{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T16:29:26Z","timestamp":1706200166271},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"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":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11390-022-2185-7","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T14:09:06Z","timestamp":1655474946000},"page":"615-625","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Local Homography Estimation on User-Specified Textureless Regions"],"prefix":"10.1007","volume":"37","author":[{"given":"Zheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Nan","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song-Hai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"issue":"5","key":"2185_CR1","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/83.668027","volume":"7","author":"\u00c9 M\u00e9min","year":"1998","unstructured":"M\u00e9min \u00c9, P\u00e9rez P. Dense estimation and object-based segmentation of the optical flow with robust techniques. IEEE Trans. Image Process., 1998, 7(5): 703-719. https:\/\/doi.org\/10.1109\/83.668027.","journal-title":"IEEE Trans. Image Process."},{"key":"2185_CR2","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A, Fischer P, Ilg E et al. FlowNet: Learning optical flow with convolutional networks. In Proc. the 2015 IEEE International Conference on Computer Vision, December 2015, pp.2758-2766. https:\/\/doi.org\/10.1109\/ICCV.2015.316.","DOI":"10.1109\/ICCV.2015.316"},{"key":"2185_CR3","doi-asserted-by":"publisher","unstructured":"Ilg E, Mayer N, Saikia T et al. FlowNet 2.0: Evolution of optical flow estimation with deep networks. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.1647-1655. https:\/\/doi.org\/10.1109\/CVPR.2017.179.","DOI":"10.1109\/CVPR.2017.179"},{"key":"2185_CR4","doi-asserted-by":"publisher","unstructured":"Ranjan A, Black M J. Optical flow estimation using a spatial pyramid network. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.2720-2729. https:\/\/doi.org\/10.1109\/CVPR.2017.291.","DOI":"10.1109\/CVPR.2017.291"},{"key":"2185_CR5","doi-asserted-by":"publisher","unstructured":"Sun D Q, Yang X D, Liu M Y, Kautz J. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In Proc. the 2018 IEEE Conference on Computer Vision and Pattern Recognition, June 2018, pp.8934-8943. https:\/\/doi.org\/10.1109\/CVPR.2018.00931.","DOI":"10.1109\/CVPR.2018.00931"},{"key":"2185_CR6","doi-asserted-by":"crossref","unstructured":"Zhao S Y, Sheng Y L, Dong Y et al. MaskFlownet: Asymmetric feature matching with learnable occlusion mask. In Proc. the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2020, pp.6277-6286. 10.1109\/CVPR42600.2020.00631.","DOI":"10.1109\/CVPR42600.2020.00631"},{"key":"2185_CR7","doi-asserted-by":"publisher","unstructured":"Teed Z, Deng J. RAFT: Recurrent all-pairs field transforms for optical flow. In Proc. the 16th European Conference on Computer Vision, August 2020, pp.402-419. https:\/\/doi.org\/10.1007\/978-3-030-58536-5_24.","DOI":"10.1007\/978-3-030-58536-5_24"},{"issue":"2","key":"2185_CR8","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe D G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 2004, 60(2): 91-110. https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94.","journal-title":"Int. J. Comput. Vis."},{"key":"2185_CR9","doi-asserted-by":"publisher","unstructured":"DeTone D, Malisiewicz T, Rabinovich A. Superpoint: Self-supervised interest point detection and description. In Proc. the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 2018, pp.224-236. https:\/\/doi.org\/10.1109\/CVPRW.2018.00060.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"2185_CR10","doi-asserted-by":"publisher","unstructured":"Luo Z X, Zhou L, Bai X Y et al. ASLFeat: Learning local features of accurate shape and localization. In Proc. the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2020, pp.6588-6597. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00662.","DOI":"10.1109\/CVPR42600.2020.00662"},{"key":"2185_CR11","doi-asserted-by":"publisher","unstructured":"Sarlin P E, DeTone D, Malisiewicz T, Rabinovich A. SuperGlue: Learning feature matching with graph neural networks. In Proc. the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2020, pp.4937-4946. