{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:56:39Z","timestamp":1775066199068,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515110733"],"award-info":[{"award-number":["2022A1515110733"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515110733"],"award-info":[{"award-number":["2022A1515110733"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515110733"],"award-info":[{"award-number":["2022A1515110733"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00138-024-01512-8","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T09:02:22Z","timestamp":1708506142000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A gradient fusion-based image data augmentation method for reflective workpieces detection under small size datasets"],"prefix":"10.1007","volume":"35","author":[{"given":"Baori","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingxiang","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"1512_CR1","doi-asserted-by":"publisher","unstructured":"QibtiAE, R.: Convolutional neural network model in machine learning methods and computer vision for image recognition: a review. In: ICEBS 2018 (2018) https:\/\/doi.org\/10.22587\/jasr.2018.14.6.5","DOI":"10.22587\/jasr.2018.14.6.5"},{"key":"1512_CR2","unstructured":"Hartwig, S., Ropinski, T.: Training object detectors on synthetic images containing reflecting materials (2019) arXiv:1904.00824"},{"key":"1512_CR3","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.rcim.2016.09.001","volume":"44","author":"S Astanin","year":"2017","unstructured":"Astanin, S., Antonelli, D., Chiabert, P., et al.: Reflective workpiece detection and localization for flexible robotic cells. Robot. Comput. Integr. Manuf. 44, 190\u2013198 (2017)","journal-title":"Robot. Comput. Integr. Manuf."},{"issue":"3\u20134","key":"1512_CR4","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.optlaseng.2008.03.010","volume":"47","author":"G Rosati","year":"2009","unstructured":"Rosati, G., Boschetti, G., Biondi, A., et al.: Real-time defect detection on highly reflective curved surfaces. Opt. Lasers Eng. 47(3\u20134), 379\u2013384 (2009). https:\/\/doi.org\/10.1016\/j.optlaseng.2008.03.010","journal-title":"Opt. Lasers Eng."},{"key":"1512_CR5","doi-asserted-by":"crossref","unstructured":"Yang, J., Gao, Y., Li, D., et al.: ROBI: A multi-view dataset for reflective objects in robotic bin-picking. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic. pp 9788\u20139795 (2021) arXiv:2105.04112","DOI":"10.1109\/IROS51168.2021.9635871"},{"key":"1512_CR6","doi-asserted-by":"publisher","unstructured":"Lu, Q., Laligant, O., Fauvet, E., et al.: Entire reflective object surface structure understanding. In: Proceedings of the British Machine Vision Conference (BMVC), Swansea, United Kingdom (2015) https:\/\/doi.org\/10.1016\/j.patrec.2015.09.006","DOI":"10.1016\/j.patrec.2015.09.006"},{"key":"1512_CR7","doi-asserted-by":"publisher","unstructured":"Yang, D., Jayawardena, S., Gould, S., et al.: Reflective features detection and hierarchical reflections separation in image sequences. In: 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20137 (2014) https:\/\/doi.org\/10.1109\/DICTA.2014.7008127","DOI":"10.1109\/DICTA.2014.7008127"},{"issue":"2","key":"1512_CR8","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1109\/LRA.2020.3047796","volume":"6","author":"D Park","year":"2021","unstructured":"Park, D., Park, Y.H.: Identifying reflected images from object detector in indoor environment utilizing depth information. IEEE Robot. Autom. Lett. 6(2), 635\u2013642 (2021). https:\/\/doi.org\/10.1109\/LRA.2020.3047796","journal-title":"IEEE Robot. Autom. Lett."},{"key":"1512_CR9","unstructured":"Hestness, J., Narang, S., Ardalani, N., et al.: Deep Learning Scaling is Predictable, Empirically. arXiv e-prints (2017) arXiv.1712.00409"},{"key":"1512_CR10","first-page":"52","volume":"14","author":"LE Aik","year":"2019","unstructured":"Aik, L.E., Hong, T.W., Junoh, A.K.: A new formula to determine the optimal dataset size for training neural networks. ARPN J. Eng. Appl. Sci. 14, 52\u201361 (2019)","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"1512_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, C., Cheng, J.: Image scoring: Patch based CNN model for small or medium dataset. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC). pp. 2055\u20132059 (2017) https:\/\/doi.org\/10.1109\/CompComm.2017.8322898","DOI":"10.1109\/CompComm.2017.8322898"},{"key":"1512_CR12","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.eswa.2017.06.025","volume":"87","author":"A Jalali","year":"2017","unstructured":"Jalali, A., Mallipeddi, R., Lee, M.: Sensitive deep convolutional neural network for face recognition at large standoffs with small dataset. Expert Syst. Appl. 87, 304\u2013315 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2017.06.025","journal-title":"Expert Syst. Appl."