{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:47:14Z","timestamp":1742960834494,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031530357"},{"type":"electronic","value":"9783031530364"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-53036-4_7","type":"book-chapter","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T07:02:28Z","timestamp":1706857348000},"page":"93-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the\u00a03D Position of\u00a0a\u00a0Car with\u00a0a\u00a0Single 2D Camera Using Siamese Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4880-6218","authenticated-orcid":false,"given":"Youssef Bel Haj","family":"Yahia","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5842-4602","authenticated-orcid":false,"given":"J\u00falio Castro","family":"Lopes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9177-5503","authenticated-orcid":false,"given":"Eduardo","family":"Bezerra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0555-2029","authenticated-orcid":false,"given":"Pedro Jo\u00e3o","family":"Rodrigues","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-5078","authenticated-orcid":false,"given":"Rui Pedro","family":"Lopes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,3]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Castro Lopes, J., Vieira, J., Van-Deste, I., Lopes, R.P.: An architecture for capturing and synchronizing heart rate and body motion for stress inference. In: Accepted for publication: 11th International Conference on Serious Games and Applications for Health. Athens, Greece (2023)","DOI":"10.1109\/SeGAH57547.2023.10253815"},{"key":"7_CR2","doi-asserted-by":"publisher","unstructured":"Chatzichristofis, S.A.: Recent advances in educational robotics. Electronics 12(4), 925 (2023). https:\/\/doi.org\/10.3390\/electronics12040925, https:\/\/www.mdpi.com\/2079-9292\/12\/4\/925","DOI":"10.3390\/electronics12040925"},{"key":"7_CR3","series-title":"Methods in Molecular Biology","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-1-0716-0826-5_3","volume-title":"Artificial Neural Networks","author":"D Chicco","year":"2021","unstructured":"Chicco, D.: Siamese neural networks: an overview. In: Cartwright, H. (ed.) Artificial Neural Networks. MMB, vol. 2190, pp. 73\u201394. Springer, New York (2021). https:\/\/doi.org\/10.1007\/978-1-0716-0826-5_3"},{"key":"7_CR4","unstructured":"Dahlberg, T., Str\u00f6mberg, V.: Automatic LiDAR-camera calibration: extrinsic calibration for a LiDAR-camera pair using structure from motion and stochastic optimization (2022). https:\/\/hdl.handle.net\/20.500.12380\/304955"},{"key":"7_CR5","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-72632-4_1","volume-title":"Smart Technologies for Precision Assembly","author":"M Dalle Mura","year":"2021","unstructured":"Dalle Mura, M., Dini, G.: Augmented reality in assembly systems: state of the art and future perspectives. In: Ratchev, S. (ed.) IPAS 2020. IAICT, vol. 620, pp. 3\u201322. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72632-4_1"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Durchon, H., Preda, M., Zaharia, T., Grall, Y.: Challenges in applying deep learning to augmented reality for manufacturing. In: Proceedings of the 27th International Conference on 3D Web Technology. pp. 1\u20134. Web3D \u201922, Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3564533.3564572. https:\/\/dl.acm.org\/doi\/10.1145\/3564533.3564572","DOI":"10.1145\/3564533.3564572"},{"key":"7_CR7","doi-asserted-by":"publisher","unstructured":"Ellinger, A., Woerner, C., Scherer, R.: Automatic segmentation of bulk material heaps using color, texture, and topography from aerial data and deep learning-based computer vision. Remote Sens. 15(1), 211 (2023). https:\/\/doi.org\/10.3390\/rs15010211, https:\/\/www.mdpi.com\/2072-4292\/15\/1\/211","DOI":"10.3390\/rs15010211"},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Hasan, Z., Mohammad, H.R., Jishkariani, M.: Machine learning and data mining methods for cyber security: a survey. Mesopotamian J. CyberSecur. 