{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:45:08Z","timestamp":1760132708680,"version":"3.40.4"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The National Science Foundation of China","award":["61972120"],"award-info":[{"award-number":["61972120"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00521-024-10661-w","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T10:05:06Z","timestamp":1739786706000},"page":"8245-8260","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sufficient learning: mining denser high-quality pixel-level labels for edge detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Xiaodiao","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7041-1302","authenticated-orcid":false,"given":"Wenya","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiuting","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Xiaoyang","family":"Mao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"issue":"2","key":"10661_CR1","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1109\/TCSVT.2020.2980853","volume":"31","author":"Z Tu","year":"2020","unstructured":"Tu Z, Ma Y, Li C, Tang J, Luo B (2020) Edge-guided non-local fully convolutional network for salient object detection. IEEE Trans Circuits Syst Video Technol 31(2):582\u2013593","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10661_CR2","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1109\/TIP.2020.3037536","volume":"30","author":"Z Chen","year":"2020","unstructured":"Chen Z, Zhou H, Lai J, Yang L, Xie X (2020) Contour-aware loss: boundary-aware learning for salient object segmentation. IEEE Trans Image Process 30:431\u2013443","journal-title":"IEEE Trans Image Process"},{"key":"10661_CR3","doi-asserted-by":"publisher","first-page":"108231","DOI":"10.1016\/j.patcog.2021.108231","volume":"121","author":"Z Yang","year":"2022","unstructured":"Yang Z, Soltanian-Zadeh S, Farsiu S (2022) Biconnet: an edge-preserved connectivity-based approach for salient object detection. Pattern Recognit 121:108231\u2013108241","journal-title":"Pattern Recognit"},{"key":"10661_CR4","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11263-021-01533-0","volume":"130","author":"L Yu","year":"2022","unstructured":"Yu L, Li Z, Xu M, Gao Y, Luo J, Zhang J (2022) Distribution-aware margin calibration for semantic segmentation in images. Int J Comput Vis 130:95\u2013110","journal-title":"Int J Comput Vis"},{"issue":"7","key":"10661_CR5","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1109\/TNNLS.2020.3009373","volume":"32","author":"C Li","year":"2020","unstructured":"Li C, Xia W, Yan Y, Luo B, Tang J (2020) Segmenting objects in day and night: Edge-conditioned cnn for thermal image semantic segmentation. IEEE Trans Neural Netw Learn Syst 32(7):3069\u20133082","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10661_CR6","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Chai Y, Schroff F, Adam H, Leibe B, Chen L-C (2019) Feelvos: Fast end-to-end embedding learning for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9481\u20139490","DOI":"10.1109\/CVPR.2019.00971"},{"key":"10661_CR7","doi-asserted-by":"crossref","unstructured":"Caelles S, Maninis K-K, Pont-Tuset J, Leal-Taix\u00e9 L, Cremers D, Van\u00a0Gool L (2017) One-shot video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 221\u2013230","DOI":"10.1109\/CVPR.2017.565"},{"key":"10661_CR8","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang L, Bertinetto L, Hu W, Torr PH (2019) Fast online object tracking and segmentation: A unifying approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1328\u20131338","DOI":"10.1109\/CVPR.2019.00142"},{"key":"10661_CR9","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"6","author":"J Canny","year":"1986","unstructured":"Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679\u2013698","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"10661_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0262-8856(83)90006-9","volume":"1","author":"J Kittler","year":"1983","unstructured":"Kittler J (1983) On the accuracy of the sobel edge detector. Image Vis Comput 1(1):37\u201342","journal-title":"Image Vis Comput"},{"issue":"8","key":"10661_CR11","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1109\/TPAMI.2018.2878849","volume":"41","author":"Y Liu","year":"2019","unstructured":"Liu Y, Cheng M-M, Hu X, Bian J-W, Zhang L, Bai X, Tang J (2019) Richer convolutional features for edge detection. IEEE Trans Pattern Anal Mach Intell 41(8):1939\u20131946","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10661_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/neco_a_01627","volume":"36","author":"L Jianfeng","year":"2023","unstructured":"Jianfeng L, Zhongliang Y, Yifan L, Guanghui S (2023) Gtan: graph-based tracklet