one of the important applications of the wavelets. Following wavelet

decomposition, the high frequency components contain most of the noise information

and little signal information. Therefore, soft thresholding is applied to various

components. The threshold is applied to higher values for high frequency components

and lower values for low frequency components.

3333.... ResultsResultsResultsResults andandandand DiscussionDiscussionDiscussionDiscussion

The radiography image often has low contrast, and need to be processed. In the

research, SFS was applied to different radiography images to improve the image quality

and detectability of weld defects. At the first stage, the SFS algorithm was directly

implemented to the original radiography images from some welded specimens that were

provided according to section 2.1. Then the digital images were opened and denoising

algorithm and SFS method were applied to the image data (section 2.2). The image

processing program was written by MATLAB 2012 b software.

For this, synthetic images were extracted with the assumption of ρ=0.6 and I = [0, 0, 1]T.

The chosen ρ isn’t effect on the reconstructed image because it is a constant value in

equation (1). Figure 1 shows a typical example of the original radiographic images and

the reconstructed images by denoising wavelet and SFS algorithm. However, the

reconstructed images in Fig.1-b, the image are smoothed and the defects aren’t

detectable clearly. Figure 1-c shows some defects such as porosity and crack in 3-D

visualization have depth but all gradient variations appear as noise in this image.

Fig. 2: The line profile of the region

defect (The yellow line in figure 1)

for the original image (solid line)

and the denoised image (the dashed line)

Fig. 3: The line profile of the region

defect (yellow line in Fig. 1)

for the reprocessed image by SFS

For evaluation of the results, the line profiles of the defect region are plotted in figures 2

and 3. These regions are shown in figure 1 by yellow lines

4444. Conclusions. Conclusions. Conclusions. Conclusions

In this research, a wavelet denoising algorithm and SFS method have been implemented

to radiographic images in order to eliminate noise and 3D images reconstruction. The

results show that SFS algorithm and wavelet denoising can be relied on for better

detection of weld defects in radiographic images, and these methods can improve

shape, style and region of defect distinction.

ReferencesReferencesReferencesReferences

1. K Edalati, N Rastkhah, A Kermani, M Seiedi and A Movafeghi, ‘The use of radiography for thickness

measurement and corrosion monitoring in pipes’, Intl J. of Pres. Vessels & Piping, Elsevier, 736-741, Vol.

83., 2006.

2. X Li, S K Tso, X P Guan, Q Huang, ‘Improving automatic detection of defects in castings by applying

wavelet technique’, IEEE Trans Industrial Electronic; 53(6), pp. 1927–34, 2006.

3. ASME, ‘Digital image acquisition, display, interpretation and storage of radiographs for nuclear

applications’, ASME Boiler and pressure vessel code and standard, Section 5, Article 2, American Society

of Mechanical Engineering, 2013.

4. Imperial College, London, MEng Individual Report A, ‘A Study of Statistical Methods for Facial Shape-

from-shading’, June 18, 2012.

5. A Ahmed, and A Farag, ‘A new statistical model combining shape and spherical harmonics illumination

for face reconstruction, in Advances in Visual Computing’, Lecture Notes in Computer Science, vol.4841,

pp. 531,541, Springer, 2007.

6. EN 14096-1, “Non-destructive testing – Qualification of radiographic film digitization systems – part 1:

Definitions, qualitative measurements of image quality parameters, standard reference film and

qualitative control”, European Norm, 2004.

Digital Industrial Radiology and Computed Tomography (DIR 2015) 22-25 June 2015, Belgium, Ghent - www.ndt.net/app.DIR2015