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1 G Journal of the Korea Concrete Institute Vol. 20, No. 4, pp. 513~522, August, 2008 PVA-ECC» ½ 1) Á 2) Á½ 2) * 1) û w m œw 2) w w» y œw Fiber Classification and Detection Technique Proposed for Applying on the PVA-ECC Sectional Image Yun Yong Kim, 1) Bang Yeon Lee, 2) and Jin-Keun Kim 2) * 1) DeptU of Civil Engineering, Chungnam National University, Daejeon , Korea 2) DeptU of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon , Korea ABSTRACT : The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC (Polyvinyl alcohol- Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction. Keywords : PVA fiber, categorization, artificial neural network (ANN), watershed algorithm, morphological reconstruction 1. ECC (engineered cementitious composite) š w k 2% ü w y w gj p, w x y š w s p p (Portland cement) w w gj p/ k w w p w, w ¼ 1) (chopped fibers) xk» p ü sw. ECC w ƒ p ³ ƒ w x» w w e. sƒw w,, X-ray n mw, k, w sƒw» š. w 2-6) sƒw ƒ *Corresponding author Received February 4, 2008, Revised May 18, 2008, Accepted May 19, by Korea Concrete Institute š w sƒw. Ozyurt 7) AC-impedance Ÿ» (spectroscopy) w» w w sƒw w. ù k š w. x ¾ PVA (polyvinyl alcohol)» sƒw w ƒ w wš, sƒw p l w w» 8). p p p PVA ƒ û» Ÿwx w w». w w» w p q p q w p xÿ w. w û w. xÿ x (fluorescence microscope) w w Torigoe 9) w. ù xÿ w w j» ù š, ƒ 513

2 는 섬유의 개수를 수작업에 의하여 파악한 후, 이 자료 를 바탕으로 섬유분산성을 파악한 초보적인 단계였다. 따 라서 이 방법은 블록의 크기에 따라 섬유분산성 계수가 다르게 평가되는 등 정량적인 평가를 하기에는 뚜렷한 한계를 갖고 있다. 한편, 김윤용 등 은 이러한 한계를 극 복하기 위하여 이미지 프로세싱 기법과 통계적인 방법을 응용하여 자동으로 분산성을 평가할 수 있는 기법을 제 안한 바 있다. 제안된 이미지 프로세싱 기법은 형광 디 지털 이미지를 우선 이진화 한 후, 이를 통하여 획득된 1차 검출 이미지에서 섬유 이미지의 유형을 4가지로 임 의 분류하여, 그 유형에 따라 분수령 알고리즘 (watershed algorithm) 을 적용하는 절차를 포함하고 있다. 이 연구에서는 기존의 연구 를 토대로 섬유의 유형을 보다 합리적으로 분류할 수 있도록 인공신경회로망 (ANN; artificial neural network)을 적용하였다. 또한 분류된 각각의 섬유 이미지 종류에 따라 분수령 알고리즘 (watershed algorithm)과 형태학적 재구성 (morphological reconstruction) 을 적용함으로써 섬유 검출의 정확도를 증진시키는 기법 을 개발하였다. 8) 10) 7) Fig. 1 8) Composite image obtained by combining original grayscale image with detected binary image (the white line surrounding gray particles) 섬유 이미지의 섬유 검출 기법 개요 마이크로역학 기법 을 적용하여 우수한 성능을 갖도록 설계된 PVA-ECC 실험체를 28일 동안 수중양생을 실시 하였으며, 양생된 실험체의 양 단면을 다이아몬드 커터 를 이용하여 두께 13 mm, 폭 36 mm, 높이 20 mm로 재단 하여 섬유분산성 평가를 위한 시편을 제작하였다. 형광 현미경으로부터 선명한 이미지를 얻기 위하여 시편의 표 면을 그라인더로 연마한 후, 형광현미경 (Olympus, BX51) 을 통하여 시편을 관찰하였다. CCD가 탑재된 형광현미 경을 통하여 40배로 확대하여 촬영된 섬유 이미지에서 섬유만을 검출하기 위하여 먼저 칼라 이미지 (RGB image) 를 회색 이미지 (grayscale image)로 변환한 후 회색 이미 지를 이진 이미지 (binary image)로 변환하는 과정이 필요 하다. 