Proceedings of Topical Meeting on Optoinformatics (pdf-format, 1.21 ...

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16 OPTOINFORMATICS’05 MODELLING THE SPECKLE PATTERNS OF DEFORMABLE SURFACE FOR RIGID BODY MOTION ELIMINATION Fityo N. V. Karpenko Physico-Mechanical Institute ong>ofong> NAS ong>ofong> Ukraine, Lviv, Ukraine Technique for determination and elimination ong>ofong> rigid body motion (RBM) during study ong>ofong> surface deformation fields is described. Performance ong>ofong> the given technique is shown by using computer-generated speckle-patterns. The digital speckle correlation (DSC) technique is one ong>ofong> the techniques for study surface deformation in fracture mechanics [1] . DSC technique is based on comparison ong>ofong> speckle-patterns ong>ofong> specimen surfaces before and after loading. This technique allows us to receive 2D discrete field ong>ofong> displacements using cross-correlation ong>ofong> the initial specklepattern subimages and appropriate subimages ong>ofong> the strained surface speckle-pattern. However, such procedure ong>ofong> displacement field determination leads to the situation when displacement fields will contain not only field ong>ofong> deformations, but also the displacement ong>ofong> whole pattern, ong>ofong>ten called rigid body motion (RBM) [2] . This paper represents the new technique for determination ong>ofong> RBM proposed by the author. Analysis ong>ofong> the given technique ong>ofong> RBM determination is performed with a help ong>ofong> computer generated specklepatterns. In order to analyze mentioned above technique, the test speckle-patterns were generated according to [3] . The dimensions ong>ofong> the patterns were 512 by 512 pixels. Taking into account possible nonuniformities ong>ofong> the specimen surface and other errors during experiments, the random errors were introduced along both x- and y- axis. Deformation ong>ofong> the pattern was introduced only along x-axis: left edge ong>ofong> the pattern was fixed and right edge was stretched. The area with the absence ong>ofong> deformation was also formed in the image. Speckle-patterns before and after loading were divided into the equal quantity ong>ofong> subimages with dimensions 32 by 32 pixels. To achieve the subpixel resolution ong>ofong> displacement determination, the interpolation algorithm represented in [3] was used. We have found the dependence between the values ong>ofong> deformation (in pixels) and RBM (in pixels) for appropriate determination ong>ofong> zero order deformation area by this technique. Reasons ong>ofong> improper or impossible determination ong>ofong> zero order deformation areas for given parameters were analyzed. Presentation ong>ofong> this paper was partially supported by ICO travel-grant program. 1. Digital speckle pattern interferometry and related techniques / Ed. by P.K.Rastogi. – Chichester: John Wiley and Sons, 2001. 2. Муравський Л.И., Фитьо Н.В. Оценка перемещений поверхностей деформируемых объектов и твердых тел техникой оптической спекл-корреляции // Оптический журнал, 72, №5, 67-72, (2005). 3. Sjödahl M., Benckert L.R. Electronic speckle photography: analysis ong>ofong> an algorithm giving the displacement with subpixel accuracy // Appl. Opt., 32, 2278-2284, (1993).

SAINT-PETERSBURG, October 17 – 20, 2005 17 IMAGE ENHANCEMENT BY IMPROVED CONTRAST- STRETCHING TECHNIQUE Ching-Chung Yang, Department ong>ofong> Electronic Engineering, Far East College, 49 Chung Hua Road, Hsin-Shih, Tainan, Taiwan, R. O. C. E-mail: yang10.cc@msa.hinet.net We demonstrate a modified contrast-stretching method to enhance a nonuniformly illuminated image. Low-frequency information ong>ofong> the image is still processed by the conventional technique, while the high-frequency information is exaggerated by the log transformation. The final image improves the contrast to a better extent. This article demonstrates a brand new approach to sharpening a non-uniformly illuminated image. The proposed method at first separates the original image matrix to two sub-matrices representing the high and low frequency information. The low frequency submatrix is then processed by the contrast-stretching manipulation, while the high frequency sub-matrix processed by the log transformation. At last we reconstruct these two new submatrices to derive the enhanced final image. The element in the high frequency sub-matrix is represented by (I 1 -I 2 )/2, and that ong>ofong> the low frequency is (I 1 +I 2 )/2. Where I 1 I 2 are any two near-by pixels among the original image matrix. In this study, the low frequency information is processed by the conventional rubberband transformation to raise the image’s contrast. In the meantime, the near-by pixels’ visibility [(I 1 -I 2 )/(I 1 +I 2 )] is also improved for that the high frequency information is simultaneously enhanced by the log transformation. Thus the original image could be further sharpened on the bases ong>ofong> the traditional algorithm. We illustrate our method by employing a temple’s image that is partially shaded indoors as shown in Figure 1(a). The original image looks unclear in the shaded area owing to the lack ong>ofong> illumination. Our method could sharpen the whole image to a better extent in comparison with the conventional contrast-stretching approach. This is shown in Figure 1(b)1(c). To quantitatively evaluate our method, we compare the histograms ong>ofong> the processed images by Matlab 6.0. It is clear that the original picture gathers its pixels in lower graylevels as shown in Figure 2(a). Although the conventional method shifts these pixels toward the higher gray-level as shown in Figure 2(b), the discontinuity happening in the lower gray-level region somehow degrades its own contrast. While by using our method, the final image continuously spreads its pixels from lower to higher gray-levels without any discontinuity as shown in Figure 2(c). This would help to increase the overall contrast ong>ofong> the original image. We also calculate the statistical data including the mean value, standard deviation, minimum, and maximum. The calculated mean value implies that our method is essentially based on the conventional contrast-stretching approach. While by comparison with the standard deviations, it is obvious that our image has better performance in the contrast property. This is also shown in Table 1.

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