[1]	A. Goshtasby, Theory and Applications of Image Registration. Hoboken, NJ, USA: John Wiley & Sons, 2017.
[2]	D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 50, no. 2, pp. 91-110, 2004.
[3]	T. Haidegger, “Autonomy for surgical robots: concepts and paradigms,” IEEE Trans. Med. Robot. Bionics, vol. 1, no. 2, pp. 65-76, May 2019. 
[4]	A. Leporini et al., “Technical and functional validation of a teleoperated multirobots platform for minimally invasive surgery,” IEEE Trans. Med. Robot. Bionics, vol. 2, no. 2, pp. 148-156, May 2020.
[5]	L. Qian, J. Y. Wu, S. P. DiMaio, N. Navab and P. Kazanzides, “A review of augmented reality in robotic-assisted surgery,” IEEE Trans. Med. Robot. Bionics, vol. 2, no. 1, pp. 1-16, Feb. 2020.
[6]	C. Girerd, A. V. Kudryavtsev, P. Rougeot, P. Renaud, K. Rabenorosoa and B. Tamadazte, “Automatic tip-steering of concentric tube robots in the trachea based on visual SLAM,” IEEE Trans. Med. Robot. Bionics, vol. 2, no. 4, pp. 582-585, Nov. 2020.
[7]	Y. Liu et al., “Real-time robust stereo visual SLAM system based on bionic eyes,” IEEE Trans. Med. Robot. Bionics, vol. 2, no. 3, pp. 391-398, Aug. 2020.
[8]	J. Song, J. Wang, L. Zhao, S. Huang and G. Dissanayake, “MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing,” IEEE Robot. Autom. Lett, vol. 3, no. 4, pp. 4068-4075, Oct. 2018.
[9]	M. N. Cheema et al., “Image-aligned dynamic liver reconstruction using intra-operative field of views for Minimal Invasive Surgery,” IEEE Trans. Biomed. Eng., vol. 66, no. 8, pp. 2163-2173, Aug. 2019.
[10]	H. Zhou and J. Jagadeesan, “Real-time dense reconstruction of tissue surface from stereo optical video,” IEEE Trans. Med. Imag., vol. 39, no. 2, pp. 400-412, Feb. 2020.
[11]	P. Vagdargi et al., “Real-time 3-D video reconstruction for guidance of transventricular neurosurgery,” IEEE Trans. Med. Robot. Bionics, vol. 5, no. 3, pp. 669-682, Aug. 2023.
[12]	V. Penza, Z. Cheng, M. Koskinopoulou, A. Acemoglu, D. G. Caldwell and L. S. Mattos, “Vision-guided autonomous robotic electrical bio-impedance scanning system for abnormal tissue detection,” IEEE Trans. Med. Robot. Bionics, vol. 3, no. 4, pp. 866-877, Nov. 2021.
[13]	S. Zhang, L. Zhao, S. Huang, M. Ye and Q. Hao, “A template-based 3D reconstruction of colon structures and textures from stereo colonoscopic images,” IEEE Trans. Med. Robot. Bionics, vol. 3, no. 1, pp. 85-95, Feb. 2021.
[14]	D. Stoyanov, M. Visentini-Scarzanella, P. Pratt, and G. Z. Yang, “Real-time stereo reconstruction in robotic assisted minimally invasive surgery,” in Proc. 10th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2010.
[15]	G. A. Puerto-Souza, J. A. Cadeddu and G. Mariottini, “Toward long-term and accurate augmented-reality for monocular endoscopic videos,” IEEE Trans. Biomed. Eng., vol. 61, no. 10, pp. 2609–2620, Oct. 2014.
[16]	M. C. Yip, D. G. Lowe, S. E. Salcudean, R. N. Rohling and C. Y. Nguan, “Tissue tracking and registration for image-guided surgery,” IEEE Trans. Med. Imag., vol. 31, no. 11, pp. 2169–2182, Nov. 2012.
[17]	T. Bergen and T. Wittenberg, “Stitching and surface reconstruction from endoscopic image sequences: A review of applications and methods,” IEEE Journal Biomed. Health Informatics, vol. 20, no. 1, pp. 304–321, Jan. 2016.
