Design and Implementation of Multimodel Estimation Algorithm for Nonrigid Matching Images

Ruoyan Wei; Siyuan Huo; Xiaoqing Zhu

doi:10.3788/LOP202259.1215018

[1] Shu A, Pei H D, Duan H X. Trinocular stereo visual measurement method for spatial non-cooperative target[J]. Acta Optica Sinica, 41, 0615001(2021).

[2] Ma J Y, Zhao J, Tian J W et al. Robust point matching via vector field consensus[J]. IEEE Transactions on Image Processing, 23, 1706-1721(2014).

[3] Fischler M A. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 24, 381-395(1981).

[4] Myatt D R, Torr P H S, Nasuto S J et al. NAPSAC: high noise, high dimensional robust estimation-it’s in the bag[C], 458-467(2002).

[5] Chum O, Matas J. Matching with PROSAC-progressive sample consensus[C], 220-226(2005).

[6] Sattler T, Leibe B, Kobbelt L. SCRAMSAC: improving RANSAC’s efficiency with a spatial consistency filter[C], 2090-2097(2009).

[7] Chum O, Matas J, Kittler J. Locally optimized RANSAC[M]. Michaelis B, Krell G. Pattern recognition, 2781, 236-243(2003).

[8] Barath D, Ivashechkin M, Matas J. Progressive NAPSAC: sampling from gradually growing neighborhoods[EB/OL]. https://arxiv.org/abs/1906.02295

[9] Fotouhi M, Hekmatian H, Kashani-Nezhad M A et al. SC-RANSAC: spatial consistency on RANSAC[J]. Multimedia Tools and Applications, 78, 9429-9461(2019).

[10] Barath D, Matas J, Noskova J. MAGSAC: marginalizing sample consensus[C], 10189-10197(2019).

[11] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004).

[12] Park Y H, Kwon O S. Multiple homographies estimation using a guided sequential RANSAC[J]. The Journal of the Korea Contents Association, 10, 10-22(2010).

[13] Cavalli L, Larsson V, Oswald M R et al. AdaLAM: revisiting handcrafted outlier detection[EB/OL]. https://arxiv.org/abs/2006.04250

[14] Torresani L, Kolmogorov V, Rother C. Feature correspondence via graph matching: models and global optimization[M]. Forsyth D, Torr P, Zisserman Z. Computer vision-ECCV 2008, 5303, 596-609(2008).

[15] Leordeanu M, Hebert M. A spectral technique for correspondence problems using pairwise constraints[C], 1482-1489(2005).

[16] Liu H R, Yan S C. Common visual pattern discovery via spatially coherent correspondences[C], 1609-1616(2010).

[17] Loiola E M, de Abreu N M M, Boaventura-Netto P O et al. A survey for the quadratic assignment problem[J]. European Journal of Operational Research, 176, 657-690(2007).

[18] Sarlin P E, DeTone D, Malisiewicz T et al. SuperGlue: learning feature matching with graph neural networks[C], 4937-4946(2020).

[19] Caetano T S, McAuley J J, Cheng L et al. Learning graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 1048-1058(2009).

[20] Bian J W, Lin W Y, Matsushita Y et al. GMS: grid-based motion statistics for fast, ultra-robust feature correspondence[C], 2828-2837(2017).

[21] Xie Y Y, Tang Y H, Tang G G et al. Learning to find good correspondences of multiple objects[C], 2779-2786(2021).

[22] Yi K M, Trulls E, Ono Y et al. Learning to find good correspondences[C], 2666-2674(2018).

[23] Garcia-Garcia A, Gomez-Donoso F, Garcia-Rodriguez J et al. PointNet: a 3D convolutional neural network for real-time object class recognition[C], 1578-1584(2016).

[24] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https://arxiv.org/abs/1706.02413

[25] Zhang J, Sun D, Luo Z et al. Learning two-view correspondences and geometry using order-aware network[C], 5844-5853(2019).

[26] Zhao C, Cao Z G, Li C et al. NM-net: mining reliable neighbors for robust feature correspondences[C], 215-224(2019).

[33] Isack H, Boykov Y. Energy-based geometric multi-model fitting[J]. International Journal of Computer Vision, 97, 123-147(2012).

[34] Barath D, Matas J, Hajder L. Multi-H: efficient recovery of tangent planes in stereo images[C], 1-13(2016).

[35] Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619(2002).

[36] Barath D, Matas J. Multi-class model fitting by energy minimization and mode-seeking[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11220, 229-245(2018).

[37] Magri L, Fusiello A. Fitting multiple heterogeneous models by multi-class cascaded T-linkage[C], 7452-7460(2019).

[38] Kluger F, Brachmann E, Ackermann H et al. CONSAC: robust multi-model fitting by conditional sample consensus[C], 4633-4642(2020).