{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T20:08:11Z","timestamp":1771358891995,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031210617","type":"print"},{"value":"9783031210624","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-21062-4_5","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T15:04:09Z","timestamp":1668783849000},"page":"53-66","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Object Segmentation for\u00a0Bin Picking Using Deep Learning"],"prefix":"10.1007","author":[{"given":"Artur","family":"Cordeiro","sequence":"first","affiliation":[]},{"given":"Lu\u00eds F.","family":"Rocha","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Costa","sequence":"additional","affiliation":[]},{"given":"Manuel F.","family":"Silva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Doumanoglou, A., Kouskouridas, R., Malassiotis, S., Kim, T.-K.: Recovering 6d object pose and predicting next-best-view in the crowd. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Jun 2016. https:\/\/doi.org\/10.1109\/cvpr.2016.390","DOI":"10.1109\/cvpr.2016.390"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Pochyly, A., Kubela, T., Singule, V., Cihak, P.: Robotic bin-picking system based on a revolving vision system. In: 2017 19th International Conference on Electrical Drives and Power Electronics (EDPE). IEEE, Oct 2017. https:\/\/doi.org\/10.1109\/edpe.2017.8123228","DOI":"10.1109\/edpe.2017.8123228"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Choi, C., Taguchi, Y., Tuzel, O., Liu, M.-Y., Ramalingam, S.: Voting-based pose estimation for robotic assembly using a 3d sensor. In: 2012 IEEE International Conference on Robotics and Automation. IEEE, May 2012. https:\/\/doi.org\/10.1109\/icra.2012.6225371","DOI":"10.1109\/icra.2012.6225371"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Yan, W., Xu, Z., Zhou, X., Su, Q., Li, S., Wu, H.: Fast object pose estimation using adaptive threshold for bin-picking. IEEE Access 8, 63 055\u201363 064 (2020). https:\/\/doi.org\/10.1109\/access.2020.2983173","DOI":"10.1109\/access.2020.2983173"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Le\u00e3o, G., Costa, C.M., Sousa, A., Veiga, G.: Detecting and solving tube entanglement in bin picking operations. Appli. Sci. 10(7), 2264 (2020). https:\/\/doi.org\/10.3390\/app10072264","DOI":"10.3390\/app10072264"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps (2013). https:\/\/arxiv.org\/abs\/1301.3592","DOI":"10.15607\/RSS.2013.IX.012"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Mahler, J., et al.: Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2016. https:\/\/doi.org\/10.1109\/icra.2016.7487342","DOI":"10.1109\/icra.2016.7487342"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Mahler, J., et al.: Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics (2017). https:\/\/arxiv.org\/abs\/1703.09312","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, A., et al.: Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge (2016). https:\/\/arxiv.org\/abs\/1609.09475","DOI":"10.1109\/ICRA.2017.7989165"},{"issue":"16","key":"5_CR10","doi-asserted-by":"publisher","first-page":"3602","DOI":"10.3390\/s19163602","volume":"19","author":"T-T Le","year":"2019","unstructured":"Le, T.-T., Lin, C.-Y.: Bin-picking for planar objects based on a deep learning network: A case study of USB packs. Sensors 19(16), 3602 (2019). https:\/\/doi.org\/10.3390\/s19163602","journal-title":"Sensors"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"H\u00f6fer, T., Shamsafar, F., Benbarka, N., Zell, A.: Object detection and autoencoder-based 6d pose estimation for highly cluttered bin picking (2021). https:\/\/arxiv.org\/abs\/2106.08045","DOI":"10.1109\/ICIP42928.2021.9506304"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Danielczuk, M., et al.: Segmenting unknown 3d objects from real depth images using mask r-cnn trained on synthetic data (2018). https:\/\/arxiv.org\/abs\/1809.05825","DOI":"10.1109\/ICRA.2019.8793744"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn (2015). https:\/\/arxiv.org\/abs\/1504.08083","DOI":"10.1109\/ICCV.2015.169"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn (2017). https:\/\/arxiv.org\/abs\/1703.06870","DOI":"10.1109\/ICCV.2017.322"},{"key":"5_CR15","unstructured":"Abdulla, W.: Mask r-cnn for object detection and instance segmentation on keras and tensorflow (2017). https:\/\/github.com\/matterport\/Mask_RCNN"},{"key":"5_CR16","unstructured":"Tested build configurations. https:\/\/www.tensorflow.org\/install\/source#gpu. (Accessed: 03 Mar 2022)"},{"key":"5_CR17","unstructured":"Mask r-cnn for object detection and segmentation using tensorflow 2.0. https:\/\/github.com\/ahmedfgad\/Mask-RCNN-TF2. (Accessed 13 Mar 2022)"},{"key":"5_CR18","unstructured":"Industrial 3d scanner: Phoxi\u00ae. https:\/\/www.photoneo.com\/phoxi-3d-scanner\/?gclid=EAIaIQobChMI_4y38LLp-AIVCcPVCh2E0A8eEAAYASAAEgJMwfD_BwE"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.robot.2015.09.030","volume":"76","author":"C Costa","year":"2016","unstructured":"Costa, C., Sobreira, H., Sousa, A., Veiga, G.: \"Robust 3\/6 dof self-localization system with selective map update for mobile robot platforms. Robot. Autonom. Syst. 76, 113\u2013140 (2016)","journal-title":"Robot. Autonom. Syst."},{"key":"5_CR20","first-page":"07","volume":"67","author":"J de Souza Carvalho","year":"2020","unstructured":"de Souza Carvalho, J., et al.: Reconfigurable grasp planning pipeline with grasp synthesis and selection applied to picking operations in aerospace factories. Robot. Comput.-Integ. Manuf. 67, 07 (2020)","journal-title":"Robot. Comput.-Integ. Manuf."}],"container-title":["Lecture Notes in Networks and Systems","ROBOT2022: Fifth Iberian Robotics Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21062-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T15:39:05Z","timestamp":1668785945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21062-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,19]]},"ISBN":["9783031210617","9783031210624"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21062-4_5","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,19]]},"assertion":[{"value":"19 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ROBOT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Robotics conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zaragoza","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"robot2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iberianroboticsconf.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}