圖像匹配綜述[轉載]survey、overview、review

期刊發表 | 文獻綜述的含義及撰寫方法

https://www.zhihu.com/people/xing-yu-6-67

圖像特徵匹配（1）——問題定義&研究背景與意義

https://zhuanlan.zhihu.com/p/112071397

圖像特徵匹配（2）——研究現狀及發展趨勢

https://zhuanlan.zhihu.com/p/112082541

 

【參考文獻】

[1] 馬頌德. 計算機視覺: 計算理論與算法基礎. 北京: 科學出版社 [M]. 1998.

[2] 宋智禮. 圖像配準技術及其應用的研究 [D]. 復旦大學, 2010.

[3] 馬佳義. 基於非參數模型的點集匹配算法研究 [D]. 華中科技大學, 2014.

[4] 於偉. 基於卷積神經網絡特徵的圖像匹配研究 [D]. 哈爾濱工業大學, 2017.

[5] Zitova B, Flusser J. Image registration methods: a survey[J]. Image and vision computing, 2003, 21(11): 977–1000.

[6] Dawn S, Saxena V, Sharma B. Remote sensing image registration techniques: A survey[C]. International Conference on Image and Signal Processing. 2010: 103– 112.

[7] Pratt W K. Digital image processing john wiley & sons[J]. Inc., New York, 1991.

[8] Viola P, Wells iii W M. Alignment by maximization of mutual information[J]. International journal of computer vision, 1997, 24(2): 137–154.

[9] Barnea D I, Silverman H F. A class of algorithms for fast digital image registration[J]. IEEE transactions on Computers, 1972, 100(2): 179–186.

[10] Bracewell R N, Bracewell R N. The Fourier transform and its applications: Vol 31999[M]. [S.l.]: McGraw-Hill New York, 1986.

[11] De castro E, Morandi C. Registration of translated and rotated images using finite Fourier transforms[J]. IEEE Transactions on pattern analysis and machine intelligence, 1987(5): 700–703.

[12] Chen Q-S, Defrise M, Deconinck F. Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1994(12): 1156–1168.

[13] Tuytelaars T, Mikolajczyk K, others. Local invariant feature detectors: a survey[J]. Foundations and trends® in computer graphics and vision, 2008, 3(3): 177–280.

[14] Harris C G, Stephens M, others. A combined corner and edge detector.[C]. Alvey vision conference: Vol 15. 1988: 10–5244.

[15] BeaudetPR.Rotationallyinvariantimageoperators[C].Proc.4thInt.JointConf. Pattern Recog, Tokyo, Japan, 1978. 1978.

[16] Rosten E, Drummond T. Machine learning for high-speed corner detection[C]. European conference on computer vision. 2006: 430–443.

[17] Yi K M, Trulls E, Lepetit V, et al. Lift: Learned invariant feature transform[C]. European Conference on Computer Vision. 2016: 467–483.

[18] Matas J, Chum O, Urban M, et al. Robust wide-baseline stereo from maximally stable extremal regions[J]. Image and vision computing, 2004, 22(10): 761–767.

[19] Moravec H P. Techniques towards automatic visual obstacle avoidance[J], 1977.

[20] Brogefors G. Hierarchical chamfer matching: A parametric edge matching algorithm[J].IEEETransactionsonPatternAnalysis&MachineIntelligence,1988(6): 849–865.

[21] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shapecontexts[J].IEEETransactionsonPatternAnalysis&MachineIntelligence, 2002(4): 509–522.

[22] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points[C]. null. 2001: 525.

[23] Radke R J, Andra S, Al-kofahi O, et al. Image change detection algorithms: a systematic survey[J]. IEEE transactions on image processing, 2005, 14(3): 294– 307.

[24] Zheng L, Yang Y, Tian Q. SIFT meets CNN: A decade survey of instance retrieval[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 40(5): 1224–1244.

[25] Ma J, Ma Y, Li C. Infrared and visible image fusion methods and applications: A survey[J]. Information Fusion, 2019, 45: 153–178.

