References
- Meshlab. 2005. Available from: http://meshlab.sourceforge.net/.
- Agarwal S, Furukawa Y, Snavely N, Simon I, Curless B, Seitz SM, Szeliski R. 2011. Building rome in a day. Commun ACM. 54:105–112.
- Altman NS. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. 46:175–185.
- Baghaie A, D’souza RM, Yu Z. 2015a. Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal. OCT images. In: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on. New York (NY): IEEE; p. 226–230.
- Baghaie A, D’Souza RM, Yu Z. 2015b. Dense correspondence and optical flow estimation using gabor, schmid and steerable descriptors. In: Advances in Visual Computing; Las Vegas (NV): Springer; p. 406–415.
- Baghaie A, Yu Z, D’souza RM. 2015. State-of-the-art in retinal optical coherence tomography image analysis. Quant Imaging Med Surg. 5:603–617.
- Baghaie A, Yu Z, D’souza RM. 2014. Fast mesh-based medical image registration. In: Advances in Visual Computing; Las Vegas (NV): Springer; p. 1–10.
- Baumberg A. 2000. Reliable feature matching across widely separated views. In: IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE; vol. 1; p. 774–781.
- Bay H, Tuytelaars T, Van Gool L. 2006. Surf: speeded up robust features. In: Computer vision-eccv 2006; Graz: Springer; p. 404–417.
- Bozzola JJ, Russell LD. 1999. Electron microscopy: principles and techniques for biologists. Jones \amp Bartlett Learning.
- Brown M, Lowe DG. 2002. Invariant features from interest point groups. In: British Machine Vision Conference; Cardiff, Wales, UK. p. 656–665.
- Calonder M, Lepetit V, Fua P, Konolige K, Bowman J, Mihelich P. 2009. Compact signatures for high-speed interest point description and matching. In: 2009 IEEE 12th International Conference on Computer Vision. Kyoto: IEEE; p. 357–364.
- Calonder M, Lepetit V, Strecha C, Fua P. 2010. Brief: binary robust independent elementary features. In: Computer vision-eccv 2010; Crete: Springer; p. 778–792.
- Chakraborty UK. 2008. Advances in differential evolution. St. Louis (MO): Mathematics \amp Computer Science Department, University of Missouri.
- Cignoni P, Rocchini C, Scopigno R. 1998. Metro: measuring error on simplified surfaces. Comput Graphics Forum. 17:167–174.
- Everingham M, Van Gool L, Williams C, Winn J, Zisserman A. 2005. Pascal visual object classes challenge results. Available from www.pascal-network.org
- Fischler MA, Bolles RC. 1981. Random sample consesus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Communications of the ACM; New York, NY, USA.
- Florack L, Romeny BTH, Koenderink JJ, Viergever MA. 1994. General intensity transformations and differential invariants. J Math Imaging Vision. 4:171–187.
- Freeman WT, Adelson EH. 1991. The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell. 13:891–906.
- Harris C, Stephens M. 1988. A combined corner and edge detector. In: Alvey Vision Conference; Manchester, UK; vol. 15; p. 50.
- Hartely R, Zisserman A. 2004. Multiple view geometry in computer vision. Cambridge University Pressy.
- Hua G, Brown M, Winder S. 2007. Discriminant embedding for local image descriptors. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007. Rio de Janeiro: IEEE; p. 1–8.
- Ke Y, Sukthankar R. 2004. Pca-sift: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. Washington (DC): IEEE; vol. 2; II–506.
- Lepetit V, Fua P. 2006. Keypoint recognition using randomized trees. IEEE Trans Pattern Anal Mach Intell. 28:1465–1479.
- Lindeberg T. 1994. Scale-space theory: a basic tool for analyzing structures at different scales. J Appl Stat. 21:225–270.
- Lindeberg T. 1998. Feature detection with automatic scale selection. Int J Comput Vision. 30:79–116.
- Lowe DG. 1999. Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999. Kerkyra: IEEE; vol. 2; p. 1150–1157.
- Lowe DG. 2004. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 60:91–110.
- Mikolajczyk K, Schmid C. 2001. Indexing based on scale invariant interest points. In: Proceedings. Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. Vancouver: IEEE; vol. 1; p. 525–531.
- Mikolajczyk K, Schmid C. 2002. An affine invariant interest point detector. In: Computer Vision eccv 2002; Copenhagen: Springer; p. 128–142.
- Mikolajczyk K, Schmid C. 2005. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell. 27:1615–1630.
- Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L. 2005. A comparison of affine region detectors. Int J Comput Vision. 65:43–72.
- Mindru F, Tuytelaars T, Gool LV, Moons T. 2004. Moment invariants for recognition under changing viewpoint and illumination. Comput Vision Image Und. 94:3–27.
- James Munkres. 1999. Topologyn. Prentice Hall.
- Ozuysal M, Calonder M, Lepetit V, Fua P. 2010. Fast keypoint recognition using random ferns. IEEE Trans Pattern Anal Mach Intell. 32:448–461.
- Parry-Vernon KD. 2000. Scanning electron microscopy: an introduction. III-Vs Rev. 13:40–44.
- Paysan P, Knothe R, Amberg B, Romdhani S, Vetter T. 2009. A 3d face model for pose and illumination invariant face recognition. In: Proceedings of the 6th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS) for Security, Safety and Monitoring in Smart Environments; Genoa, Italy.
- Rosin PL. 1999. Measuring corner properties. Comput Vision Image Und. 73:291–307.
- Rosten E, Drummond T. 2006. Machine learning for high-speed corner detection. In: Computer Vision-eccv 2006; Graz: Springer; p. 430–443.
- Rublee E, Rabaud V, Konolige K, Bradski G. 2011. Orb: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV). Barcelona: IEEE; p. 2564–2571.
- Shakhnarovich G. 2005. Learning task-specific similarity [dissertation]. Cambridge (MA): Massachusetts Institute of Technology.
- Tafti AP, Hassannia H, Piziak D, Yu Z. 2015. Selibcv: a service library for computer vision researchers. In: Advances in Visual Computing; Las Vegas (NV): Springer; p. 542–553.
- Tafti AP, Kirkpatrick AB, Alavi Z, Owen HA, Yu Z. 2015. Recent advances in 3d sem surface reconstruction. Micron. 78:54–66.
- Tafti AP, Kirkpatrick AB, Holz JD, Owen HA, Yu Z. 2016. 3dsem: a 3d microscopy dataset. Data Brief. 6:112–116.
- Tafti AP, Kirkpatrick AB, Owen HA, Yu Z. 2014. 3d microscopy vision using multiple view geometry and differential evolutionary approaches. 10th Int Symp Visual Comput (ISVC), LNCS. 8888: 141–152.
- Torralba A, Fergus R, Weiss Y. 2008. Small codes and large image databases for recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008.CVPR 2008. Anchorage (AK): IEEE; p. 1–8.
- Tuytelaars T, Schmid C. 2007. Vector quantizing feature space with a regular lattice. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007. Rio de Janeiro: IEEE; p. 1–8
- Winder S, Hua G, Brown M. 2009. Picking the best daisy. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. Miami (FL): IEEE; p. 178–185.
- Witkin AP. 1984. Scale-space filtering: a new approach to multi-scale description. In: Acoustics Speech and Signal Processing, IEEE International Conference on ICASSP’84; San Diego, CA, USA. vol. 9; p. 150–153.
- Wöhler C. 2012. 3d Computer vision: efficient methods and applications. Springer Science & Business Media.