},{"key":"1512_CR13","doi-asserted-by":"crossref","unstructured":"Tan, W., Guo, H.: Data augmentation and CNN classification for automatic COVID-19 diagnosis from CT-scan images on small dataset. arXiv e-prints (2021) arXiv:2108.07148","DOI":"10.1109\/ICMLA52953.2021.00234"},{"key":"1512_CR14","doi-asserted-by":"crossref","unstructured":"AEmed, T., RAEman, C. R., Abid, M.: Rice grain disease identification using dual phase convolutional neural network-based system aimed at small dataset. arXiv e-prints (2020) arXiv:2004.09870","DOI":"10.31220\/agriRxiv.2021.00062"},{"key":"1512_CR15","doi-asserted-by":"publisher","unstructured":"Zhao., W.: Research on the deep learning of the small sample data based on transfer learning. In: American Institute of Physics Conference Series American Institute of Physics Conference Series, 020018 (2017) https:\/\/doi.org\/10.1063\/1.4992835","DOI":"10.1063\/1.4992835"},{"key":"1512_CR16","unstructured":"Tripuraneni, N., Jordan, M. I., Jin, C.: On the theory of transfer learning: the importance of task diversity (2020) arXiv:2006.11650"},{"key":"1512_CR17","doi-asserted-by":"publisher","unstructured":"Liang, H., Fu, W., & Yi, F.: A survey of recent advances in transfer learning. In: 2019 IEEE 19th International Conference on Communication Technology (ICCT) (2019) https:\/\/doi.org\/10.1109\/icct46805.2019.8947072","DOI":"10.1109\/icct46805.2019.8947072"},{"key":"1512_CR18","doi-asserted-by":"publisher","unstructured":"Gozes, O., Greenspan, H.: Deep feature learning from a hospital-scale chest X-ray dataset with application to TB detection on a small-scale dataset. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4076\u20134079 (2019) https:\/\/doi.org\/10.1109\/EMBC.2019.8856729","DOI":"10.1109\/EMBC.2019.8856729"},{"key":"1512_CR19","doi-asserted-by":"publisher","unstructured":"Girshick, R., DonAEue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014) https:\/\/doi.org\/10.1109\/CVPR.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"issue":"4","key":"1512_CR20","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1512_CR21","doi-asserted-by":"publisher","unstructured":"Gao, X., Guanghui, L.I., Tan, R., et al.: Using deep neural networks to predict the tensile property of ceramic matrix composites based on incomplete small dataset. In: 4th International Conference on Advanced Materials Research and Manufacturing Technology (2019) https:\/\/doi.org\/10.1088\/1757-899X\/647\/1\/012004","DOI":"10.1088\/1757-899X\/647\/1\/012004"},{"issue":"4","key":"1512_CR22","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s11554-014-0444-8","volume":"12","author":"V Popovic","year":"2016","unstructured":"Popovic, V., Seyid, K., Pignat, E., et al.: Multi-camera platform for panoramic real-time HDR video construction and rendering. J. Real Time Image Process. 12(4), 697\u2013708 (2016). https:\/\/doi.org\/10.1007\/s11554-014-0444-8","journal-title":"J. Real Time Image Process."},{"issue":"4","key":"1512_CR23","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1109\/TIM.2010.2087835","volume":"60","author":"C Kao","year":"2011","unstructured":"Kao, C., Cheng, L.W., Chien, C.-Y., et al.: Robust brightness measurement and exposure control in real-time video recording. IEEE Trans. Instrum. Meas. 60(4), 1206\u20131216 (2011). https:\/\/doi.org\/10.1109\/TIM.2010.2087835","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1512_CR24","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1007\/s00170-021-07812-x","volume":"117","author":"B Zhang","year":"2021","unstructured":"Zhang, B., Shi, Y., Cui, Y., et al.: A high-dynamic-range visual sensing method for feature extraction of welding pool based on adaptive image fusion. Int. J. Adv. Manuf. Technol. 117, 1675\u20131687 (2021). https:\/\/doi.org\/10.1007\/s00170-021-07812-x","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"1512_CR25","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.jmapro.2020.03.053","volume":"63","author":"B Zhang","year":"2021","unstructured":"Zhang, B., Shi, Y., Cui, Y., et al.: Prediction of keyhole TIG weld penetration based on high-dynamic range imaging. J. Manuf. Process. 63, 179\u2013190 (2021). https:\/\/doi.org\/10.1016\/j.jmapro.2020.03.053","journal-title":"J. Manuf. Process."