[26] Fuentes-pacheco J, Ruiz-ascencio J, Rendón-mancha J M. Visual simultaneous localization and mapping: a survey[J]. Artificial Intelligence Review, 2015, 43(1):

[27] Fan B, Kong Q, Wang X, et al. A Performance Evaluation of Local Features for Image Based 3D Reconstruction[J]. arXiv preprint arXiv:1712.05271, 2017.

[28] Mur-artal R, Montiel J M M, Tardos J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE transactions on robotics, 2015, 31(5): 1147– 1163.

[29] Mur-artalR, TardósJD.Orb-slam2: Anopen-sourceslamsystemformonocular, stereo, andrgb-dcameras[J].IEEETransactionsonRobotics, 2017, 33(5): 1255– 1262.

[30] Hua Z, Li Y, Li J. Image stitch algorithm based on SIFT and MVSC[C]. 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery: Vol 6. 2010: 2628–2632.

[31] Wang C, Wang L, Liu L. Progressive mode-seeking on graphs for sparse feature matching[C]. European Conference on Computer Vision. 2014: 788–802.

【參考文獻】

[32] SmithSM,BradyJM.SUSAN—anewapproachtolowlevelimageprocessing[J]. International journal of computer vision, 1997, 23(1): 45–78.

[33] Rosten E, Porter R, Drummond T. Faster and better: A machine learning approach to corner detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 32(1): 105–119.

[34] MairE,HagerGD,BurschkaD,etal.Adaptiveandgenericcornerdetectionbased on the accelerated segment test[C]. European conference on Computer vision. 2010: 183–196.

[35] Rublee E, Rabaud V, Konolige K, et al. ORB: An efficient alternative to SIFT or SURF.[C]. ICCV: Vol 11. 2011: 2.

[36] Lindeberg T. Feature detection with automatic scale selection[J]. International journal of computer vision, 1998, 30(2): 79–116.

[37] LoweDG, others.Objectrecognitionfromlocalscale-invariantfeatures.[C].iccv: Vol 99. 1999: 1150–1157.

[38] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2): 91–110.

[39] Bay H, Tuytelaars T, Van gool L. Surf: Speeded up robust features[C]. European conference on computer vision. 2006: 404–417.

[40] Morel J-M, Yu G. ASIFT: A new framework for fully affine invariant image comparison[J]. SIAM journal on imaging sciences, 2009, 2(2): 438–469.

[41] Abdel-hakim A E, Farag A A. CSIFT: A SIFT descriptor with color invariant characteristics[C]. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06): Vol 2. 2006: 1978–1983.

[42] Ke Y, Sukthankar R, others. PCA-SIFT: A more distinctive representation for local image descriptors[J]. CVPR (2), 2004, 4: 506–513.

[43] Agrawal M, Konolige K, Blas M R. Censure: Center surround extremas for realtime feature detection and matching[C]. European Conference on Computer Vision. 2008: 102–115.

[44] Schmid C, Mohr R, Bauckhage C. Evaluation of interest point detectors[J]. International Journal of computer vision, 2000, 37(2): 151–172.

[45] Mukherjee D, Wu Q J, Wang G. A comparative experimental study of image feature detectors and descriptors[J]. Machine Vision and Applications, 2015, 26(4): 443–466.

[46] Uchida Y. Local feature detectors, descriptors, and image representations: A survey[J]. arXiv preprint arXiv:1607.08368, 2016.

[47] Krig S. Interest point detector and feature descriptor survey[G]. Computer vision metrics. [S.l.]: Springer, 2016: 187–246.

[48] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015, 521(7553): 436.

[49] Choy C B, Gwak J, Savarese S, et al. Universal correspondence network[C]. Advances in Neural Information Processing Systems. 2016: 2414–2422.

[50] Rocco I, Cimpoi M, Arandjelović R, et al. Neighbourhood consensus networks[C]. Advances in Neural Information Processing Systems. 2018: 1658–1669.

