{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T22:39:37Z","timestamp":1782945577520,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T00:00:00Z","timestamp":1682121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["1519\/20"],"award-info":[{"award-number":["1519\/20"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aims to achieve accurate three-dimensional (3D) localization of multiple objects in a complicated scene using passive imaging. It is challenging, as it requires accurate localization of the objects in all three dimensions given recorded 2D images. An integral imaging system captures the scene from multiple angles and is able to computationally produce blur-based depth information about the objects in the scene. We propose a method to detect and segment objects in a 3D space using integral-imaging data obtained by a video camera array. Using objects\u2019 two-dimensional regions detected via deep learning, we employ local computational integral imaging in detected objects\u2019 depth tubes to estimate the depth positions of the objects along the viewing axis. This method analyzes object-based blurring characteristics in the 3D environment efficiently. Our camera array produces an array of multiple-view videos of the scene, called elemental videos. Thus, the proposed 3D object detection applied to the video frames allows for 3D tracking of the objects with knowledge of their depth positions along the video. Results show successful 3D object detection with depth localization in a real-life scene based on passive integral imaging. Such outcomes have not been obtained in previous studies using integral imaging; mainly, the proposed method outperforms them in its ability to detect the depth locations of objects that are in close proximity to each other, regardless of the object size. This study may contribute when robust 3D object localization is desired with passive imaging, but it requires a camera or lens array imaging apparatus.<\/jats:p>","DOI":"10.3390\/s23094191","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T03:04:08Z","timestamp":1682305448000},"page":"4191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Video"],"prefix":"10.3390","volume":"23","author":[{"given":"Michael","family":"Kadosh","sequence":"first","affiliation":[{"name":"Department of Electro-Optics and Photonics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4974-9683","authenticated-orcid":false,"given":"Yitzhak","family":"Yitzhaky","sequence":"additional","affiliation":[{"name":"Department of Electro-Optics and Photonics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TAC.2007.902731","article-title":"Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty","volume":"52","author":"Aguiar","year":"2007","journal-title":"IEEE Trans. Automat. Contr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.3390\/ijgi4031301","article-title":"Tracking 3D Moving Objects Based on GPS\/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data","volume":"4","author":"Hosseinyalamdary","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1177\/0278364913507796","article-title":"Appearance Learning for 3D Tracking of Robotic Surgical Tools","volume":"33","author":"Reiter","year":"2014","journal-title":"Int. J. Robot. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sebe, I.O., Hu, J., You, S., and Neumann, U. (2003, January 2\u20138). 3D Video Surveillance with Augmented Virtual Environments. Proceedings of the First ACM SIGMM International Workshop on Video Surveillance\u2014IWVS\u201903, New York, NY, USA.","DOI":"10.1145\/982452.982466"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.visres.2014.10.023","article-title":"Active Confocal Imaging for Visual Prostheses","volume":"111","author":"Jung","year":"2015","journal-title":"Vis. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Avraham, D., and Yitzhaky, Y. (2021). Effects of Depth-Based Object Isolation in Simulated Retinal Prosthetic Vision. Symmetry, 13.","DOI":"10.3390\/sym13101763"},{"key":"ref_7","first-page":"446","article-title":"La Photographie Integrale","volume":"146","author":"Lippmann","year":"1908","journal-title":"Comptes-Rendus"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/JPROC.2006.870696","article-title":"Three-Dimensional Image Sensing, Visualization, and Processing Using Integral Imaging","volume":"94","author":"Stern","year":"2006","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.optlaseng.2014.08.016","article-title":"Modified Computational Integral Imaging-Based Double Image Encryption Using Fractional Fourier Transform","volume":"66","author":"Li","year":"2015","journal-title":"Opt. Lasers Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1364\/AO.52.000546","article-title":"Advances in Three-Dimensional Integral Imaging: Sensing, Display, and Applications","volume":"52","author":"Xiao","year":"2013","journal-title":"Appl. Opt."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/JDT.2014.2370734","article-title":"Towards 3D Television through Fusion of Kinect and Integral-Imaging Concepts","volume":"11","author":"Hong","year":"2014","journal-title":"J. Disp. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/JDT.2013.2291110","article-title":"Depth-of-Field Enhancement in Integral Imaging by Selective Depth-Deconvolution","volume":"10","author":"Navarro","year":"2013","journal-title":"J. Disp. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1364\/OPTICA.1.000332","article-title":"Three-Dimensional Integral Imaging Displays Using a Quick-Response Encoded Elemental Image Array","volume":"1","author":"Markman","year":"2014","journal-title":"Optica"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bae, J., and Yoo, H. (2020). Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure. Sensors, 20.","DOI":"10.3390\/s20174795"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, J., and Cho, M. (2022). Three-Dimensional Integral Imaging with Enhanced Lateral and Longitudinal Resolutions Using Multiple Pickup Positions. Sensors, 22.","DOI":"10.3390\/s22239199"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1109\/JDT.2014.2361147","article-title":"Augmented Reality 3D Displays with Micro Integral Imaging","volume":"11","author":"Wang","year":"2015","journal-title":"J. Disp. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hansard, M., Lee, S., Choi, O., and Horaud, R. (2013). Time-of-Flight Cameras: Principles, Methods and Applications, Springer. SpringerBriefs in Computer Science.","DOI":"10.1007\/978-1-4471-4658-2"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1778765.1778812","article-title":"Nonlinear Disparity Mapping for Stereoscopic 3D","volume":"29","author":"Lang","year":"2010","journal-title":"ACM Trans. Graph."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/JPHOT.2012.2205912","article-title":"Experiments with Three-Dimensional Integral Imaging under Low Light Levels","volume":"4","author":"Stern","year":"2012","journal-title":"IEEE Photonics J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1364\/OE.478125","article-title":"3D Object Detection through Fog and Occlusion: Passive Integral Imaging vs Active (LiDAR) Sensing","volume":"31","author":"Usmani","year":"2023","journal-title":"Opt. Express"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5488","DOI":"10.1364\/AO.41.005488","article-title":"Digital Three-Dimensional Image Correlation by Use of Computer-Reconstructed Integral Imaging","volume":"41","author":"Frauel","year":"2002","journal-title":"Appl. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"09LB05","DOI":"10.1143\/JJAP.48.09LB05","article-title":"Three-Dimensional Object Reconstruction and Recognition Using Computational Integral Imaging and Statistical Pattern Analysis","volume":"48","author":"Yeom","year":"2009","journal-title":"Jpn. J. Appl. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5624","DOI":"10.1364\/AO.50.005624","article-title":"Depth Extraction of Three-Dimensional Objects Using Block Matching for Slice Images in Synthetic Aperture Integral Imaging","volume":"50","author":"Lee","year":"2011","journal-title":"Appl. Opt."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tao, M.W., Hadap, S., Malik, J., and Ramamoorthi, R. (, January December). Depth from Combining Defocus and Correspondence Using Light-Field Cameras. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.89"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1364\/AO.53.002777","article-title":"Simultaneous Reconstruction of Multiple Depth Images without Off-Focus Points in Integral Imaging Using a Graphics Processing Unit","volume":"53","author":"Yi","year":"2014","journal-title":"Appl. Opt."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/LPT.2014.2304883","article-title":"Detection of Object Existence from a Single Reconstructed Plane Obtained by Integral Imaging","volume":"26","author":"Aloni","year":"2014","journal-title":"IEEE Photonics Technol. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6717","DOI":"10.1364\/AO.54.006717","article-title":"Automatic 3D Object Localization and Isolation Using Computational Integral Imaging","volume":"54","author":"Aloni","year":"2015","journal-title":"Appl. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106695","DOI":"10.1016\/j.optlaseng.2021.106695","article-title":"Deep Learning Integral Imaging for Three-Dimensional Visualization, Object Detection, and Segmentation","volume":"146","author":"Yi","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1364\/AO.56.002132","article-title":"Effects of Elemental Images\u2019 Quantity on Three-Dimensional Segmentation Using Computational Integral Imaging","volume":"56","author":"Aloni","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Avraham, D., Samuels, G., Jung, J.-H., Peli, E., and Yitzhaky, Y. (2022). Computational Integral Imaging Based on a Novel Miniature Camera Array, Optica Publishing Group.","DOI":"10.1364\/3D.2022.3Tu5A.2"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3528","DOI":"10.1364\/OE.11.003528","article-title":"Improved Resolution 3D Object Sensing and Recognition Using Time Multiplexed Computational Integral Imaging","volume":"11","author":"Kishk","year":"2003","journal-title":"Opt. Express"},{"key":"ref_33","unstructured":"(2023, February 20). SQ11 mini DV User Guide. Available online: https:\/\/org-info.mobi\/shop\/sq11-wifi-mini-dv.html."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","unstructured":"Heikkila, J., and Silv\u00e9n, O. (1997). A Four-Step Camera Calibration Procedure with Implicit Image Correction, IEEE."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017). Mask R-Cnn, IEEE.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Computer Vision\u2014ECCV 2014, Proceedings of the 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10605-2"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). YOLACT: Real-Time Instance Segmentation. Proceedings of the 2019 IEEE\/CVF In-ternational Conference on Computer Vision (ICCV), Seoul, South Korea.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref_39","first-page":"17721","article-title":"Solov2: Dynamic and Fast Instance Segmentation","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13735-020-00195-x","article-title":"A Survey on Instance Segmentation: State of the Art","volume":"9","author":"Hafiz","year":"2020","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","volume":"22","author":"Feng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. 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