Abstract

The extraction of the valid measurement region from the interference fringe pattern is a significant step when measuring gear tooth flank form deviation with grazing incidence interferometry, which will affect the measurement accuracy. In order to overcome the drawback of the conventionally used method in which the object image pattern must be captured, an improved segmentation approach is proposed in this paper. The interference fringe patterns feature, which is smoothed by the nonlinear diffusion, would be extracted by the structure tensor first. And then they are incorporated into the vector-valued Chan-Vese model to extract the valid measurement region. This method is verified in a variety of interference fringe patterns, and the segmentation results show its feasibility and accuracy.

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  1. S. Fang, L. Wang, P. Yang, L. Meng, M. Komori, and A. KuboImprovement of the oblique-incidence optical interferometric system to measure tooth flanks of involute helical gearsOpt. Soc. Am.201128590595
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  6. S. Fang, L. Wang, P. Yang, L. Meng, and M. KomoriObject-image-based method to construct an unweighted quality map for phase extraction and phase unwrappingAppl. Opt.20115014821487
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  33. X. Wang, S. Fang, and X. ZhuWeighted least-squares phase unwrapping algorithm based on a non-interfering image of an objectAppl. Opt.2017564543

Other (33)

S. Fang, L. Wang, P. Yang, L. Meng, M. Komori, and A. KuboImprovement of the oblique-incidence optical interferometric system to measure tooth flanks of involute helical gearsOpt. Soc. Am.201128590595

S. Fang, L. Wang, S. Liu, M. M. Komori, and A. KuboPositioning the actual interference fringe pattern on the tooth flank in measuring gear tooth flanks by laser interferometryOpt. Eng.201150055601

S.-P. Fang, L.-J. Wang, M. Komori, and A. KuboDesign of laser interferometric system for measurement of gear tooth flankOptik - Int. J. Light Electron Opt.201112213011304

H. A. Vrooman and A. A. MaasImage processing algorithms for the analysis of phase-shifted speckle interference patternsAppl. Opt.19913016361641

T. Ino and T. YatagaiOblique incidence interferometry for gear-tooth surface profilingProc. SPIE1720464469

S. Fang, L. Wang, P. Yang, L. Meng, and M. KomoriObject-image-based method to construct an unweighted quality map for phase extraction and phase unwrappingAppl. Opt.20115014821487

V. Estellers, D. Zosso, R. Lai, S. Osher, J. Thiran, and X. BressonAn efficient algorithm for level set method preserving distance functionIEEE Trans. Image Process. Publ. IEEE Signal Process. Soc.20122147224734

J. Zhang, G. Wang, Z. Pan, and B. HuangUnsupervised color texture segmentation using active contour model and oscillating informationNinth International Conference on Machine VisionInternational Society for Optics and Photonics20171034116

A. Tatu and S. BansalA novel active contour model for texture segmentationInternational Workshop on Energy Minimization Methods in Computer Vision and Pattern RecognitionSpringer International Publishing2015223236

K. Sarafrazi, M. Yazdi, and M. J. AbediniA new image texture segmentation based on contourlet fractal featuresArabian J. Sci. Eng.20133834373449

K. O. Na and C. KimUnsupervised texture segmentation of natural scene images using region-based markov random fieldJ. Signal Process. Syst.201683423436

T. Boonnuk, S. Srisuk, and T. SripramongTexture segmentation using active contour model with edge flow vectorInt. J. Inf. Electron. Eng.20155107

N. Paragios and R. DericheGeodesic active regions and level set methods for supervised texture segmentationInt. J. Computer Vision200246223247

C. Sagiv, N. A. Sochen, and Y. Y. ZeeviTexture segmentation via a diffusion-segmentation scheme in the gabor feature spaceProc. Texture 2002, 2nd International Workshop on Texture Analysis and Synthesis2002123128

B. Sandberg, T. Chan, and L. VeseUCLA Department of Mathematics CAM ReportA level-set and Gabor-based active contour algorithm for segmenting textured imagesCiteseer20020239

K. Betty and P. Sasi KiranOptimal gabor filter for multi scale texture segmentation with color indicationIJRCCT20154708713

M. Rousson, T. Brox, and R. DericheActive unsupervised texture segmentation on a diffusion based feature spaceProc. Computer Society Conference on Computer Vision and Pattern RecognitionIEEE2003II-699704

T. Brox and J. WeickertNonlinear matrix diffusion for optic flow estimationPattern RecognitionSpringer2002446453

R. M. Patil and A. A. ManjrekarA novel image classification and image retrieval using saliency driven multiscale nonlinear diffusion filtering and linear distance codingInternational Conference on Information Processing2016338343

N. H. Hà and P. T. NhuNoise filtering and detecting edge image in nonlinear anisotropic diffusionVietnam J. Sci. Technol.20104819

C. Tang, L. Han, H. Ren, D. Zhou, Y. Chang, X. Wang, and X. CuiSecond-order oriented partial-differential equations for denoising in electronic-speckle-pattern interferometry fringesOpt. Lett.20083321792181

J. WeickertAnisotropic diffusion in image processingB.g.teubner Stuttgart199616272

T. Brox, J. Weickert, B. Burgeth, and P. MrázekNonlinear structure tensorsImage Vision Comput.2006244155

P. Perona and J. MalikScale-space and edge detection using anisotropic diffusionIEEE Trans. Pattern Anal. Mach. Intell.199012629639

L. Alvarez, P.-L. Lions, and J.-M. MorelImage selective smoothing and edge detection by nonlinear diffusion. IISIAM J. Numer. Anal.199229845866

G. Gerig, O. Kübler, R. Kikinis, and F. A. JoleszNonlinear anisotropic filtering of MRI dataIEEE Trans. Med. Imag.199211221232

X. Yang, Q. Yu, and S. FuAn algorithm for estimating both fringe orientation and fringe densityOpt. Commun.2007274286292

T. F. Chan and L. VeseActive contours without edgesIEEE Trans. Image Process.200110266277

T. F. Chan, B. Y. Sandberg, and L. A. VeseActive contours without edges for vector-valued imagesJ. Visual Commun. Image Representation200011130141

M. MeilǎComparing clusterings—an information based distancJournal of Multivariate Analysis200798873895

D. Martin, C. Fowlkes, D. Tal, and J. MalikA database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statisticsComputer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference onIEEE2001416423

C. Pantofaru and M. HebertA comparison of image segmentation algorithmsBiomedical Engineering International Conference201514

X. Wang, S. Fang, and X. ZhuWeighted least-squares phase unwrapping algorithm based on a non-interfering image of an objectAppl. Opt.2017564543

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