Object-independent structural image analysis: History and modern approaches

1. E. Taijo, Fujitsu Corp., “Device for image recognition,” Japanese Patent No. 56-33747 (1973).
2. Hajime Industries, Ltd., “Pattern discrimination method,” British Claim No. 2,035,645, Abstract No. 4766 (1980).
3. V. Lutsiv, Automatic Image Analysis. Object-Independent Structural Approach (Lambert Academic Publishing, Saarbrucken, Germany, 2011).
4. D. Casasent and A. Furman, “Sources of correlation degradation,” Appl. Opt. 16, 1652 (1977).
5. D. Casasent, “Optical coherent pattern recognition,” Proc. IEEE 67, 813 (1979).
6. D. Casasent and D. Psaltis, “Deformation-invariant, space-variant optical pattern recognition,” Prog. Opt. 16, 291 (1978).
7. Q. Chen, M. Defrise, and F. Deconinck, “Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 1156 (1994).
8. J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles (Addison Wesley, Reading, Mass., 1974; Mir, Moscow, 1978).
9. V. V. Chavchanidze, “Method of classifying objects according to N attributes,” USSR Patent No. 596,979 (1978).
10. Zh. M. Agadzhayan, “Device for counting and recognizing the images of microobjects,” USSR Patent No. 898466 (1982).

11. Koge gijutsu inte, “Setup for identifying contour information,” Japanese Claim No. 56-27913, Tokke Kokho, No. 6-698 (1981).
12. Nippon Denki, Inc., “Device for Image Recognition,” Japanese Claim No. 57-21750, Tokke Kokho, No. 6-544 (1982).
13. Koge gijutsu inte, “Method for distinguishing the characteristics of lines of images,” Japanese Claim No. 56-15544, Tokke Kokho, No. 6-389 (1981).
14. S. Maitra, “Moment invariants,” Proc. IEEE. 67, 697 (1979).
15. M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Inf. Theory IT-8, 179 (1962).
16. A. K. Zherebko and V. R. Lutsiv, “Matched filtering in natural and artificial neural networks,” Opt. Zh. 66, No. 9, 69 (1999) [J. Opt. Technol. 66, 822 (1999)].
17. Ya. Sindzi, Khitati Seisakuse Corp., “Device for recognizing images by means of their fragment-by-fragment comparison with standards,” Japanese Claim No. 53-17021, Tokke Kokho, No. 47-47204 (1978).
18. S. Kashioka, M. Eijri, M. Mese, and T. Miyatake, Hitachi, Ltd., “Pattern position detecting system,” U.S. Patent No. 4,091,394 (1978).
19. Calspan Corp., “Fingerprint identification method and apparatus,” British Claim No. 1,577,797, Abridgments, No. 4779 (1980).
20. E. N. Schroeder and K. Lexington, “Adaptive alignment for pattern recognition system,” U.S. Patent No. 4,204,193 (1980).
21. K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biol. Cybernet. 36, No. 4, 93 (1980).
22. F. Rosenblatt, Principles of Neurodynamics (Spartan Books, New York, 1962; Mir, Moscow, 1965).
23. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” Parallel Distrib. Process. 1, 318 (1986).
24. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323, 533 (1986).
25. D. P. Casasent and E. Barnard, “Adaptive clustering optical neural net,” Appl. Opt. 29, 2603 (1990).
26. K. B. Farr and R. L. Hartman, “Optical-digital neural network system for aided target recognition,” Proc. SPIE 2485, 141 (1995).
27. A. McAulay and I. Kadar, “Image compression and automatic target recognition,” Proc. SPIE 1099, 74 (1989).
28. K. Yamada, H. Kami, J. Tsukumo, and T. Temma, “Handwritten numeral recognition by multilayered neural network with improved learning algorithm,” in International Joint Conference on Neural Networks (IJCNN), Washington, D.C., 1989, pp. II-259–II-266.
29. K. Yamada, “Improved learning algorithm for multilayer neural networks and handwritten numeral recognition,” NEC Research Dev. No. 98, 81 (1990).
30. K. Fukushima, “Neural network model for selective attention in visual pattern recognition and associative recall,” Appl. Opt. 26, 4985 (1987).
31. K. S. Fu, Syntactic Methods in Pattern Recognition (Academic Press, 1974; Mir, Moscow, 1977).
32. G. E. Pozdnyank, ed., “Integrated robots,” in Collection of articles translated from English, No. 1 (Mir, Moscow, 1973).