[51] Bookstein F L. Principal warps: Thin-plate splines and the decomposition of deformations[J]. IEEE Transactions on pattern analysis and machine intelligence,1989, 11(6): 567–585.

[52] AradN, DynN, ReisfeldD,etal.Imagewarpingbyradialbasisfunctions: Application to facial expressions[J]. CVGIP: Graphical models and image processing, 1994, 56(2): 161–172.

[53] Chui H, Rangarajan A. A new point matching algorithm for non-rigid registration[J]. Comput. Vis. Image Understand., 2003, 89: 114–141.

[54] Myronenko A, Song X. Point Set Registration: Coherent Point Drift[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32(12): 2262–2275.

[55] Cook D J, Holder L B. Mining graph data[M]. [S.l.]: John Wiley & Sons, 2006.

[56] BabaiL.Groups,Graphs,Algorithms: TheGraphIsomorphismProblem[J].Proc. Internat. Congr. of Mathematicians 2018, 2018.

[57] Yan J, Cho M, Zha H, et al. Multi-graph matching via affinity optimization with graduated consistency regularization[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38(6): 1228–1242.

[58] Yan J, Wang J, Zha H, et al. Consistency-driven alternating optimization for multigraph matching: a unified approach.[J]. IEEE Trans. Image Process., 2015, 24(3): 994–1009.

[59] Umeyama S. An eigendecomposition approach to weighted graph matching problems[J]. IEEE transactions on pattern analysis and machine intelligence, 1988, 10(5): 695–703.

[60] Leordeanu M, Hebert M. A Spectral Technique for Correspondence Problems Using Pairwise Constraints[C]. Proc. IEEE Int. Conf. Comput. Vis.. 2005: 1482– 1489.

[61] Liu H, Yan S. Common Visual Pattern Discovery via Spatially Coherent Correspondence[C]. Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. 2010: 1609– 1616.

[62] SuhY,ChoM,LeeKM.Graphmatchingviasequentialmontecarlo[C].European Conference on Computer Vision. 2012: 624–637.

[63] Cho M, Lee J, Lee K M. Reweighted random walks for graph matching[C]. European conference on Computer vision. 2010: 492–505.

[64] CaelliT,KosinovS.Aneigenspaceprojectionclusteringmethodforinexactgraph matching[J]. IEEE transactions on pattern analysis and machine intelligence, 2004, 26(4): 515–519.

[65] Dong J, Soatto S. Domain-size pooling in local descriptors: DSP-SIFT[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 5097–5106.

[66] Calonder M, Lepetit V, Strecha C, et al. Brief: Binary robust independent elementary features[C]. European conference on computer vision. 2010: 778–792.

[67] Leutenegger S, Chli M, Siegwart R Y. BRISK: Binary robust invariant scalable keypoints[M]. [S.l.]: IEEE, 2011.

[68] Alahi A, Ortiz R, Vandergheynst P. Freak: Fast retina keypoint[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012: 510–517.

[69] Johnson A E, Hebert M. Using spin images for efficient object recognition in cluttered 3D scenes[J]. IEEE Transactions on pattern analysis and machine intelligence, 1999, 21(5): 433–449.

[70] ZaharescuA,BoyerE,VaranasiK,etal.Surfacefeaturedetectionanddescription with applications to mesh matching[C]. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009. 2009: 373–380.

[71] FischlerMA,BollesRC.Randomsampleconsensus: aparadigmformodelfitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 6(6): 381–395.

[72] Torr P H, Zisserman A. MLESAC: A New Robust Estimator with Application to Estimating Image Geometry[J]. Comput. Vis. Image Understand., 2000, 78(1): 138–156.

[73] Chum O, Matas J. Matching with PROSAC - Progressive Sample Consensus[C]. CVPR. 2005: 220–226.

[74] Sattler T, Leibe B, Kobbelt L. SCRAMSAC: Improving RANSAC’s efficiency with a spatial consistency filter[C]. Proc. IEEE Int. Conf. Comput. Vis.. 2009:2090–2097.