그러나 이러한 일반적인 절차만으로는 Fig. 1의 B, D와 같이 모든 섬유를 정확히 검출하기 어렵다. 즉, 인 접한 여러 개의 섬유들이 하나의 섬유로 잘못 검출되는 오류가 빈번히 발생하게 된다. 검출 성능을 향상시키기 위하여 이 연구에서는 2단계의 추가 알고리즘을 제안하 였다. 1단계는 분류 알고리즘 (Fig. 2)으로서 이진화 기 법 에 의하여 1차 검출된 섬유 이미지 중에서 정확히 검출된 이미지와 여러 개의 섬유가 하나로 잘못 검출된 이미지를 유형별로 분류하는 것으로 이 연구에서는 ANN 을 분류기로 이용하였다. 2단계는 1차 검출 알고리즘으 로 잘못 검출된 섬유 이미지를 정확히 재검출하기 위한 목적으로 개발하였으며, 분수령 알고리즘과 형태학적 재 구성 기법을 적용하였다. 1) 10) 514 한국콘크리트학회 논문집 제20권 제4호 (2008) Fig Proposed classification system 섬유 이미지의 분류 섬유 이미지의 유형 섬유 이미지를 분류하기 위한 첫 번째 과정은 정확히 검출된 섬유 이미지와 인접한 여러 개의 섬유들이 하나 의 섬유로 잘못 검출된 섬유 이미지를 유형별로 구분하 는 것이다. 이를 위하여 이미지 밝기의 정점 (peak)을 분 석하여 물체를 구분하는 분수령 알고리즘 을 적용하였 다. 분수령 알고리즘을 적용함으로써 1차 검출된 섬유 이미지는 이미지의 밝기 값에 따른 정점의 개수를 물체 의 개수로 인식한다. Table 1은 이진화 기법 에 의하여 1차 검출된 섬유의 개수, 분수령 알고리즘에 의해 구분된 물체의 개수, 그 리고 원본 이미지로 직접 판단한 섬유의 개수를 각각 비 교한 것이다. Type S1은 3가지의 경우 모두가 1개의 섬 유 이미지이므로 1차 검출 결과가 정확하다고 판단할 수 있다. 따라서 추가적인 검출 과정이 필요하지 않다. 다 른 유형의 섬유 이미지는 분수령 알고리즘에 의해 구분 11) 10)

3 Table 1 Type of fiber image Type Number of fibers (thresholding algorithm 10P ) Number of segmented objects(watershed algorithm) Most probable number of fibers* Example Single fiber Type S Type N1 1 More than 2 1 Type N2 1 More than 2 1 Possible single fiber Type N3 1 More than 2 More than 2 Type N4 1 More than 2 More than 2 *These numbers are obtained from fluorescence image observations ƒ 2 w w x» 4ƒ w (Fig. 3). Type N1 Type N2 š q w wù w w š w w. š w w ù» (irregularity) w w w (over-segmentation) w. 12) 1 ƒ v w. x d Type N1 w w sww x ƒ¾, Type N2 w w ƒ¾ ƒ j x ùkü. Type N3 Type S1, Type N4 Type N1 Type Fig. 3 Typical fiber image according to fiber image types N2 x. Type N3 Type N4 x y w» w ƒ. p, Type N4 x ¼ ƒ ¼ p» w. š w w ƒ w š w v ƒ p l w» w x w» w p (feature extraction). p j 2ƒ., x w w ùküš x w ù kü x w w. š wš w w v w w. p,, x e. w p» x w ww p. e w (random) sw» w p. Fig. 3 y w x x w w p. x w x» 13) (A ob ), w (convex hull) (A ch ), (A cc ), ¼ (perimeter: l p ), ¼ (l l ). ù w x» x w p ƒ w z (rotation), (translation), j» (scale), š» y (illumination change) y w PVA-ECC» 515

4 p w. 4ƒ x, Type N1, Type N2, Type N3 Type N4 w» w p w y w p ƒ 5 p x» w. p Solidity (F s ) w ù. Type N1 Type N2 x x w ww, txw (1). F s A ob A ch p Packing density (F c ) w w ù. p Type N1 x x w ww, txw (2). (1) F c A ob A cc F c Fig. 4(a) s w ¼ ƒ ¼ 0 ƒ¾ ùküš, Fig. 4(b) ƒ ¾ 1 ƒ¾ ùkü. w F c ƒ» ƒ (3)»,» ùký (Fig. 5). F c πdl/4 d d DPT πl 2 θ /4 l d/ DPT θ» θ ƒ» ƒ, d. l ƒ ùkü k ¼. F c» ƒ, θ w cosθ. p (F p ) ¼ ù. F p Type N3 x x w ww txw (4). (2) (3) Fig. 5 The relationship between the inclined angle of the fiber and the packing density; (a) plan view on the cutting plane; and (b) side view at the section of A-A. s³ ¼ (l avg ) ƒ ƒ ù ( (5) (6)). Type N2 Type N4 x w w. F l F rl l l A ob l l l avg Fig. 6 1,350 4ƒ x wš x 5ƒ p s v txw. l» ANN w. (5) (6) F p l p A ob p F l F rl ¼ (4) Fig. 4 Two types of objects surrounded by a circumscribed circle for (a) relatively low packing density and (b) relatively high packing density Fig. 6 Scatter plot matrix of F s, F c, F p, F l, and F rl by group 516 w gj pwz 20«4y (2008)

5 2.2.3 ANN w» 1) ANN ANN l w w, l sw ƒ txw y w. w ANN w p j (complexity) w, p j d (hidden layer), ( w ; transfer function), ƒ w ƒ e (weight) (range of interconnections), š w. ANN ANN ƒ ew w t ƒ w w w (Ockam's Razor ). 14) Moody and Yarvin w w, w w ƒ ƒ ùkü y w. p ƒ sw ù x w w ùkü. d ƒ (7) w r k p w (hyperbolic tangent sigmoid function) w w, d (output layer) (8) x w w w. f( x) f( x) x e 2x w d w (sigmoidal transfer function) w wù d t š» wù d w. 15,16) ƒ e w w w yw» w ƒ e (weight decay) w ƒ š 17) (Levenberg-Marquardt algorithm) w. w š w (error function) (9). E w ( ) ε w old 2 ( ) + λ w new w old w i i C ( ) 2» λ (damping parameter) š, α (regularization parameter). w C ƒ e w ùküš, W old W new ƒƒ x ƒ e ƒ e ùkü. (9) w x ƒ zw w» w ƒ (step) j» w» w. w ƒ e w, w x ùküš (10). ε( w old ) M 1 -- t m y m w 2 old m 1 2 [ ( )] α 2 (7) (8) (9) (10)» t y ƒƒ t (target value) (output value) ùkü. q (back-propagation) p j ƒ e w d mw d (input layer). ƒ e (11). 1 [ ( )] [ ( ) α ] w new w old Z T Z + I λ + α Z T ε w old + w old (11)» Z ƒ e 1 r w Jacobian matrix š, I identity matrix. 2) w w» w 1 l 10 ¾ ƒ gƒ 10-fold ww l w š w. ƒ e» w w vwš 10 z ww » w 5 p š ANN w w d t Table 2. Fig. 7 w l l ( y ) ùkü. Fig. 7 y w y» x ANN w x e w. x sƒw» w ANN l w w mw w. Table 3 Jack knife w ùkü, Tables 4 5 Table 2 Desired outputs for learning Type N1 Type N2 Type N3 Type N4 Node Node Node Node Fig. 7 Performance on the training sets and test sets versus number of hidden nodes PVA-ECC» 517

6 Table 3 Performance of the classifier (ANN) Actual classification x w w ANN ƒ e ùkü. Tables 4 5 (12) mw œ z» w. yx ( ) Ω f sig( + ) + β UBO w x b (12)» x 5ƒ p (13) (14) w 1 l 1 y. W b ƒƒ Table 3 ƒ e, Ω β Table 4 ƒ e. x f mapping (<F s F c F p F l F rl > T ) (13) f mapping ( t) ANN classification Type1 Type2 Type3 Type4 Total Type N Type N Type N Type N Accuracy (%) ( t 1 t 1, min ) ( t 2 t 2, min ) T 1Λ t 1, max t 1, min t 2, max t 2, min (14) y w» w š w. š 18) Digabel and Lantuèjoul w 19) š, Beucher and Lantuèjoul w z w š y. x š Fig. 8 ùkü» w». 