{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T04:24:14Z","timestamp":1770956654097,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030777715","type":"print"},{"value":"9783030777722","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-77772-2_21","type":"book-chapter","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T23:04:56Z","timestamp":1625267096000},"page":"310-325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automatic Generation of Machine Learning Synthetic Data Using ROS"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8254-5722","authenticated-orcid":false,"given":"Kyle M.","family":"Hart","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ari B.","family":"Goodman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan P.","family":"O\u2019Shea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"key":"21_CR1","unstructured":"Quigley, M., et al.: ROS: an open-source robot operating system. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) Workshop on Open Source Robotics, Kobe, Japan (2009)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: 2004 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2149\u20132154 (IEEE Cat. No.04CH37566) (2004)","DOI":"10.1109\/IROS.2004.1389727"},{"key":"21_CR3","unstructured":"Roh, Y., Heo, G., Whang, S.E.: A survey on data collection for machine learning: a big data -- AI integration perspective. ArXiv181103402 Cs stat (2019)"},{"key":"21_CR4","unstructured":"Alexey: Yolo_mark"},{"key":"21_CR5","unstructured":"Tzutalin: LabelImg (2015)"},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3389\/frobt.2018.00066","volume":"5","author":"L Sixt","year":"2018","unstructured":"Sixt, L., Wild, B., Landgraf, T.: RenderGAN: generating realistic labeled data. Front. Robot. AI. 5, 66 (2018). https:\/\/doi.org\/10.3389\/frobt.2018.00066","journal-title":"Front. Robot. AI."},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Pfeiffer, M., et al.: Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. ArXiv190702882 Cs stat (2019)","DOI":"10.1007\/978-3-030-32254-0_14"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Lee, Y.-H., Chuang, C.-C., Lai, S.-H., Jhang, Z.-J.: automatic generation of photorealistic training data for detection of industrial components. In: 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, pp. 2751\u20132755. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803339"},{"key":"21_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-030-50344-4_14","volume-title":"Distributed, Ambient and Pervasive Interactions","author":"A Besginow","year":"2020","unstructured":"Besginow, A., B\u00fcttner, S., R\u00f6cker, C.: Making object detection available to everyone\u2014a hardware prototype for semi-automatic synthetic data generation. In: Streitz, N., Konomi, S. (eds.) Distributed, Ambient and Pervasive Interactions. LNCS, vol. 12203, pp. 178\u2013192. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50344-4_14"},{"issue":"24","key":"21_CR10","doi-asserted-by":"publisher","first-page":"31991","DOI":"10.1007\/s11042-018-6247-3","volume":"77","author":"A Dutta","year":"2018","unstructured":"Dutta, A., Verma, Y., Jawahar, C.V.: Automatic image annotation: the quirks and what works. Multimedia Tools Appl. 77(24), 31991\u201332011 (2018). https:\/\/doi.org\/10.1007\/s11042-018-6247-3","journal-title":"Multimedia Tools Appl."},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Kuhner, T., Wirges, S., Lauer, M.: Automatic generation of training data for image classification of road scenes. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, pp. 1097\u20131103. IEEE (2019)","DOI":"10.1109\/ITSC.2019.8917089"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Rong, G., et al.: LGSVL simulator: a high fidelity simulator for autonomous driving. ArXiv200503778. Cs Eess (2020)","DOI":"10.1109\/ITSC45102.2020.9294422"},{"key":"21_CR13","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. ArXiv180402767 Cs (2018)"},{"key":"21_CR14","unstructured":"Redmon, J.: Darknet: open source neural networks in C (2013)"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1147\/sj.41.0025","volume":"4","author":"JE Bresenham","year":"1965","unstructured":"Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4, 25\u201330 (1965)","journal-title":"IBM Syst. J."},{"key":"21_CR16","unstructured":"Bresenham, J.: A linear, incremental algorithm for digitally plotting circles. Tech Rep (1964)"},{"key":"21_CR17","first-page":"120","volume":"25","author":"G Bradski","year":"2000","unstructured":"Bradski, G.: The openCV library. Dr Dobbs J. Softw. Tools. 25, 120\u2013125 (2000)","journal-title":"Dr Dobbs J. Softw. Tools."},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Foote, T.: tf: The transform library. In: IEEE International Conference on Technologies for Practical Robot Applications (TePRA), 2013, pp. 1\u20136 (2013)","DOI":"10.1109\/TePRA.2013.6556373"},{"key":"21_CR19","unstructured":"Rossum, G. van, Talin.: Introducing Abstract Base Classes (2007)"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Peng, S., Liu, Y., Huang, Q., Bao, H., Zhou, X.: PVNet: pixel-wise voting network for 6DoF pose estimation. ArXiv181211788 Cs (2018)","DOI":"10.1109\/CVPR.2019.00469"},{"key":"21_CR21","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris, C.R., et al.: Array programming with NumPy. Nature 585, 357\u2013362 (2020). https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. ArXiv170306870 Cs (2018)","DOI":"10.1109\/ICCV.2017.322"},{"key":"21_CR23","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. ArXiv14050312 Cs (2015)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77772-2_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T22:20:47Z","timestamp":1751494847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77772-2_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030777715","9783030777722"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77772-2_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}