association network for multi-object tracking. Neural Comput Appl 36:1\u201314","journal-title":"Neural Comput Appl"},{"key":"10661_CR13","first-page":"1","volume":"61","author":"X-D Chen","year":"2023","unstructured":"Chen X-D, Wu W, Yang W, Qin H, Wu X, Mao X (2023) Make segment anything model perfect on shadow detection. IEEE Trans Geosci Remote Sens 61:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10661_CR14","doi-asserted-by":"publisher","first-page":"20499","DOI":"10.1007\/s10489-023-04587-4","volume":"53","author":"W Yang","year":"2023","unstructured":"Yang W, Wu W, Chen X-D, Tao X, Mao X (2023) How to use extra training data for better edge detection? Appl Intell 53:20499\u201320513","journal-title":"Appl Intell"},{"key":"10661_CR15","doi-asserted-by":"publisher","first-page":"6213","DOI":"10.1109\/TCSVT.2023.3263903","volume":"33","author":"W Wu","year":"2023","unstructured":"Wu W, Yang W, Ma W, Chen X-D (2023) How many annotations do we need for generalizing new-coming shadow images? IEEE Trans Circuits Syst Video Technol 33:6213\u20136224","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10661_CR16","doi-asserted-by":"crossref","unstructured":"Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395\u20131403","DOI":"10.1109\/ICCV.2015.164"},{"key":"10661_CR17","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations, pp. 1\u201314"},{"issue":"27","key":"10661_CR18","doi-asserted-by":"publisher","first-page":"19979","DOI":"10.1007\/s00521-023-08814-4","volume":"35","author":"Y Qi","year":"2023","unstructured":"Qi Y, Hu J, Han X, Hu L, Zhao Z (2023) Mfgcn: an efficient graph convolutional network based on multi-order feature information for human skeleton action recognition. Neural Comput Appl 35(27):19979\u201319995","journal-title":"Neural Comput Appl"},{"key":"10661_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103341","volume":"216","author":"W Wu","year":"2022","unstructured":"Wu W, Zhou K, Chen X-D, Yong J-H (2022) Light-weight shadow detection via gcn-based annotation strategy and knowledge distillation. Comput Vis Image Underst 216:103341","journal-title":"Comput Vis Image Underst"},{"issue":"32","key":"10661_CR20","doi-asserted-by":"publisher","first-page":"23521","DOI":"10.1007\/s00521-023-08964-5","volume":"35","author":"M Mesgaran","year":"2023","unstructured":"Mesgaran M, Hamza AB (2023) A graph encoder-decoder network for unsupervised anomaly detection. Neural Comput Appl 35(32):23521\u201323535","journal-title":"Neural Comput Appl"},{"issue":"32","key":"10661_CR21","doi-asserted-by":"publisher","first-page":"23631","DOI":"10.1007\/s00521-023-08979-y","volume":"35","author":"L Wang","year":"2023","unstructured":"Wang L, Yan Z, Cao W, Ji J (2023) Diegraph: dual-branch information exchange graph convolutional network for deformable medical image registration. Neural Comput Appl 35(32):23631\u201323647","journal-title":"Neural Comput Appl"},{"key":"10661_CR22","unstructured":"Soberanis-Mukul RD, Navab N, Albarqouni S (2020) Uncertainty-based graph convolutional networks for organ segmentation refinement. In: Medical Imaging with Deep Learning, pp. 755\u2013769"},{"key":"10661_CR23","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r P, Zitnick CL (2013) Structured forests for fast edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1841\u20131848","DOI":"10.1109\/ICCV.2013.231"},{"key":"10661_CR24","doi-asserted-by":"crossref","unstructured":"Bertasius G, Shi J, Torresani L (2015) Deepedge: A multi-scale bifurcated deep network for top-down contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4380\u20134389","DOI":"10.1109\/CVPR.2015.7299067"},{"key":"10661_CR25","doi-asserted-by":"crossref","unstructured":"Ganin Y, Lempitsky V (2014) $$\\text{N}\\hat{4}$$-fields: Neural network nearest neighbor fields for image transforms. In: Proceedings of the Asian Conference on Computer Vision, pp. 536\u2013551","DOI":"10.1007\/978-3-319-16808-1_36"},{"key":"10661_CR26","doi-asserted-by":"crossref","unstructured":"Shen W, Wang X, Wang Y, Bai X, Zhang Z (2015) Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3982\u20133991","DOI":"10.1109\/CVPR.2015.7299024"},{"issue":"8","key":"10661_CR27","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TPAMI.2014.2377715","volume":"37","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r P, Zitnick CL (2014) Fast edge detection using structured forests. IEEE Trans Pattern Anal Mach Intell 37(8):1558\u20131570","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10661_CR28","doi-asserted-by":"crossref","unstructured":"Hallman S, Fowlkes CC (2015) Oriented edge forests for boundary detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1732\u20131740","DOI":"10.1109\/CVPR.2015.7298782"},{"key":"10661_CR29","doi-asserted-by":"crossref","unstructured":"He