20) Vincent 11) w e (immersion analysis) w xkw (morphological reconstruction) 2.2.1» w watershed algorithm w w» (irregularity) w w x w. w w» w xkw» w. t xkw j (mask image: g) f (marker image: f) q (dilation) j ƒ y ¾ w w. j» w w ƒ y» f g w. 21) Fig. 9 ùkü.» j, f j l (h).» ƒ p ƒƒ (0.3913, 0.028, , , ), ƒƒ (1, 0.962, , , ) š (Watershed algorithm) Type N3 Type N4 wù Table 4 Weights and biases between input nodes and hidden nodes Fig. 8 Watershed segmentation No. of hidden Weight No. of input node node Bias Table 5 Weights and biases between hidden nodes and hidden nodes No. of hidden node Weight No. of input node Bias w gj pwz 20«4y (2008)

7 Fig. 9 Morphological reconstruction to minimize over- segmentation in detection of Type 3 fiber image j» / šƒ, w w 20 w. Fig. 10 w š ùkü. 1 ( y» ) w wù š w w ù., š w ƒ 1 y w 1 ùkü» Type S1 š, Type N. Type N wù ùkü š w w ùkù (Type N1 Type N2) 1 w 1 (Type N3 Type N4) ù, ANN w ƒƒ x. Type S1, Type N1 Type N2 1 ƒ y» ƒ š, Type N3 š w y w ƒ w. Type N4 ¼» w w ƒ w», yw» w xkw w z, š w. 3. w» z y sƒw» w j» š w w j» ( ) w, w š w. 3.1» w š w w w. Fig. 11 š mw w w. Table 6 w. Table 6 y w š mw w j» 6.84% j. y» w Fig 10. Flow chart of enhanced detection algorithm Fig. 11 Comparison of original fiber image and detected fiber image PVA-ECC» 519

8 Diameter of fiber image, which is equivalent to the minor axis length Real Test 1 Test 2 Average Error (%) 6.84 Diameter of fiber (µm) Table 6 분리 (segmentation)하는 밝기값의 역치 (threshold)가 다 소 작은 값이기 때문이다. 따라서 전체 면적 대비 섬유 의 면적을 계산하는 등 섬유의 크기를 정확히 검출할 필 요가 있는 경우에는 최적의 역치를 결정할 필요가 있다. 그러나 이 연구와 같이 섬유의 분산성을 평가하는 것이 목적인 경우에는 섬유의 크기보다 섬유의 위치가 더욱 중요하므로, 10% 이내의 크기 오차는 허용할 수 있는 범 위인 것으로 판단하였다. 3.2 섬유 검출 성능 제안된 기법의 검증을 위하여 섬유 검출 성능을 검증 하였는데, 이를 위하여 총 3가지 알고리즘의 결과를 서 로 비교하였다. 비교한 알고리즘은 1차 검출 후 추가적 인 검출 과정이 없는 이진화 알고리즘, Type N3과 Type N4를 재검출하기 위하여 분수령 알고리즘을 추가한 알 고리즘, 그리고 Type N4를 검출하는데 선수행 과정 (preprocess)으로 형태학적 재구성을 추가한 알고리즘이다. 3 번째 알고리즘이 이 연구에서 최종 제안한 알고리즘이다. Fig. 12는 각 알고리즘 별 검출 성능을 평가하기 위하여 사용된 섬유 이미지를 나타내며, 개별 섬유로 검출된 결 과를 각 섬유의 무게 중심에 흰 점으로 나타내었다 (Fig. 13). Fig. 13에서 A, B, C, D는 전형적인 Type N3 섬유 이미지로서 제안된 알고리즘을 적용함으로써 기존의 이 진화 알고리즘에 의하여 발생된 오류, 즉 인접한 여러 개의 섬유들을 1개의 이미지로 검출한 오류가 바로 잡 힌 것을 확인할 수 있다. Fig. 13에서 E, F, G는 전형적 인 Type N4 섬유 이미지를 나타내며, Fig. 13(c)에서 알 수 있듯이 형태학적 재구성을 우선 수행함으로써 분수령 알고리즘에 의하여 발생할 수 있는 과도 분할 문제를 해 결할 수 있음을 알 수 있다. 