{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:50Z","timestamp":1760145950749,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO\u2019s detection performance.<\/jats:p>","DOI":"10.3390\/s24186050","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T04:59:54Z","timestamp":1726721994000},"page":"6050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9382-0945","authenticated-orcid":false,"given":"Songtao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2806-014X","authenticated-orcid":false,"given":"Yaonan","family":"Zhu","sequence":"additional","affiliation":[{"name":"The School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7860-0725","authenticated-orcid":false,"given":"Tadayoshi","family":"Aoyama","sequence":"additional","affiliation":[{"name":"Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masayuki","family":"Nakaya","sequence":"additional","affiliation":[{"name":"Robot Division, System Department, NACHI-FUJIKOSHI CORP., Toyama 930-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9917-098X","authenticated-orcid":false,"given":"Yasuhisa","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Le, T.T., and Lin, C.Y. (2019). Bin-picking for planar objects based on a deep learning network: A case study of USB packs. Sensors, 19.","DOI":"10.3390\/s19163602"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1007\/s00170-021-07649-4","article-title":"Machine vision-based intelligent manufacturing using a novel dual-template matching: A case study for lithium battery positioning","volume":"116","author":"Guo","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111087","DOI":"10.1016\/j.measurement.2022.111087","article-title":"Online detection of external thread surface defects based on an improved template matching algorithm","volume":"195","author":"Kong","year":"2022","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14320","DOI":"10.1109\/JSEN.2022.3180385","article-title":"Color template matching based on fuzzy density clustering for vision sensor based shoe detection in human-robot coexisting environment","volume":"22","author":"Brahma","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69204","DOI":"10.1109\/ACCESS.2023.3292410","article-title":"A shared control framework for enhanced grasping performance in teleoperation","volume":"11","author":"Zhu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Korman, S., Reichman, D., Tsur, G., and Avidan, S. (2013, January 23\u201328). Fast-match: Fast affine template matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.302"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dekel, T., Oron, S., Rubinstein, M., Avidan, S., and Freeman, W.T. (2015, January 7\u201312). Best-buddies similarity for robust template matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298813"},{"key":"ref_8","first-page":"317","article-title":"Matching by tone mapping: Photometric invariant template matching","volume":"36","author":"David","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.compag.2016.08.001","article-title":"Multi-template matching algorithm for cucumber recognition in natural environment","volume":"127","author":"Bao","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-3363-7","article-title":"Multi-template matching: A versatile tool for object-localization in microscopy images","volume":"21","author":"Thomas","year":"2020","journal-title":"BMC Bioinform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e68946","DOI":"10.7554\/eLife.68946","article-title":"Locating macromolecular assemblies in cells by 2D template matching with cisTEM","volume":"10","author":"Lucas","year":"2021","journal-title":"eLife"},{"key":"ref_12","unstructured":"Ye, C., Li, K., Jia, L., Zhuang, C., and Xiong, Z. (2016, January 22\u201324). Fast hierarchical template matching strategy for real-time pose estimation of texture-less objects. Proceedings of the Intelligent Robotics and Applications: 9th International Conference, ICIRA 2016, Tokyo, Japan. Proceedings, Part I 9."},{"key":"ref_13","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_14","unstructured":"Doersch, C. (2016). Tutorial on variational autoencoders. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0377-2217(96)00385-2","article-title":"Optimization of computer simulation models with rare events","volume":"99","author":"Rubinstein","year":"1997","journal-title":"Eur. J. Oper. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_17","unstructured":"Pinneri, C., Sawant, S., Blaes, S., Achterhold, J., Stueckler, J., Rolinek, M., and Martius, G. (2021, January 8\u201311). Sample-efficient cross-entropy method for real-time planning. Proceedings of the Conference on Robot Learning PMLR, London, UK."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/01691864.2024.2324303","article-title":"Latent regression based model predictive control for tissue triangulation","volume":"38","author":"Liu","year":"2024","journal-title":"Adv. Robot."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/TPAMI.2011.106","article-title":"Performance evaluation of full search equivalent pattern matching algorithms","volume":"34","author":"Ouyang","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1093\/nar\/gkg509","article-title":"SIFT: Predicting amino acid changes that affect protein function","volume":"31","author":"Ng","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, P., Hue, C., Vermaak, J., and Gangnet, M. (2002, January 28\u201331). Color-based probabilistic tracking. Proceedings of the Computer Vision\u2014ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark. Proceedings, Part I 7.","DOI":"10.1007\/3-540-47969-4_44"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). Surf: Speeded up robust features. Proceedings of the Computer Vision\u2014ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria. Proceedings, Part I 9.","DOI":"10.1007\/11744023_32"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 2\u20136). Segment anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_24","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_25","unstructured":"Higgins, I., Matthey, L., Pal, A., Burgess, C.P., Glorot, X., Botvinick, M.M., Mohamed, S., and Lerchner, A. (2017, January 24\u201326). beta-vae: Learning basic visual concepts with a constrained variational framework. Proceedings of the 5th International Conference on Learning Representations, Toulon, France."},{"key":"ref_26","unstructured":"Kim, H., and Mnih, A. (2018, January 10\u201315). Disentangling by factorising. Proceedings of the International Conference on Machine Learning PMLR, Stockholm, Sweden."},{"key":"ref_27","unstructured":"Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J. (2019, January 9\u201315). Learning latent dynamics for planning from pixels. Proceedings of the International Conference on Machine Learning PMLR, Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: Challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","unstructured":"Miwa, D., Shiraishi, T., Duy, V.N.L., Katsuoka, T., and Takeuchi, I. (2024). Statistical Test for Anomaly Detections by Variational Auto-Encoders. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6050\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:59:13Z","timestamp":1760111953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":31,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186050"],"URL":"https:\/\/doi.org\/10.3390\/s24186050","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,9,19]]}}}