A robust scheme for the identification of centerlines of moire fringes from optical experimental images

被引:0
|
作者
Palevicius, Paulius [1 ]
Sudintas, Antanas [2 ]
Fedaravicius, Algimantas [3 ]
Ragulskiene, Jurate [1 ]
机构
[1] Kaunas Univ Technol, Res Grp Math & Numer Anal Dynam Syst, Kaunas, Lithuania
[2] Kaunas Univ Technol, Dept Heat & Nucl Engn, Kaunas, Lithuania
[3] Kaunas Univ Technol, Inst Def Technol, Kaunas, Lithuania
关键词
moire fringes; centerline identification; pattern detection;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A robust scheme for the identification of centerlines of moire fringes from optical experimental images is proposed in this paper. The proposed computational algorithm comprises three basic steps: the thresholding of the experimental image, thinning of the projected grating based on morphological and mid-point detection rules and, finally, the reconstruction of the map of continuous curves from the binary matrix of pixels representing fringe centerlines. The first steps can be considered as adaptations of standard image processing techniques, while the identification and the reconstruction of continuous curves is the original contribution specifically developed for optical projection moire images. The functionality of such an approach is demonstrated for a demanding optical experimental image.
引用
收藏
页码:1807 / 1814
页数:8
相关论文
共 44 条
  • [21] Robust mosquito species identification from diverse body and wing images using deep learning
    Nolte, Kristopher
    Sauer, Felix Gregor
    Baumbach, Jan
    Kollmannsberger, Philip
    Lins, Christian
    Luehken, Renke
    PARASITES & VECTORS, 2024, 17 (01):
  • [22] ROBUST IDENTIFICATION OF MOTION AND OUT-OF-FOCUS BLUR PARAMETERS FROM BLURRED AND NOISY IMAGES
    FABIAN, R
    MALAH, D
    CVGIP-GRAPHICAL MODELS AND IMAGE PROCESSING, 1991, 53 (05): : 403 - 412
  • [23] Identification of layers in optical coherence tomography of skin: comparative analysis of experimental and Monte Carlo simulated images
    Shlivko, I. L.
    Kirillin, M. Yu.
    Donchenko, E. V.
    Ellinsky, D. O.
    Garanina, O. E.
    Neznakhina, M. S.
    Agrba, P. D.
    Kamensky, V. A.
    SKIN RESEARCH AND TECHNOLOGY, 2015, 21 (04) : 419 - 425
  • [24] Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images
    Kamran, Sharif Amit
    Hossain, Khondker Fariha
    Tavakkoli, Alireza
    Zuckerbrod, Stewart Lee
    Baker, Salah A.
    OPHTHALMIC MEDICAL IMAGE ANALYSIS, OMIA 2022, 2022, 13576 : 22 - 32
  • [25] AN ACCOUNT FOR POLARIZATION OF OPTICAL-FIELDS UPON STATISTICAL IDENTIFICATION OF OBJECTS FROM THEIR IMAGES
    USTINOV, ND
    ANUFRIEV, AV
    ZIMIN, YA
    VOLPOV, AL
    KVANTOVAYA ELEKTRONIKA, 1986, 13 (02): : 249 - 254
  • [26] Robust 3D reconstruction and identification of dendritic spines from optical microscopy imaging
    Janoos, Firdaus
    Mosaliganti, Kishore
    Xu, Xiaoyin
    Machiraju, Raghu
    Huang, Kun
    Wong, Stephen T. C.
    MEDICAL IMAGE ANALYSIS, 2009, 13 (01) : 167 - 179
  • [27] A Neural Network Approach to Retinal Layer Boundary Identification From Optical Coherence Tomography Images
    McDonough, Kevin
    Kolmanovsky, Ilya
    Glybina, Inna V.
    2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB), 2015, : 194 - 201
  • [28] Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images
    Ji, Qingge
    Huang, Jie
    He, Wenjie
    Sun, Yankui
    ALGORITHMS, 2019, 12 (03)
  • [29] EXPLOITING LOW-RANK STRUCTURES FROM CROSS-CAMERA IMAGES FOR ROBUST PERSON RE-IDENTIFICATION
    Fu, Ming-Hang
    Wang, Yu-Chiang Frank
    Chen, Chu-Song
    2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 2427 - 2431
  • [30] Comparison of Subjective and Objective Methods of Corneoscleral Limbus Identification from Anterior Segment Optical Coherence Tomography Images
    Skrok, Marta K.
    Alonso-Caneiro, David
    Przezdziecka-Dolyk, Joanna
    Siedlecki, Damian
    OPTOMETRY AND VISION SCIENCE, 2021, 98 (02) : 127 - 136