The final structuring set nB is evaluated only once using Eqn. (1.2) and is used subsequently in the opening operation of Eqn. (1.1). The opening f nB
of a function is a low-pass nonlinear filter that erases all peaks (local maxima) in which the structuring element nB cannot fit. Thus, the image f – f nB
contains only those peaks and no background at all. Since cracks are local minima rather than local maxima, the top-hat transform should be applied on the negated luminance image.
The thresholded (binary) output of the top-hat transform on the luminance component of an image containing cracks (Fig. 1.1) can be seen in Fig.1. 2.
Fig. 1.1 Original Image [4]
Fig.1.2. Thresholded output of the top-hat transform http://cmm.ensmp.fr/Micromorph/course/sld011.htm
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The parameters were the following for figure 2:
• Structuring element type: square;
• Structuring element size: 3 X 3;
• Number n of dilations in (2): 2.
1.3
S UMMARY
The material in this chapter is to detect or identify the cracks from the cracked image. For the detection of cracks Top-hat transform is used which is a grayscale morphological filter.
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C HAPTER 2
LITERATURE SURVEY
2.1
B ACKGROUND S TUDY
Image processing techniques have recently been applied to analysis, preservation and restoration of artwork. Old paintings are cultural assets for country which can be preserved by computer aided analysis and processing. These paintings get deteriorated mainly by an undesired pattern that causes breaks in the paint, or varnish. Such a pattern can be rectangular, circular, spider-web, unidirectional, tree branches and random [2] and are usually called cracks. Cracks are caused mainly by aging, drying and mechanical factors like vibration, and human handling.
Computer aided tools can be used to implement image processing technique for the elimination and detection of cracks in ancient digital paintings. Though this methodology produces a digitally restored version of the artwork, it can still prove to be a useful guide for historians, and museum curators.
2.2
R ELATED W ORK
A system that is capable of tracking and interpolating cracks is presented in [1]. The user should manually select a point on each crack to be restored. The study presents a system for removing cracks from old paintings and frescos. In many cases, cracks severely deteriorate the aspect of paintings both because of their number and their heaviness. Thus, a system capable of removing them is of great interest, even if the removal is only virtual. For crack identification, the system the author developed first asks the user to manually select a point on the crack. Then, it automatically tracks the crack. Users must select a point on the crack manually because it’s impossible for the system to distinguish between cracks and lines belonging to the drawing. Once the selection has been made, crack removal is completely automatic. By noting that cracks are usually considerably darker than the background and that they are characterized by a uniform gray level, tracking is accomplished on the basis of two main features: absolute
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gray level and crack uniformity. Once the system knows some pixels belong to a crack, it assigns to the crack new pixels if their gray levels lie in a given range and do not differ significantly from those of the pixels already classified as belonging to the crack.
A method for the detection of cracks using multioriented Gabor filters is presented in [3].The authors F.S. Abas and K. Martinez described a method for the extraction of distinguishable features from crack patterns, particularly those in paintings. First they filtered the selected crack image using 8 differently oriented Gober filters. Then they thinned the image to 1 pixel wide using a morphological thinning algorithm and implemented a crack following algorithm and generated statistical structure of global and local features from a chain code based representation.
Crack detection and removal bears certain similarities with methods proposed for the detection and removal of scratches and other artifacts from motion picture films [4]–
[6]. However, such methods rely on information obtained over several adjacent frames for both artifact detection and filling and, thus, are not directly applicable in the case of painting cracks. L. Joyeux, O.Buisson, B. Besserer and S. Boukir [4] have studied that
Line scratches are common degradations in motion picture films. They presented an efficient method for line scratches detection strengthened by a Kalman filter. A new interpolation technique, dealing with both low and high frequencies (i.e. film grain) around the line artifacts, is investigated to achieve a nearby invisible reconstruction of damaged areas.
Ani1 C. Kokaram, Robin D. Morris, William J. Fitzgerald, and Peter J. W. Rayner
[5] suggested that Abstract a right and dark flashes are typical artifacts in degraded motion picture material. The distortion is referred to as “dirt and sparkle” in the motion picture industry. This is caused either by dirt becoming attached to the frames of the film, or by the film material being abraded. The visual result is random patches of the frames having grey level values totally unrelated to the initial information at those sites. Further they studied that in order to restore the film without causing distortion to areas of the frames that are not affected; the locations of the blotches must be identified. Heuristic and model based methods for the detection of these missing data regions are proposed by the authors.
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R E F E R E N C E S
1.
Kurt lange, “Bending of sheet “, Hand book of metal forming, Mc-GRAW Hill
Publications Reprint 2006, PP. 300-332.
2.
S. K. Garg, “Workshop Technology (Manufacturing Processes)”, Laxmi Publication
(p) Ltd., Reprint 2006, PP. 429-432.
3.
Jong Gye Shin et.al. Tac Joon Park & Hyunjune Yim “ Roll Bending”, Tran,
ASME, J. Mechanical Design, 123 May 2001, PP 284-290
4. M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of
artworks,” IEEE Multimedia, vol. 7, no. 2, pp. 34–37, Jun. 2000.
5. F.S. Abas, and K. Martinez, Classification of Painting Cracks for Content-based
Analysis, in Proc. of IS&T/SPIE's l5th Annual Symposium on Electronic imaging:
Machine Vision Applications in Industrial InspectionVol. I l, pp. 149-160, 2003.
6. F. Abas and K. Martinez, “Craquelure analysis for content-based retrieval,” in Proc.
14th Int. Conf. Digital Signal Processing, vol. 1, 2002, pp. 111–114.
7. L. Joyeux, O. Buisson, B. Besserer, and S. Boukir, “Detection and removal of line scratches in motion picture films,” in Proc. IEEE Int. Conf. Computer Vision and
Pattern Recognition, 1999, pp. 548–553.
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PUBLICATIONS
1. Sachin V. Solanki, Prof. Mrs. A. R. Mahajan, Digital Image Processing
Approach for Inspecting and Interpolating Cracks in Digitized Pictures ,
International Conference On VLSI and Communication (ICVCom 09), 16 th
- 18 th
April
2009, Saintgits College of Engineering, Kottayam, Kerala.
2. Sachin V. Solanki, et all Inspection and Interpolation of Cracks Digitized Artistic
Pictures using Image Processing Approach , IEEE International Advanced Computing
Conference (IACC’09) 6-7 March 2009, Thapar University, Patiala, Punjab , VOL.No. -
,Issue No. ,pp(in case of journal paper)
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