[18]	H. Zhou and J. Jayender, “Real-time nonrigid mosaicking of laparoscopy images,” IEEE Trans. Med. Imag., vol. 40, no. 6, pp. 1726–1736, June 2021.
[19]	H. Bay, A. Ess, T. Tuytelaars and L. Gool, “Speeded-up robust features (SURF),” Comput. Vis. Image Understand., vol.110, No.3, 2008, pp. 346–359, 2008.
[20]	E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proc. ICCV, 2011, pp. 2564–2571.
[21]	J. Sun, Z. Shen, Y. Wang, H. Bao and X. Zhou, “LoFTR: Detector-free local feature matching with transformers,” in Proc. CVPR, 2021, pp. 8918–8927.
[22]	Q. Wang, J. Zhang, K. Yang, K. Peng and R. Stiefelhagen, “Matchformer: Interleaving attention in transformers for feature matching,” in Proc. ACCV, 2022, pp. 256–273.
[23]	H. Chen, Z. Luo, L. Zhou, Y. Tian, M. Zhen, T. Fang, D. McKinnon, Y. Tsin and L. Quan, “Aspanformer: Detector-free image matching with adaptive span transformer,” in Proc. ECCV, 2022, pp. 20–36.
[24]	J. Ni, Y. Li, Z. Huang, H. Li, H. Bao, Z. Cui and G. Zhang, “PATS: Patch area transportation with subdivision for local feature matching,” in Proc. CVPR, 2023, pp. 17776–17786.
[25]	D. DeTone, T. Malisiewicz and A. Rabinovich, “SuperPoint: Self-supervised interest point detection and description,” in Proc. CVPRW, 2018, pp. 337–349.
[26]	P. -E. Sarlin, D. DeTone, T. Malisiewicz and A. Rabinovich, “SuperGlue: Learning feature matching with graph neural networks,” in Proc. CVPR, 2020, pp. 4937–4946.
[27]	H. Chen, Z. Luo, J. Zhang, L. Zhou, X. Bai, Z. Hu, C.-L. Tai and L. Quan, “Learning to match features with seeded graph matching network,” in Proc. ICCV, 2021, pp. 6281–6290.
[28]	P. Lindenberger, P. -E. Sarlin and M. Pollefeys, “LightGlue: Local feature matching at light speed,” in Proc. ICCV, 2023, pp. 17581–17592.
[29]	Q.H. Tran, T.J. Chin, G. Carneiro, M.S. Brown, and D. Suter, “In defence of RANSAC for outlier rejection in deformable registration,” in Proc. ECCV, 2012, pp. 274–287.
[30]	G. A. Puerto-Souza and G. Mariottini, “A fast and accurate feature-matching algorithm for minimally-invasive endoscopic images,” IEEE Trans. Med. Imag., vol. 32, no. 7, pp. 1201–1214, July 2013.
[31]	H. Zhou and J. Jayender, “EMDQ: Removal of image feature mismatches in real-time,” IEEE Trans. Image Process., vol. 31, pp. 706-720, 2022.
[32]	J. Ma, J. Zhao, J. Tian, A. L. Yuille, and Z. Tu, “Robust point matching via vector field consensus,” IEEE Trans. Image Process., vol. 23, no. 4, pp. 1706–1721, Apr. 2014.
[33]	J. Ma, J. Zhao, J. Jiang, H. Zhou, and X. Guo, “Locality preserving matching,” Int. J. Comput. Vis., vol. 127, no. 5, pp. 512–531, 2019.
[34]	J. Ma, X. Jiang, J. Jiang, J. Zhao, and X. Guo, “LMR: Learning a two-class classifier for mismatch removal,” IEEE Trans. Image Process., vol. 28, pp. 4045–4059, Aug. 2019.
[35]	X. Jiang, J. Ma, J. Jiang and X. Guo, “Robust feature matching using spatial clustering with heavy outliers,” IEEE Trans. Image Process., vol. 29, pp. 736–746, 2020.
[36]	G. Wang, and Y. Chen, “Robust feature matching using guided local outlier factor,” Pattern Recognit., vol. 117, 2021, 107986.