J, Zhang S, Yang M, Shan Y, Huang T (2019) Bi-directional cascade network for perceptual edge detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3828\u20133837","DOI":"10.1109\/CVPR.2019.00395"},{"key":"10661_CR30","doi-asserted-by":"crossref","unstructured":"Su Z, Liu W, Yu Z, Hu D, Liu L (2021) Pixel difference networks for efficient edge detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5117\u20135127","DOI":"10.1109\/ICCV48922.2021.00507"},{"key":"10661_CR31","doi-asserted-by":"crossref","unstructured":"Pu M, Huang Y, Liu Y, Guan Q, Ling H (2022) Edter: Edge detection with transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1402\u20131412","DOI":"10.1109\/CVPR52688.2022.00146"},{"issue":"10","key":"10661_CR32","doi-asserted-by":"publisher","first-page":"6778","DOI":"10.1109\/TCSVT.2022.3170048","volume":"32","author":"Q Bo","year":"2022","unstructured":"Bo Q, Ma W, Lai Y-K, Zha H (2022) All-higher-stages-in adaptive context aggregation for semantic edge detection. IEEE Trans Circuits Syst Video Technol 32(10):6778\u20136791","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10661_CR33","doi-asserted-by":"crossref","unstructured":"Fu Y, Guo X (2023) Practical edge detection via robust collaborative learning. In: Proceedings of the IEEE\/CVF ACM International Conference on Multimedia, pp. 1\u201310","DOI":"10.1145\/3581783.3612099"},{"key":"10661_CR34","doi-asserted-by":"crossref","unstructured":"Soria X, Li Y, Rouhani M, Sappa AD (2023) Tiny and efficient model for the edge detection generalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1\u201310","DOI":"10.1109\/ICCVW60793.2023.00147"},{"key":"10661_CR35","unstructured":"Kendall A, Gal Y (2017) What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, vol. 30"},{"key":"10661_CR36","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Proceedings of the International Conference on Machine Learning, pp. 1050\u20131059"},{"key":"10661_CR37","doi-asserted-by":"publisher","first-page":"108777","DOI":"10.1016\/j.patcog.2022.108777","volume":"130","author":"Z Feng","year":"2022","unstructured":"Feng Z, Zhou Q, Gu Q, Tan X, Cheng G, Lu X, Shi J, Ma L (2022) Dmt: dynamic mutual training for semi-supervised learning. Pattern Recognit 130:108777\u2013108789","journal-title":"Pattern Recognit"},{"key":"10661_CR38","doi-asserted-by":"crossref","unstructured":"Zhou C, Huang Y, Pu M, Guan Q, Huang L, Ling H (2023) The treasure beneath multiple annotations: An uncertainty-aware edge detector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15507\u201315517","DOI":"10.1109\/CVPR52729.2023.01488"},{"key":"10661_CR39","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1109\/TCE.2023.3300534","volume":"70","author":"G Yi","year":"2024","unstructured":"Yi G, Wu S (2024) Graphical visual analysis of consumer electronics public comment information mining under knowledge graph. IEEE Trans Consum Electron 70:2917\u20132924","journal-title":"IEEE Trans Consum Electron"},{"key":"10661_CR40","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations"},{"key":"10661_CR41","unstructured":"Soberanis-Mukul RD, Navab N, Albarqouni S (2020) Uncertainty-based graph convolutional networks for organ segmentation refinement. In: Proceedings of the Medical Imaging with Deep Learning, pp. 755\u2013769"},{"key":"10661_CR42","doi-asserted-by":"crossref","unstructured":"Pan S-Y, Lu C-Y, Lee S-P, Peng W-H (2021) Weakly-supervised image semantic segmentation using graph convolutional networks. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1\u20136","DOI":"10.1109\/ICME51207.2021.9428116"},{"key":"10661_CR43","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TAI.2023.3329799","volume":"5","author":"T Zhuang","year":"2024","unstructured":"Zhuang T, Qin Z, Ding Y, Deng F, Chen L, Qin Z, Choo K-KR (2024) Temporal refinement graph convolutional network for skeleton-based action recognition. IEEE Trans Artif Intell 5:1586\u20131598","journal-title":"IEEE Trans Artif Intell"},{"key":"10661_CR44","doi-asserted-by":"publisher","first-page":"2830","DOI":"10.1109\/JBHI.2024.3367756","volume":"28","author":"Q Jiang","year":"2024","unstructured":"Jiang Q, Ye H, Yang B, Cao F (2024) Label-decoupled medical image segmentation with spatial-channel graph convolution and dual attention enhancement. IEEE J Biomed Health Inf 28:2830\u20132841","journal-title":"IEEE J Biomed Health Inf"},{"key":"10661_CR45","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: Simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"10661_CR46","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations, pp. 1\u201322"},{"key":"10661_CR47","first-page":"1","volume":"60","author":"C Zhang","year":"2022","unstructured":"Zhang C, Jiang W, Zhang Y, Wang W, Zhao Q, Wang C (2022) Transformer and cnn hybrid deep neural network for semantic segmentation of very-high-resolution remote sensing imagery. IEEE Trans Geosci Sens 60:1\u201320","journal-title":"IEEE Trans Geosci Sens"},{"key":"10661_CR48","unstructured":"Kr\u00e4henb\u00fchl P, Koltun V (2011) Efficient inference in fully connected crfs with gaussian rdge potentials. In: Advances in Neural Information Processing Systems, vol. 24"},{"issue":"5","key":"10661_CR49","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez P, Maire M, Fowlkes C, Malik J (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898\u2013916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10661_CR50","doi-asserted-by":"crossref","unstructured":"Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: Proceedings of the European Conference on Computer Vision, pp. 746\u2013760","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"10661_CR51","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.visres.2015.11.007","volume":"120","author":"DA M\u00e9ly","year":"2016","unstructured":"M\u00e9ly DA, Kim J, McGill M, Guo Y, Serre T (2016) A systematic comparison between visual cues for boundary detection. Vis Res 120:93\u2013107","journal-title":"Vis Res"},{"key":"10661_CR52","doi-asserted-by":"publisher","first-page":"6602","DOI":"10.1109\/TPAMI.2021.3084197","volume":"44","author":"L Huan","year":"2022","unstructured":"Huan L, Xue N, Zheng X, He W, Gong J, Xia G-S (2022) Unmixing convolutional features for crisp edge detection. IEEE Trans Pattern Anal Mach Intell 44:6602\u20136609","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10661_CR53","unstructured":"Poma XS, Riba E, Sappa A (2020) Dense extreme inception network: Towards a robust CNN model for edge detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1923\u20131932"},{"issue":"5","key":"10661_CR54","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","volume":"26","author":"DR Martin","year":"2004","unstructured":"Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530\u2013549","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10661_CR55","unstructured":"Xiaofeng R, Bo L (2012) Discriminatively trained sparse code gradients for contour detection. In: Advances in Neural Information Processing Systems, vol. 25"},{"key":"10661_CR56","doi-asserted-by":"crossref","unstructured":"Maninis K-K, Pont-Tuset J, Arbel\u00e1ez P, Gool LV (2016) Convolutional oriented boundaries. In: Proceedings of the European Conference on Computer Vision, pp. 580\u2013596","DOI":"10.1007\/978-3-319-46448-0_35"},{"key":"10661_CR57","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhao X, Huang K (2017) Deep crisp boundaries. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3892\u20133900","DOI":"10.1109\/CVPR.2017.187"},{"key":"10661_CR58","doi-asserted-by":"crossref","unstructured":"Deng R, Shen C, Liu S, Wang H, Liu X (2018) Learning to predict crisp boundaries. In: Proceedings of the European Conference on Computer Vision, pp. 562\u2013578","DOI":"10.1007\/978-3-030-01231-1_35"},{"key":"10661_CR59","doi-asserted-by":"crossref","unstructured":"Gupta S, Girshick R, Arbel\u00e1ez P, Malik J (2014) Learning rich features from rgb-d images for object detection and segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 345\u2013360","DOI":"10.1007\/978-3-319-10584-0_23"},{"key":"10661_CR60","unstructured":"Xu D, Ouyang W, Alameda-Pineda X, Ricci E, Wang X, Sebe N (2017) Learning deep structured multi-scale features using attention-gated crfs for contour prediction. In: Advances in Neural Information Processing Systems, pp. 3961\u20133970"},{"key":"10661_CR61","doi-asserted-by":"crossref","unstructured":"Gupta S, Arbel\u00e1ez, P, Malik J (2013) Perceptual organization and recognition of indoor dcenes from rgb-d images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 564\u2013571","DOI":"10.1109\/CVPR.2013.79"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10661-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10661-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10661-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T08:13:33Z","timestamp":1746260013000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10661-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":61,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10661"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10661-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,2,17]]},"assertion":[{"value":"27 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The work follows appropriate ethical standards in conducting research and writing the manuscript. This work presents computational models trained with publicly available data, for which no ethical approval was required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}