이렇게 검출된 섬유는 김윤 Fig. 13 Test results, where the white point located on the grayscale image (fiber) indicates the center of the detected fiber image 용 등 이 제시한 기법을 통하여 섬유의 분산성을 정량 적으로 평가할 수 있다. 8) 4. Fig. 12 Test image 520 한국콘크리트학회 논문집 제20권 제4호 (2008) 결 론 이 연구에서는 PVA-ECC의 섬유 분산성을 보다 정확하 게 평가할 수 있는 섬유 검출 기법을 제안하였다. 제안된 기법은 섬유 이미지의 유형별 분류 기법, 분류된 섬유의 유형에 따라 검출 성능을 향상시키는 기법으로 이루어진 다. 이 기법의 타당성을 검증하기 위하여 검증 실험을 수 행하였으며, 이를 통하여 얻은 결론은 다음과 같다. 1) PVA-ECC의 섬유 분산성을 평가하기 위한 섬유 검 출 기법의 절차는 1) 시편의 준비, 2) 형광 이미지 획득, 3) 섬유 이미지 검출로 이루어진다. 제안된 검

9 š mw xÿ p (binary) y. y» w 1 w ƒ 1 ƒ w. 2) 1 w» w x w., 1 š w» (peak) ƒ 1 (Type S) 2 (Type N) w. Type N x x p š w 4ƒ x (Type N1, Type N2, Type N3, Type N4) w, x p F s, F c, F p, F l, F rl š l w ANN w w x» w. 3) 5 x 1 ƒ x (Type N3 Type N4) w w š w. Type N3 w š w w w š, Type N4 š w» w xkw ww z š w w š w. 4) w» y z w» w w, 6.84% ƒ w ùkû. sƒw» w e w j»ƒ w» x w q w. w» š w š w, w š w x. w w p» (R ) w, Ì ¾. š x 1. Li, V. C., Wang, S., and Wu, C., Tensile Strain- Hardening Behavior of Polyvinyl Alcohol-Engineered Cementitious Composite (PVA-ECC), ACI Materials Journal, Vol. 98, No. 6, 2001, pp. 483~ Guild, F. J. and Summerscales, J., Microstructural Image Analysis Applied to Fibre Composites Materials: A Review, Composites, Vol. 24, No. 5, 1993, pp. 383~ Yang, Y., Methods Study on Dispersion of Fibers in CFRC, Cement and Concrete Research, Vol. 32, 2002, pp. 747~ Chermant, J. L., Chermant, L., Coster, M., Dequiedt, A. S., and Redon, C., Some Fields of Applications of Automatic Image Analysis in Civil Engineering, Cement and Concrete Composites, Vol. 23, 2001, pp. 157~ Benson, S. D. P. and Karihaloo, B. L., CARDIFRCó- Manufacture and Constitutive Behavior, High Performance Fiber Reinforced Cement Composites (HPFRCC4), Ann Arbor, Mich. 2003, pp. 65~ Akkaya, Y., Shah, S. P. and Ankenman, B., Effect of Fiber Dispersion on Multiple Cracking of Cement Composites, Journal of Engineering Materials in Civil Engineering, Vol. 127, No. 4, 2001, pp. 311~ Ozyurt, N., Woo, L. Y, Mason, T. O., and Shah, S. P., Monitoring Fiber Dispersion in Fiber-Reinforced Cementitious Materials: Comparison of AC-Impedance Spectroscopy and Image Analysis, ACI Materials Journal, Vol. 103, No. 5, 2006, pp. 340~ ½,, ½, ½, v» w w sƒ, q wz, 27«, 2y, 2007, pp. 148~ Torigoe, S., Horikoshi, T., and Ogawa, A., Study on Evaluation Method for PVA Fiber Distribution in Engineered Cementitious Composite, Journal of Advanced Concrete Technology, Vol. 1, No. 3, 2003, pp. 265~ Otsu, N. A., Threshold Selection Method from