2022(47\u201356) (2022). https:\/\/doi.org\/10.58496\/MJCS\/2022\/006, https:\/\/mesopotamian.press\/journals\/index.php\/CyberSecurity\/article\/view\/30","DOI":"10.58496\/MJCS\/2022\/006"},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). https:\/\/doi.org\/10.48550\/arXiv.1512.03385, http:\/\/arxiv.org\/abs\/1512.03385,arXiv:1512.03385 [cs]","DOI":"10.48550\/arXiv.1512.03385"},{"key":"7_CR10","doi-asserted-by":"publisher","unstructured":"Hoque, S., Xu, S., Maiti, A., Wei, Y., Arafat, M.Y.: Deep learning for 6D pose estimation of objects - A case study for autonomous driving. Exp. Syst. Appl. 223, 119838 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119838, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417423003391","DOI":"10.1016\/j.eswa.2023.119838"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Hung Nguyen, N.D., Nguyen Nguyen, L.H., Pham, P.T., Nguyen, Q.C., Ly, P.T.: Bin-picking solution for industrial robots integrating a 2D vision system. In: 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS), pp. 266\u2013270 (2022). https:\/\/doi.org\/10.1109\/HDIS56859.2022.9991341","DOI":"10.1109\/HDIS56859.2022.9991341"},{"key":"7_CR12","unstructured":"Ingberg, B.: Registration of 2D objects in 3D data (2015). https:\/\/urn.kb.se\/resolve?urn=urn:nbn:se:liu:diva-119338"},{"key":"7_CR13","doi-asserted-by":"publisher","unstructured":"Kim, Y., Kum, D.: Deep learning based vehicle position and orientation estimation via inverse perspective mapping image. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 317\u2013323 (2019). https:\/\/doi.org\/10.1109\/IVS.2019.8814050, iSSN: 2642-7214","DOI":"10.1109\/IVS.2019.8814050"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Kite, D.H., Magee, M.: Determining the 3D position and orientation of a robot camera using 2D monocular vision. Pattern Recogn. 23(8), 819\u2013831 (1990). https:\/\/doi.org\/10.1016\/0031-3203(90)90129-9, https:\/\/www.sciencedirect.com\/science\/article\/pii\/0031320390901299","DOI":"10.1016\/0031-3203(90)90129-9"},{"key":"7_CR15","doi-asserted-by":"publisher","unstructured":"Lim, S., Jung, J., Lee, B.H., Choi, J., Kim, S.C.: Radar sensor-based estimation of vehicle orientation for autonomous driving. IEEE Sens. J. 22(22), 21924\u201321932 (2022). https:\/\/doi.org\/10.1109\/JSEN.2022.3210579","DOI":"10.1109\/JSEN.2022.3210579"},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Liu, X., et al.: A pose estimation approach based on keypoints detection for robotic bin-picking application. In: 2021 China Automation Congress (CAC), pp. 3672\u20133677 (2021). https:\/\/doi.org\/10.1109\/CAC53003.2021.9727987, iSSN: 2688-0938","DOI":"10.1109\/CAC53003.2021.9727987"},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Lopes, R.P., et al.: Digital technologies for innovative mental health rehabilitation. Electronics 10(18), 2260 (2021). https:\/\/doi.org\/10.3390\/electronics10182260, https:\/\/www.mdpi.com\/2079-9292\/10\/18\/2260, iF (2021): 2.69 - Q2","DOI":"10.3390\/electronics10182260"},{"key":"7_CR18","doi-asserted-by":"publisher","unstructured":"Mendes-Neves, T., Meireles, L., Mendes-Moreira, J.: A survey of advanced computer vision techniques for sports (2023). https:\/\/doi.org\/10.48550\/arXiv.2301.07583, http:\/\/arxiv.org\/abs\/2301.07583, arXiv:2301.07583 [cs]","DOI":"10.48550\/arXiv.2301.07583"},{"key":"7_CR19","doi-asserted-by":"publisher","unstructured":"Miseikis, J., Brijacak, I., Yahyanejad, S., Glette, K., Elle, O.J., Torresen, J.: Multi-objective convolutional neural networks for robot localisation and 3D position estimation in 2D camera images. In: 2018 15th International Conference on Ubiquitous Robots (UR), pp. 597\u2013603 (2018). https:\/\/doi.org\/10.1109\/URAI.2018.8441813","DOI":"10.1109\/URAI.2018.8441813"},{"issue":"2","key":"7_CR20","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1007\/s11042-021-11358-1","volume":"81","author":"N Mohan","year":"2022","unstructured":"Mohan, N., Kumar, M.: Room layout estimation in indoor environment: a review. Multimedia Tools Appl. 81(2), 1921\u20131951 (2022). https:\/\/doi.org\/10.1007\/s11042-021-11358-1","journal-title":"Multimedia Tools Appl."},{"key":"7_CR21","doi-asserted-by":"publisher","unstructured":"Mourtzis, D., Angelopoulos, J., Panopoulos, N.: Challenges and opportunities for integrating augmented reality and computational fluid dynamics modeling under the framework of industry 4.0. Procedia CIRP 106, 215\u2013220 (2022). https:\/\/doi.org\/10.1016\/j.procir.2022.02.181, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2212827122001822","DOI":"10.1016\/j.procir.2022.02.181"},{"key":"7_CR22","doi-asserted-by":"publisher","unstructured":"Ozuysal, M., Lepetit, V., Fua, P.: Pose estimation for category specific multiview object localization. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 778\u2013785. IEEE, Miami, FL (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206633, https:\/\/ieeexplore.ieee.org\/document\/5206633\/","DOI":"10.1109\/CVPR.2009.5206633"},{"key":"7_CR23","doi-asserted-by":"publisher","unstructured":"Qi, Q., Zhao, S., Shen, J., Lam, K.M.: Multi-scale capsule attention-based salient object detection with multi-crossed layer connections. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1762\u20131767 (2019). https:\/\/doi.org\/10.1109\/ICME.2019.00303, iSSN: 1945-788X","DOI":"10.1109\/ICME.2019.00303"},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3278933","volume":"61","author":"C Rao","year":"2023","unstructured":"Rao, C., Wang, J., Cheng, G., Xie, X., Han, J.: Learning orientation-aware distances for oriented object detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201311 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3278933","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"7_CR25","doi-asserted-by":"publisher","unstructured":"Ren, J., Orwell, J., Jones, G., Xu, M.: A general framework for 3D soccer ball estimation and tracking. In: 2004 International Conference on Image Processing, 2004. ICIP 2004, vol. 3, pp. 1935\u20131938 (2004). https:\/\/doi.org\/10.1109\/ICIP.2004.1421458, iSSN: 1522-4880","DOI":"10.1109\/ICIP.2004.1421458"},{"key":"7_CR26","doi-asserted-by":"publisher","unstructured":"Rodrigues, A.S.F., Lopes, J.C., Lopes, R.P., Teixeira, L.F.: Classification of facial expressions under partial occlusion for vr games. In: Pereira, A.I., Ko\u0161ir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds.) Optimization, Learning Algorithms and Applications, vol. 1754, pp. 804\u2013819. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23236-7_55, https:\/\/link.springer.com\/10.1007\/978-3-031-23236-7_55","DOI":"10.1007\/978-3-031-23236-7_55"},{"key":"7_CR27","doi-asserted-by":"publisher","unstructured":"Sagodi, A., Schniertshauer, J., van Giffen, B.: Engineering AI-enabled computer vision systems: lessons from manufacturing. IEEE Softw. 39(6), 51\u201357 (2022). https:\/\/doi.org\/10.1109\/MS.2022.3189904","DOI":"10.1109\/MS.2022.3189904"},{"key":"7_CR28","doi-asserted-by":"publisher","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815\u2013823. IEEE, Boston, MA, USA (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298682, http:\/\/ieeexplore.ieee.org\/document\/7298682\/","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"7_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015), http:\/\/arxiv.org\/abs\/1409.1556,arXiv:1409.1556 [cs]"},{"key":"7_CR30","doi-asserted-by":"publisher","unstructured":"Takahashi, M., Ikeya, K., Kano, M., Ookubo, H., Mishina, T.: Robust volleyball tracking system using multi-view cameras. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2740\u20132745 (2016). https:\/\/doi.org\/10.1109\/ICPR.2016.7900050","DOI":"10.1109\/ICPR.2016.7900050"},{"key":"7_CR31","doi-asserted-by":"publisher","unstructured":"Wang, J., Choi, W., Diaz, J., Trott, C.: The 3D position estimation and tracking of a surface vehicle using a mono-camera and machine learning. Electronics 11(14), 2141 (Jan 2022). https:\/\/doi.org\/10.3390\/electronics11142141, https:\/\/www.mdpi.com\/2079-9292\/11\/14\/2141","DOI":"10.3390\/electronics11142141"},{"key":"7_CR32","doi-asserted-by":"publisher","unstructured":"Wang, J., Gao, P., Zhang, J., Lu, C., Shen, B.: Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing. Robot. Comput. Integr. Manuf. 81, 102513 (2023). https:\/\/doi.org\/10.1016\/j.rcim.2022.102513, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0736584522001958","DOI":"10.1016\/j.rcim.2022.102513"},{"key":"7_CR33","unstructured":"Wei, H., et al.: Physical adversarial attack meets computer vision: a decade survey (2022). http:\/\/arxiv-export3.library.cornell.edu\/abs\/2209.15179v1"},{"key":"7_CR34","doi-asserted-by":"publisher","unstructured":"Yan, X., Zhang, H., Li, H.: Computer vision-based recognition of 3D relationship between construction entities for monitoring struck-by accidents. Comput. Aided Civil Infrastruct. Eng. 35(9), 1023\u20131038 (2020). https:\/\/doi.org\/10.1111\/mice.12536, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/mice.12536, https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/mice.12536","DOI":"10.1111\/mice.12536"}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53036-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T07:04:55Z","timestamp":1706857495000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53036-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530357","9783031530364"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53036-4_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ponta Delgada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ol2a.ipb.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"162","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}