{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T14:31:35Z","timestamp":1778509895710,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ATASYS","award":["IP-UNIST-06"],"award-info":[{"award-number":["IP-UNIST-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Standard fringe-based structured-light processing estimates wrapped phase from phase-shifted sinusoidal images and commonly relies on phase unwrapping to obtain a globally consistent phase representation. In practical measurements, this approach may become unstable on reflective objects and under low or non-uniform illumination, where the recorded fringe signal is distorted and the recovered phase becomes unreliable. To address these limitations, we propose a correspondence extraction method based on subpixel peak localization performed directly on phase-domain images. The wrapped phase is transformed into absolute value phase profiles, \u03a6=|\u03d5w|, whose local structure follows the projected fringe pattern and is less affected by object-dependent intensity variations. The proposed method reformulates correspondence extraction as a local signal-based estimation problem in the phase-domain, thereby reducing reliance on global phase-consistency constraints at the correspondence stage.\u00a0A practical advantage observed in the evaluated examples is that the method remained usable in some regions where the phase became locally flat because of low modulation, saturation, or reflective surface effects. In such regions, conventional processing relies on sufficiently reliable phase gradients and subsequent unwrapping, whereas the proposed method uses local peak geometry in the transformed phase representation.\u00a0In the implementation used here, Gray-code information is employed only for pixel-wise phase extension and reference indexing, not as a spatial phase-unwrapping mechanism. The method does not require machine learning models or training data and can be integrated as a correspondence analysis stage in practical structured-light systems.<\/jats:p>","DOI":"10.3390\/jimaging12050182","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T17:04:59Z","timestamp":1776963899000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Phase-Domain Peak-Based Correspondence Extraction for Robust Structured-Light Imaging"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2340-5767","authenticated-orcid":false,"given":"Andrijana","family":"\u0106urkovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Science, University of Split, 21000 Split, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-7928","authenticated-orcid":false,"given":"Milan","family":"\u0106urkovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8038-853X","authenticated-orcid":false,"given":"Alen","family":"Grebo","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1364\/AOP.3.000128","article-title":"Structured-Light 3D Surface Imaging: A Tutorial","volume":"3","author":"Geng","year":"2011","journal-title":"Adv. Opt. Photonics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.patcog.2003.10.002","article-title":"Pattern Codification Strategies in Structured Light Systems","volume":"37","author":"Salvi","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.optlaseng.2018.02.017","article-title":"High-Speed 3D Shape Measurement with Structured Light Methods: A Review","volume":"106","author":"Zhang","year":"2018","journal-title":"Opt. Lasers Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, H., Li, Y., Li, Z., Gui, W., Wang, X., Zhang, C., Liang, X., and Li, X. (2025). Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration. Sensors, 25.","DOI":"10.3390\/s25206296"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sanchez-Torres, M., Hern\u00e1ndez-Capuchin, I., Ram\u00edrez-Fern\u00e1ndez, C., Clemente, E., S\u00e1nchez-Gonz\u00e1lez, J.L.J., and L\u00f3pez-Mart\u00ednez, A. (2026). Recent Advances in Digital Fringe Projection Profilometry (2022\u20132025): Techniques, Applications, and Metrological Challenges\u2014A Review. Metrology, 6.","DOI":"10.3390\/metrology6010003"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.optlaseng.2016.04.022","article-title":"Temporal Phase Unwrapping Algorithms for Fringe Projection Profilometry: A Comparative Review","volume":"85","author":"Zuo","year":"2016","journal-title":"Opt. Lasers Eng."},{"key":"ref_7","unstructured":"Ghiglia, D.C., and Pritt, M.D. (1998). Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software, Wiley."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6565","DOI":"10.1364\/AO.38.006565","article-title":"Three-Dimensional Vision Based on a Combination of Gray-Code and Phase-Shift Light Projection: Analysis and Compensation of the Systematic Errors","volume":"38","author":"Sansoni","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1364\/OE.25.004700","article-title":"Phase-Shifting Profilometry Combined with Gray-Code Patterns Projection: Unwrapping Error Removal by an Adaptive Median Filter","volume":"25","author":"Zheng","year":"2017","journal-title":"Opt. Express"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0165-1684(86)90033-2","article-title":"Real-Time Numerical Peak Detector","volume":"11","author":"Blais","year":"1986","journal-title":"Signal Process."},{"key":"ref_11","unstructured":"Schmalz, C., Hofer, M., Schaffer, M., and Angelopoulou, E. (2012). Robust Single-Shot Structured Light 3D Scanning. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, 16\u201321 June 2012, IEEE."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2002.1017618","article-title":"Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints","volume":"24","author":"Bazen","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","unstructured":"Soifer, V.A., Kotlyar, V.V., Khonina, S.N., Khramov, A.G., and Skidanov, R.V. (1998). Image Recognition Using a Directional Field Technique. Proceedings of the SPIE, Optical Pattern Recognition IX, Orlando, FL, USA, 14\u201315 April 1998, SPIE."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.1364\/OE.449468","article-title":"Composite Fringe Projection Deep Learning Profilometry for Single-Shot Absolute 3D Shape Measurement","volume":"30","author":"Li","year":"2022","journal-title":"Opt. Express"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105204","DOI":"10.1016\/j.imavis.2024.105204","article-title":"Novel Approach for Fast Structured Light Framework Using Deep Learning","volume":"146","author":"Kim","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhong, C., Gao, Z., Wang, X., Shao, S., and Gao, C. (2019). Structured Light Three-Dimensional Measurement Based on Machine Learning. Sensors, 19.","DOI":"10.3390\/s19143229"},{"key":"ref_17","first-page":"19","article-title":"High-Quality 3D Shape Measurement Using Saturated Fringe Patterns","volume":"86","author":"Chen","year":"2016","journal-title":"Opt. Lasers Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7337","DOI":"10.1364\/OE.24.007337","article-title":"High Dynamic Range Real-Time 3D Shape Measurement","volume":"24","author":"Jiang","year":"2016","journal-title":"Opt. Express"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Z., Ma, R., and Duan, S. (2025). A Clustered Adaptive Exposure Time Selection Methodology for HDR Structured Light 3D Reconstruction. Sensors, 25.","DOI":"10.3390\/s25154786"},{"key":"ref_20","first-page":"105937","article-title":"Investigating the Precision of 3D Scanner Systems Based on Digital Fringe Projection Method for Biomedical Engineering Applications","volume":"127","author":"Norouzi","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110796","DOI":"10.1016\/j.measurement.2022.110796","article-title":"A Systematic Study and Framework of Fringe Projection Profilometry with Improved Measurement Performance for In-Situ LPBF Process Monitoring","volume":"190","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_22","first-page":"113816","article-title":"Optimizing Exposure Times of Structured Light Metrology Systems Using a Digital Twin","volume":"215","author":"Ojal","year":"2023","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Mao, J., Li, C., and Cao, J. (2026). Accurate 3D Structured-Light Measurement of Highly Reflective Objects. Photonics, 13.","DOI":"10.3390\/photonics13010005"},{"key":"ref_24","first-page":"120","article-title":"Fast Normalized Cross-Correlation","volume":"10","author":"Lewis","year":"1995","journal-title":"Vis. Interface"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer.","DOI":"10.1007\/978-1-84882-935-0"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T13:57:02Z","timestamp":1778507822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jimaging12050182"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12050182","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}