Gray Level Histogram, IEEE Transactions on Systems, Man, and Cybernetics, SMC, Vol. 9, No. 1, 1979, pp. 62~ Vincent, L. and Soille, P., Watesheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, 1991, pp. 583~ Beucher, S., The Watershed Transformation Applied to Image Segmentation, Conference on Signal and Image Processing in Microscopy and Microanalysis, September, 1991, pp. 299~ Barber, C. B., Dobkin, D. P., and Huhdanpaa, H. T., The Quickhull Algorithm for Convex Hulls, ACM Transactions on Mathematical Software, Vol. 22, No. 4, 1996, pp. 469~ Moody, J. E. and Yarvin, N., Networks with Learned unit Response Functions, In: Moody, J. E., Hanson, S. J., Lippmann, R. P., Editors. Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, 1992, pp. 1048~ Hecht-Nielsen, R., Theory of the Backpropogation Neural Network, In Proceedings of International Joint Conference on Neural Networks, Washington, D.C., USA: IEEE, V. I, 1989, pp. 593~ Barron, A. R., Universal Approximation Bounds for Superpositions of a Sigmoidal Function, IEEE Transactions of Information Theory, Vol. 39, No. 3, 1993, pp. 930~ Krogh, A. and Hertz, J. A., A Simple Weight Decay Can Improve Generalization, In: Moody, J. E., Hanson, S. J., Lippmann, R. P., Editors. Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, 1992, pp. 950~ Digabel, H. and Lantu`ejoul, C. Iterative algorithms, in Proc. PVA-ECC» 521

10 2nd European Symp. Quantitative Analysis of Microstructures in Material Science, Biology and Medicine, Caen, France, Oct. 1977, Chermant, J. L. (Ed.) Stuttgart, West Germany, Riederer Verlag, 1978, pp. 85~ Beucher, S. and Lantuèjoul, C., Use of Watershed in Contour Detection, in International Workshop on Image Processing: Real-time edge and motion detection/estimation. Rennes, France, 1979, pp. 17~ Feng, J. and Lu, H., Peak Analysis of Grayscale Image: Algorithm and Application, International Journal of Information Technology, Vol. 12, No. 5, 2006, pp. 11~ Vincent, L., Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms, IEEE Transactions on Image Processing, Vol. 2, No. 2, 1993, pp. 176~201. w w ƒ p ³ ƒ w x» s p w w e. ù PVA w w PVA p û PVA p w e s p sƒ ƒ w. PVA w w s p sƒw ƒ w w w k š w. w š xÿ x w x wš, p š (watershed algorithm) xkw (morphological reconstruction) w y w. 5ƒ x w, œ z (ANN)» y w» w x p ùkü 5ƒ p, F s, F c, F p, F l F rl w. p w l w ANN w w» w x w w. w 5ƒ x š xkw mw y w» w. w : PVA, œ z (ANN), š, xkw 522 w gj pwz 20«4y (2008)

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