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00499.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"2185_CR12","doi-asserted-by":"publisher","unstructured":"Jiang W, Trulls E, Hosang J et al. COTR: Correspondence transformer for matching across images. In Proc. the 2021 IEEE\/CVF International Conference on Computer Vision, October 2021, pp.6187-6197. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00615.","DOI":"10.1109\/ICCV48922.2021.00615"},{"key":"2185_CR13","doi-asserted-by":"publisher","unstructured":"Efe U, Ince K G, Alatan A A. DFM: A performance baseline for deep feature matching. In Proc. the 2021 IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 2021, pp.4284-4293. https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00484.","DOI":"10.1109\/CVPRW53098.2021.00484"},{"issue":"10","key":"2185_CR14","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1109\/TPAMI.2008.113","volume":"30","author":"GD Evangelidis","year":"2008","unstructured":"Evangelidis G D, Psarakis E Z. Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30(10): 1858-1865. https:\/\/doi.org\/10.1109\/TPAMI.2008.113.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2185_CR15","doi-asserted-by":"publisher","unstructured":"Benhimane S, Malis E. Real-time image-based tracking of planes using efficient second-order minimization. In Proc. the 2004 IEEE\/RSJ International Conference on Intelligent Robots and Systems, September 28-October 2, 2004, pp.943-948. https:\/\/doi.org\/10.1109\/IROS.2004.1389474.","DOI":"10.1109\/IROS.2004.1389474"},{"key":"2185_CR16","doi-asserted-by":"publisher","unstructured":"Chen L, Zhou F, Shen Y et al. Illumination insensitive efficient second-order minimization for planar object tracking. In Proc. the 2017 IEEE International Conference on Robotics and Automation, May 29-June 3, 2017, pp.4429-4436. https:\/\/doi.org\/10.1109\/ICRA.2017.7989512.","DOI":"10.1109\/ICRA.2017.7989512"},{"key":"2185_CR17","unstructured":"DeTone D, Malisiewicz T, Rabinovich A. Deep image homography estimation. arXiv:1606.03798, 2016. https:\/\/arxiv.org\/pdf\/1606.03798.pdf, Jan. 2022."},{"key":"2185_CR18","doi-asserted-by":"publisher","unstructured":"Dai A, Chang A X, Savva M et al. ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.2432-2443. https:\/\/doi.org\/10.1109\/CVPR.2017.261.","DOI":"10.1109\/CVPR.2017.261"},{"key":"2185_CR19","doi-asserted-by":"crossref","unstructured":"Dai A, Niesner M, Zollh\u00f6fer M et al. BundleFusion: Real-time globally consistent 3D reconstruction using on-the-fly surface re-integration. arXiv:1604.01093, 2016. https:\/\/arxiv.org\/pdf\/1604.01093.pdf, Jan. 2022.","DOI":"10.1145\/3054739"},{"issue":"3","key":"2185_CR20","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s41095-021-0250-8","volume":"8","author":"JW Li","year":"2022","unstructured":"Li J W, Gao W, Wu Y H et al. High-quality indoor scene 3D reconstruction with RGB-D cameras: A brief review. Computational Visual Media, 2022, 8(3): 369-393. https:\/\/doi.org\/10.1007\/s41095-021-0250-8.","journal-title":"Computational Visual Media"},{"key":"2185_CR21","doi-asserted-by":"publisher","unstructured":"Muratov O, Slynko Y, Chernov V et al. 3DCapture: 3D reconstruction for a smartphone. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 26-July 1, 2016, pp.893-900. https:\/\/doi.org\/10.1109\/CVPRW.2016.116.","DOI":"10.1109\/CVPRW.2016.116"},{"issue":"12","key":"2185_CR22","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1109\/TVCG.2020.3023634","volume":"26","author":"XB Yang","year":"2020","unstructured":"Yang X B, Zhou L Y, Jiang H Q et al. Mobile3DRecon: Real-time monocular 3D reconstruction on a mobile phone. IEEE Trans. Vis. Comput. Graph., 2020, 26(12): 3446-3456. https:\/\/doi.org\/10.1109\/TVCG.2020.3023634.","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"2185_CR23","doi-asserted-by":"publisher","unstructured":"Zhang S H, Li X L, Liu Y T. Scale-aware insertion of virtual objects in monocular videos. In