},{"issue":"3","key":"1512_CR26","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s11045-013-0262-3","volume":"26","author":"IS Sevcenco","year":"2013","unstructured":"Sevcenco, I.S., Hampton, P.J., Agathoklis, P.: A wavelet based method for image reconstruction from gradient data with applications. Multidimens. Syst. Signal Process. 26(3), 717\u2013737 (2013). https:\/\/doi.org\/10.1007\/s11045-013-0262-3","journal-title":"Multidimens. Syst. Signal Process."},{"issue":"10","key":"1512_CR27","doi-asserted-by":"publisher","first-page":"1650123","DOI":"10.1142\/s0218126616501231","volume":"25","author":"S Paul","year":"2016","unstructured":"Paul, S., Sevcenco, I.S., Agathoklis, P.: Multi-exposure and multi-focus image fusion in gradient domain. J. Circuits. Syst. Comput. 25(10), 1650123 (2016). https:\/\/doi.org\/10.1142\/s0218126616501231","journal-title":"J. Circuits. Syst. Comput."},{"key":"1512_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ye, P., Xiao, G.: VIFB: a visible and infrared benchmark. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020) arXiv:2002.03322","DOI":"10.1109\/CVPRW50498.2020.00060"},{"issue":"1","key":"1512_CR29","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/JSEN.2015.2478655","volume":"16","author":"DP Bavirisetti","year":"2016","unstructured":"Bavirisetti, D.P., Dhuli, R.: Fusion of infrared and visible sensor images based on anisotropic diffusion and karhunenloeve transform. IEEE Sens. J. 16(1), 203\u2013209 (2016)","journal-title":"IEEE Sens. J."},{"issue":"23","key":"1512_CR30","doi-asserted-by":"publisher","first-page":"6480","DOI":"10.1364\/AO.55.006480","volume":"55","author":"Z Zhou","year":"2016","unstructured":"Zhou, Z., Dong, M., Xie, X., et al.: Fusion of infrared and visible images for night-vision context enhancement. Appl. Opt. 55(23), 6480\u20136490 (2016). https:\/\/doi.org\/10.1364\/AO.55.006480","journal-title":"Appl. Opt."},{"key":"1512_CR31","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","volume":"31","author":"J Ma","year":"2016","unstructured":"Ma, J., Chen, C., Li, C., et al.: Infrared and visible image fusion via gradient transfer and total variation minimization. Inf. Fusion 31, 100\u2013109 (2016). https:\/\/doi.org\/10.1016\/j.inffus.2016.02.001","journal-title":"Inf. Fusion"},{"issue":"12","key":"1512_CR32","doi-asserted-by":"publisher","first-page":"5576","DOI":"10.1007\/s00034-019-01131-z","volume":"38","author":"DP Bavirisetti","year":"2019","unstructured":"Bavirisetti, D.P., Xiao, G., Zhao, J., et al.: Multi-scale guided image and video fusion: a fast and efficient approach. Circuits Syst. Signal Process. 38(12), 5576\u20135605 (2019). https:\/\/doi.org\/10.1007\/s00034-019-01131-z","journal-title":"Circuits Syst. Signal Process."},{"issue":"5","key":"1512_CR33","doi-asserted-by":"publisher","first-page":"479","DOI":"10.14429\/dsj.61.705","volume":"61","author":"V Naidu","year":"2011","unstructured":"Naidu, V.: Image fusion technique using multi-resolution singular value decomposition. Defence Sci. J. 61(5), 479\u2013484 (2011). https:\/\/doi.org\/10.14429\/dsj.61.705","journal-title":"Defence Sci. J."},{"key":"1512_CR34","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., et al.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) https:\/\/doi.org\/10.1109\/cvpr.2016.319","DOI":"10.1109\/cvpr.2016.319"},{"issue":"2","key":"1512_CR35","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336\u2013359 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int. J. Comput. Vis."},{"key":"1512_CR36","doi-asserted-by":"publisher","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the International Conference on Machine Learning. pp. 233\u2013240 (2006) https:\/\/doi.org\/10.1145\/1143844.1143874","DOI":"10.1145\/1143844.1143874"},{"key":"1512_CR37","doi-asserted-by":"publisher","unstructured":"Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2020) https:\/\/doi.org\/10.48550\/arXiv.2010.16061","DOI":"10.48550\/arXiv.2010.16061"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01512-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01512-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01512-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T06:38:39Z","timestamp":1711175919000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-024-01512-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,21]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1512"],"URL":"https:\/\/doi.org\/10.1007\/s00138-024-01512-8","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,21]]},"assertion":[{"value":"11 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"29"}}