[75] Raguram R, Chum O, Pollefeys M, et al. USAC: a universal framework for random sample consensus[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8): 2022–2038.

[76] LiX,HuZ.RejectingMismatchesbyCorrespondenceFunction[J].Int.J.Comput. Vis., 2010, 89(1): 1–17.

[77] TikhonovAN, ArseninVI.Solutionsofill-posedproblems: Vol14[M].[S.l.]: Vh Winston, 1977.

[78] Boyd S, Vandenberghe L. Convex optimization[M]. [S.l.]: Cambridge university press, 2004.

[79] Ma J, Zhao J, Tian J, et al. Robust Estimation of Nonrigid Transformation for Point Set Registration[C]. CVPR. 2013: 2147–2154.

[80] Ma J, Zhao J, Tian J, et al. Regularized vector field learning with sparse approximation for mismatch removal[J]. Pattern Recognit., 2013, 46(12): 3519–3532.

[81] MaJ,ZhaoJ,TianJ,etal.RobustPointMatchingviaVectorFieldConsensus[J]. IEEE Trans. Image Process., 2014, 23(4): 1706–1721.

[82] Ma J, Zhao J, Ma Y, et al. Non-rigid visible and infrared face registration via regularized Gaussian fields criterion[J]. Pattern Recognit., 2015, 48(3): 772–784.

[83] Ma J, Qiu W, Zhao J, et al. Robust L2E Estimation of Transformation for NonRigid Registration[J]. IEEE Trans. Signal Process., 2015, 63(5): 1115–1129.

[84] WangG, WangZ, ChenY,etal.Arobustnon-rigidpointsetregistrationmethod basedonasymmetricgaussianrepresentation[J].Comput.Vis.ImageUnderstand., 2015, 141: 67–80.

[85] Wang G, Wang Z, Chen Y, et al. Context-Aware Gaussian Fields for Non-rigid Point Set Registration[C]. CVPR. 2016: 5811–5819.

[86] Wang G, Zhou Q, Chen Y. Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields[J]. IEEE Trans. Image Process., 2017, 26(4): 1759–1769.

[87] Ma J, Zhao J, Jiang J, et al. Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization[C]. Proc. AAAI Conf. Artificial Intelligence. 2017: 4218–4224.

[88] Ma J, Zhou H, Zhao J, et al. Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming[J]. IEEE Trans. Geosci. Remote Sens., 2015, 53(12): 6469–6481.

[89] Ma J, Zhao J, Guo H, et al. Locality preserving matching[C]. Proc. Int. Joint Conf. Artif. Intell.. 2017: 4492–4498.

[90] Ma J, Jiang J, Zhou H, et al. Guided locality preserving feature matching for remote sensing image registration[J]. IEEE Trans. Geosci. Remote Sens., 2018.

[91] BianJ, LinW-Y, MatsushitaY,etal.Gms: Grid-basedmotionstatisticsforfast, ultra-robust feature correspondence[C]. Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. 2017: 2828–2837.

[92] Lin W-Y, Wang F, Cheng M-M, et al. CODE: Coherence based decision boundariesforfeaturecorrespondence[J].IEEETrans.PatternAnal.Mach.Intell.,2018, 40(1): 34–47.

[93] Lin W-Y, Cheng M-M, Lu J, et al. Bilateral functions for global motion modeling[C]. Proc. Eur. Conf. Comput. Vis.. 2014: 341–356.

[94] Simo-serra E, Trulls E, Ferraz L, et al. Discriminative learning of deep convolutionalfeaturepointdescriptors[C].ProceedingsoftheIEEEInternationalConference on Computer Vision. 2015: 118–126.

[95] Mishchuk A, Mishkin D, Radenovic F, et al. Working hard to know your neighbor’s margins: Local descriptor learning loss[C]. Advances in Neural Information Processing Systems. 2017: 4826–4837.

[96] Wei X, Zhang Y, Gong Y, et al. Kernelized subspace pooling for deep local descriptors[C].ProceedingsoftheIEEEConferenceonComputerVisionandPattern Recognition. 2018: 1867–1875.