Proc. the 2020 IEEE International Symposium on Mixed and Augmented Reality, November 2020, pp.36-44. https:\/\/doi.org\/10.1109\/ISMAR50242.2020.00022.","DOI":"10.1109\/ISMAR50242.2020.00022"},{"issue":"2","key":"2185_CR24","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s41095-021-0212-1","volume":"7","author":"D Chen","year":"2021","unstructured":"Chen D, Tang F, Dong W M et al. SiamCPN: Visual tracking with the Siamese center-prediction network. Comput. Vis. Media, 2021, 7(2): 253-265. https:\/\/doi.org\/10.1007\/s41095-021-0212-1.","journal-title":"Comput. Vis. Media"},{"key":"2185_CR25","doi-asserted-by":"publisher","unstructured":"Xue Z X, Wu W. Anomaly detection by exploiting the tracking trajectory in surveillance videos. Sci. China: Inf. Sci., 2020, 63(5): Article No. 154101. https:\/\/doi.org\/10.1007\/s11432-018-9792-8.","DOI":"10.1007\/s11432-018-9792-8"},{"key":"2185_CR26","doi-asserted-by":"publisher","unstructured":"Zhang D, Li T S, Chen C L. Target tracking algorithm based on a broad learning system. Science China: Information Sciences, 2022, 65(5): Article No. 154201. https:\/\/doi.org\/10.1007\/s11432-020-3272-y.","DOI":"10.1007\/s11432-020-3272-y"},{"issue":"1","key":"2185_CR27","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s11390-017-1764-5","volume":"33","author":"K Li","year":"2018","unstructured":"Li K, He F, Yu H. Robust visual tracking based on convolutional features with illumination and occlusion handing. J. Comput. Sci. Technol., 2018, 33(1): 223-236. https:\/\/doi.org\/10.1007\/s11390-017-1764-5.","journal-title":"J. Comput. Sci. Technol."},{"issue":"3","key":"2185_CR28","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s11390-021-1272-5","volume":"36","author":"JC Li","year":"2021","unstructured":"Li J C, Zhong F, Xu S H, Qin X Y. 3D object tracking with adaptively weighted local bundles. J. Comput. Sci. Technol., 2021, 36(3): 555-571. https:\/\/doi.org\/10.1007\/s11390-021-1272-5.","journal-title":"J. Comput. Sci. Technol."},{"issue":"8","key":"2185_CR29","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TPAMI.2004.53","volume":"26","author":"S Avidan","year":"2004","unstructured":"Avidan S. Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26(8): 1064-1072. https:\/\/doi.org\/10.1109\/TPAMI.2004.53.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1\/2\/3","key":"2185_CR30","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","volume":"77","author":"DA Ross","year":"2008","unstructured":"Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking. Int. J. Comput. Vis., 2008, 77(1\/2\/3): 125-141. https:\/\/doi.org\/10.1007\/s11263-007-0075-7.","journal-title":"Int. J. Comput. Vis."},{"key":"2185_CR31","unstructured":"Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. In Proc. the 7th International Joint Conference on Artificial Intelligence, August 1981, pp.674-679."},{"issue":"3","key":"2185_CR32","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","unstructured":"Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(3): 583-596. https:\/\/doi.org\/10.1109\/TPAMI.2014.2345390.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"2185_CR33","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/78.978374","volume":"50","author":"MS Arulampalam","year":"2002","unstructured":"Arulampalam M S, Maskell S, Gordon N J, Clapp T. A tutorial on particle filters for online nonlinear\/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 2002, 50(2): 174-188. https:\/\/doi.org\/10.1109\/78.978374.","journal-title":"IEEE Trans. Signal Process."},{"key":"2185_CR34","doi-asserted-by":"publisher","unstructured":"Li B, Wu W, Wang Q et al. SiamRPN++: Evolution of Siamese visual tracking with very deep networks. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, June 2019, pp.4282-4291. https:\/\/doi.org\/10.1109\/CVPR.2019.00441.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"2185_CR35","doi-asserted-by":"publisher","unstructured":"Guo D Y, Wang J, Cui Y et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In Proc. the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2020, pp.6268-6276. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00630.","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"2185_CR36","doi-asserted-by":"publisher","unstructured":"Guo D Y, Shao Y Y, Cui Y et al. Graph attention tracking. In Proc. the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2021, pp.9543-9552. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00942.","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"2185_CR37","doi-asserted-by":"publisher","unstructured":"Chen X, Yan B, Zhu J W et al. Transformer tracking. In Proc. the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2021, pp.8126-8135. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00803.","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"2185_CR38","doi-asserted-by":"publisher","unstructured":"Wang N, Zhou W G, Wang J et al. Transformer meets tracker: Exploiting temporal context for robust visual tracking. In Proc. the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2021, pp.1571-1580. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00162.","DOI":"10.1109\/CVPR46437.2021.00162"},{"issue":"1\/2\/3","key":"2185_CR39","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BKP Horn","year":"1981","unstructured":"Horn B K P, Schunck B G. Determining optical flow. Artif. Intell., 1981, 17(1\/2\/3): 185-203. https:\/\/doi.org\/10.1016\/0004-3702(81)90024-2.","journal-title":"Artif. Intell."},{"issue":"2","key":"2185_CR40","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1017\/S0263574700223217","volume":"19","author":"R Hartley","year":"2001","unstructured":"Hartley R, Zisserman A. Multiple view geometry in computer vision. Robotica, 2001, 19(2): 233-236. https:\/\/doi.org\/10.1017\/S0263574700223217.","journal-title":"Robotica"},{"issue":"11","key":"2185_CR41","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","volume":"36","author":"M Muja","year":"2014","unstructured":"Muja M, Lowe D G. Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36(11): 2227-2240. https:\/\/doi.org\/10.1109\/TPAMI.2014.2321376.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"2185_CR42","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981, 24(6): 381-395. https:\/\/doi.org\/10.1145\/358669.358692.","journal-title":"Commun. ACM"},{"key":"2185_CR43","doi-asserted-by":"publisher","unstructured":"Barath D, Matas J, Noskova J. MAGSAC: Marginalizing sample consensus. In Proc. the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, June 2019, pp.10197-10205. https:\/\/doi.org\/10.1109\/CVPR.2019.01044.","DOI":"10.1109\/CVPR.2019.01044"},{"issue":"3","key":"2185_CR44","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/LRA.2018.2809549","volume":"3","author":"T Nguyen","year":"2018","unstructured":"Nguyen T, Chen S W, Shivakumar S S et al. Unsupervised deep homography: A fast and robust homography estimation model. IEEE Robotics Autom. Lett., 2018, 3(3): 2346-2353. https:\/\/doi.org\/10.1109\/LRA.2018.2809549.","journal-title":"IEEE Robotics Autom. Lett."},{"key":"2185_CR45","doi-asserted-by":"publisher","unstructured":"Zhang J R, Wang C, Liu S C et al. Content-aware unsupervised deep homography estimation. In Proc. the 16th European Conference on Computer Vision, August 2020, pp.653-669. https:\/\/doi.org\/10.1007\/978-3-030-58452-8_38.","DOI":"10.1007\/978-3-030-58452-8_38"},{"key":"2185_CR46","doi-asserted-by":"publisher","unstructured":"He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.770-778. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-022-2185-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11390-022-2185-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-022-2185-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T14:26:32Z","timestamp":1655475992000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11390-022-2185-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["2185"],"URL":"https:\/\/doi.org\/10.1007\/s11390-022-2185-7","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"value":"1000-9000","type":"print"},{"value":"1860-4749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]},"assertion":[{"value":"25 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}