[37]	M. Brown and D. G. Lowe, “Invariant features from interest point groups,” in British Machine Vision Conference, Cardiff, Wales, 2002, pp. 656–665.
[38]	E. Rosten and T. Drummond, “Machine learning for high-speed corner detection,” in Proc. ECCV, 2006, pp. 430–443.
[39]	E. Rosten, R. Porter, and T. Drummond, “Faster and better: a machine learning approach to corner detection,” IEEE Trans. Pat. Ana. Mach. Intel., vol. 32, no. 1, pp. 105–119, Jan 2010.
[40]	M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: Binary robust independent elementary features,” in Proc. ECCV, 2010, pp. 778–792.
[41]	M. A. Fischler and R. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM, vol. 24, no. 6, pp. 381–395, 1981.
[42]	A. N. Tikhonov, and V. Y. Arsenin, Solutions of Ill-posed Problems. Washington, DC, USA: Winston, 1977.
[43]	M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proc. KDD, 1996, pp. 226–231.
[44]	M. R. Pourshahabi, M. O. Ahmad and M. N. S. Swamy, “A Very Fast and Robust Method for Refinement of Putative Matches of Features in MIS Images for Robotic-Assisted Surgery,” IEEE Trans. Med. Robot. Bionics, vol. 6, no. 2, pp. 419–432, May 2024.
[45]	J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Commun. ACM, vol. 18, No. 9, 1975, pp 509–517.
[46]	G. A. Puerto-Souza and G. Mariottini, Laparoscopic dataset [Online] Accessed: 2018, http://ranger.uta.edu/gianluca/feature_matching (2014)
[47]	Hamlyn Centre laparoscopic/endoscopic video datasets [Online] Accessed: 2018, http://hamlyn.doc.ic.ac.uk/vision (2012)
[48]	A. Goshtasby, “Image registration by local approximation methods,” Image Vis. Computing 6, no. 4, November 1988, pp 255–61.
[49]	P. Pratt, D. Stoyanov, M. Visentini-Scarzanella, and G. Z. Yang, “Dynamic guidance for robotic surgery using image-constrained biomechanical models,” in Proc. 10th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2010.
[50]	M. Misawa, et al., “Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video),” Gastrointestinal Endoscopy, Vol. 93, Issue 4, pp. 960–967.e3, 2021.
[51]	H. Itoh et al., 2020, “SUN colonoscopy video database,” Dataset, sundatabase, Accessed: 2023. [Online]. Available: http://amed8k.sundatabase.org/ 
[52]	H. Borgli, et al., “HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy,” Sci Data 7, 283, 2020.
[53]	H. Borgli et al., 2020, “Hyperkvasir—The largest gastrointestinal dataset,” Dataset, simula. Accessed: 2023. [Online]. Available: https://datasets.simula.no/hyper-kvasir/
[54]	M. R. Pourshahabi, M. O. Ahmad and M.N.S. Swamy, “SIFOR: A Robust Scheme for Detection, Extraction, and Matching of Features in MIS Images for Robotic-Assisted Surgery,” currently under review.
[55]	F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Trans. Pat. Ana. Mach. Intel., vol. 11, no. 6, pp. 567–585, June 1989.
[56]	C. Harris and M. Stephens, “A combined corner and edge detector,” in Alvey Vision Conference, 1988, pp. 147–151.
[57]	P. J. Clark and F.C. Evans, “Distance to nearest neighbor as a measure of spatial relationships in populations,” Ecology, vol. 35, no. 4, pp. 445–453, 1954.
[58]	K. P. Donnelly, “Simulations to determine the variance and edge effect of total nearest neighborhood distance,” in Simulation studies in archeology, ed. I. Hodder, Cambridge, NY, USA: Cambridge University Press, 1978, pp. 91–95.
[59]	T. J. Oyana and F. M. Margai, Spatial analysis: statistics, visualization, and computational methods. Boca Raton, FL, USA: Taylor & Francis Group, 2016.
[60]	L. Zagorchev and A. Goshtasby, “A comparative study of transformation functions for nonrigid image registration,” IEEE Trans. Image Process., vol. 15, no. 3, pp. 529–538, March 2006.