{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:23:52Z","timestamp":1768555432747,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,2,22]],"date-time":"2013-02-22T00:00:00Z","timestamp":1361491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The calibration of a projector-camera system is an essential step toward accurate 3-D measurement and environment-aware data projection applications, such as augmented reality. In this paper we present a two-stage easy-to-deploy strategy for robust calibration of both intrinsic and extrinsic parameters of a projector. Two key components of the system are the automatic generation of projected light patterns and the incremental calibration process. Based on the incremental strategy, the calibration process first establishes a set of initial parameters, and then it upgrades these parameters incrementally using the projection and captured images of dynamically-generated calibration patterns. The scene-driven light patterns allow the system to adapt itself to the pose of the calibration target, such that the difficulty in feature detection is greatly lowered. The strategy forms a closed-loop system that performs self-correction as more and more observations become available. Compared to the conventional method, which requires a time-consuming process for the acquisition of dense pixel correspondences, the proposed method deploys a homography-based coordinate computation, allowing the calibration time to be dramatically reduced. The experimental results indicate that an improvement of 70% in reprojection errors is achievable and 95% of the calibration time can be saved.<\/jats:p>","DOI":"10.3390\/s130202664","type":"journal-article","created":{"date-parts":[[2013,2,22]],"date-time":"2013-02-22T11:21:34Z","timestamp":1361532094000},"page":"2664-2681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An Incremental Target-Adapted Strategy for Active Geometric Calibration of Projector-Camera Systems"],"prefix":"10.3390","volume":"13","author":[{"given":"Chia-Yen","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, 700, Kaohsiung University Rd. Nan Tzu Dist., Kaohsiung 811, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsiang-Jen","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, 700, Kaohsiung University Rd. Nan Tzu Dist., Kaohsiung 811, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,2,22]]},"reference":[{"key":"ref_1","unstructured":"Kinect Calibration with OpenCV. Available online: http:\/\/www.informatik.uni-freiburg.de\/\u223cengelhar\/calibration.html (accessed on 31 December 2012)."},{"key":"ref_2","unstructured":"HDI Advance\u2014Ultimate System for Scanning Complex Objects. Available online: http:\/\/www.3d3solutions.com\/products\/3d-scanner\/hdi-advance\/ (accessed on 31 December 2012)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1016\/j.patcog.2010.03.004","article-title":"A state of the art in structured light patterns for surface profilometry","volume":"43","author":"Salvi","year":"2010","journal-title":"Patt. Recogn"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cui, H., Dai, N., Yuan, T., Cheng, X., and Liao, W. (2008, January 27\u201330). Calibration Algorithm for Structured Light 3D Vision Measuring System. Sanya, China.","DOI":"10.1109\/CISP.2008.720"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, J.C., Dietz, P.H., Maynes-Aminzade, D., and Hudson, S.E. (2004, January 24\u201327). Automatic Projector Calibration with Embedded Light Sensors. Santa Fe, NM, USA.","DOI":"10.1145\/1029632.1029653"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Raskar, R., Vanbaar, J., and Beardsley, P. (2004, January 23\u201328). Display Grid: Ad-hoc Clusters of Autonomous Projectors. San Jose, CA, USA.","DOI":"10.1889\/1.1847737"},{"key":"ref_7","first-page":"310","article-title":"Accurate calibration for a camera-projector measurement system based on structured light projection","volume":"47","author":"Chen","year":"2008","journal-title":"Opt. Laser. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/S1874-1029(08)60060-9","article-title":"Flexible calibration of a portable structured light system through surface plane","volume":"34","author":"Gao","year":"2008","journal-title":"Acta Autom. Sinica"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Audet, S., and Okutomi, M. (2009, January 20\u201325). A User-Friendly Method to Geometrically Calibrate Projector-Camera Systems. Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5204319"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kimura, M., Mochimaru, M., and Kanade, T. (2007, January 17\u201322). Projector Calibration Using Arbitrary Planes and Calibrated Camera. Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383477"},{"key":"ref_11","unstructured":"Plane-Based Calibration of A Projector-Camera System. Available online: https:\/\/procamcalib.googlecode.com\/files\/ProCam_Calib_v2.pdf (access on 31 December 2012)."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TIM.2008.925016","article-title":"Use of LCD panel for calibrating structured-light-based range sensing system","volume":"57","author":"Song","year":"2008","journal-title":"IEEE Trans. Instrum. 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(2004). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/2\/2664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:45:05Z","timestamp":1760219105000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/2\/2664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,2,22]]},"references-count":18,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2013,2]]}},"alternative-id":["s130202664"],"URL":"https:\/\/doi.org\/10.3390\/s130202664","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,2,22]]}}}