[97] Ono Y, Trulls E, Fua P, et al. LF-Net: learning local features from images[C]. Advances in Neural Information Processing Systems. 2018: 6237–6247.

[98] DetoneD,MalisiewiczT,RabinovichA.Superpoint: Self-supervised interest point detection and description[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018: 224–236.

[99] Han X, Leung T, Jia Y, et al. Matchnet: Unifying feature and metric learning for patch-based matching[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3279–3286.

[100] WangJ,ZhouF,WenS,etal.Deep metric learning with angularl oss[C].ProceedingsoftheIEEEInternationalConferenceonComputerVision.2017: 2593–2601.

[101] Schönberger J L, Hardmeier H, Sattler T, et al. Comparative evaluation of handcrafted and learned local features[C]. Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. 2017: 6959–6968.

[102] Tian Y, Fan B, Wu F, et al. L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space.[C]. Cvpr: Vol 1. 2017: 6.

[103] Zbontar J, Lecun Y. Stereo matching by training a convolutional neural network to compare image patches[J]. Journal of Machine Learning Research, 2016, 17(132): 2.

[104] Revaud J, Weinzaepfel P, Harchaoui Z, et al. Deepmatching: Hierarchical deformable dense matching[J]. International Journal of Computer Vision, 2016, 120(3): 300–323.

[105] Menze M, Heipke C, Geiger A. Object Scene Flow[J]. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS), 2018.

[106] ScharsteinD,SzeliskiR.ATaxonomyandEvaluationofDenseTwo-FrameStereo Correspondence Algorithms[J]. International Journal of Computer Vision, 2002, 47(1-3): 7–42.

[107] ZagoruykoS,KomodakisN.Learning to compare image patches via convolutional neural networks[C].ProceedingsoftheIEEEConferenceonComputerVisionand Pattern Recognition. 2015: 4353–4361.

[108] AltwaijryH,TrullsE,HaysJ,etal.Learning to match aerial images with deep attentive architectures[C].ProceedingsoftheIEEEConferenceonComputerVision and Pattern Recognition. 2016: 3539–3547.

[109] Balakrishnan G, Zhao A, Sabuncu M R, et al. An Unsupervised Learning Model for Deformable Medical Image Registration[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 9252–9260.

[110] Jiang P, Shackleford J A. CNN Driven Sparse Multi-Level B-Spline Image Registration[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 9281–9289.

[111] Qi C R, Su H, Mo K, et al. Pointnet: Deep learning on point sets for 3d classification and segmentation[J]. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2017, 1(2): 4.

[112] Deng H, Birdal T, Ilic S. Ppfnet: Global context aware local features for robust 3d point matching[J]. Computer Vision and Pattern Recognition (CVPR). IEEE, 2018, 1.

[113] YiKM,TrullsE,OnoY,etal.LearningtoFindGoodCorrespondences[C].Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. 2018: 1–9.

[114] Luo Z, Shen T, Zhou L, et al. Geodesc: Learning local descriptors by integrating geometry constraints[C]. Proceedings of the European Conference on Computer Vision (ECCV). 2018: 168–183.

[115] Zhao C, Cao Z, Li C, et al. NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences[J]. arXiv preprint arXiv:1904.00320, 2019.

[116] 趙鍵. 點模式匹配算法研究 [D]. 國防科學技術大學, 2012.

[117] 柳成蔭. 基於點特徵的多模與多視角圖像非剛性配准算法研究 [D]. 華中科技大學, 2018.

[118] Tron R, Zhou X, Esteves C, et al. Fast multi-image matching via density-based clustering[C]. Proceedings of the IEEE International Conference on Computer Vision. 2017: 4057–4066.

[119] Maset E, Arrigoni F, Fusiello A. Practical and efficient multi-view matching[C]. Proceedings of the IEEE International Conference on Computer Vision. 2017: 4568–4576.

[120] Hu N, Huang Q, Thibert B, et al. Distributable consistent multi-object matching[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2463–2471.