33. G. E. Pozdnyank, ed., “Integrated robots,” in Collection of articles translated from English, No. 2 (Mir, Moscow, 1975).
34. U. Grenander, Pattern Synthesis: Lectures in Pattern Recognition, Vol. 18 of Applied Mathematical Sciences (Springer-Verlag, New York, 1976).
35. U. Grenander, Pattern Analysis: Lectures in Pattern Theory Vol. 24 of Applied Mathematical Sciences (Springer-Verlag, New York, 1978).
36. N. R. Corby, J. L. Mundy, and P. A. Vrobel, “An environment for intelligence analysis,” in Proceedings of the 18th Image Understanding Workshop, Cambridge, Mass., April 6–8, 1988, pp. 342–350.
37. M. A. Fisher and R. C. Bolles, “Image understanding research at SRI International,” in Proceedings of the 18th Image Understanding Workshop, Cambridge, Mass., April 6–8, 1988, pp. 53–61.
38. D. T. Lawton, T. S. Levitt, and P. Gelband, “Knowledge-based vision for terrestrial robots,” in Proceedings of the 18th Image Understanding Workshop, Cambridge, Mass., April 6–8, 1988, pp. 103–110.
39. A. A. Stepanov, “Development of artificial intelligence in Western Europe and the USA,” Novosti Zarubezh. Nauki Tekhniki. Sis. Aviats. Vooruzh. No. 15, 26 (1990).
40. T. Matsuyama, “Expert systems for image processing: Knowledge-based composition of image-analysis processes,” Comput. Vis. Graph. Image Process. 48, 22 (1989).
41. J. Dijk, A. W. M. van Eekeren, O. R. Rojas, G. J. Burghouts, and K. Shutte, “Image processing in aerial surveillance and reconnaissance: From pixels to understanding,” Proc. SPIE 8897, 88970A (2013).
42. H. Bourma, R. J. Dekker, R. M. Shoemaker, and A. A. Mohamoud, “Segmentation and wake removal of seafaring vessels in optical satellite images,” Proc. SPIE 8897, 88970B (2013).
43. E. Beech, “Sight unseen,” Flight Int. 135, No. 4151, 34 (1989).
44. A. M. Bochkarev and S. I. Pochuev, “Expert systems—electronic flight consultants (the modern status of the problem, prospects),” Zarubezh. Radioélekt. No. 10, 42 (1989).
45. M. Ergul and A. A. Alatan, “An automatic geo-spatial object-recognition algorithm for high resolution satellite images,” Proc. SPIE 8897, 88970 (2013).
46. D. M. McKeown, H. A. Wilson, Jr., and L. Ewixon, “Automating knowledge acquisition for aerial image interpretation,” Comput. Vis. Graph. Image Process. 46, 37 (1989).
47. M. A. Fisher and T. M. Strax, “Recognizing objects in a natural environment: A contextual vision system (CVS),” in DARPA Image-Understanding Workshop, Palo Alto, Cal., May 23–26, 1989, p. 774.
48. J. M. De Catrel and J. R. Surdu, “Practical recognition of armored vehicles in FLIR,” Proc. SPIE 2485, 200 (1995).
49. B. Ponteau, “Vector-to-raster change detection for property assessment,” Earth Obs. Mag. 14, No. 1, 10 (2005).
50. A. K. Shackelford and C. H. Davis, “Automated processing of high-resolution satellite imagery for feature extraction and mapping of urban areas,” Earth Obs. Mag. 14, No. 1, 17 (2005).
51. A. S. Murillo, J. J. Guerrero, and C. Sagüés, “SURF features for efficient robot localization with omnidirectional images,” in IEEE International Conference on Robotics and Automation, Rome, Italy, April 2007, pp. 3901–3907.
52. W. Y. Jeong and K. M. Lee, “Visual SLAM with line and corner features,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, October 9–15, 2006, pp. 2570–2575.
53. A. Huertas, W. Cole, and R. Nevatia, “Detecting runways in aerial images,” in Proceedings of the Sixth International Conference on Artificial Intelligence (AAAI-87), Seattle, Washington, July 13–17, 1987, pp. 712–717.
54. R. Nevatia, C. Lin, and A. Huertas, “A system for building detection from aerial images,” in Automatic Extraction of Man-Made Objects from Aerial and Space Images (Birkhaser Verlag, Basel, 1997), pp. 77–86.
55. V. R. Lutsiv, D. S. Dolinov, A. K. Zherebko, and T. A. Novikova, “Using artificial neural networks in image-processing problems,” J. Opt. Technol. 64, 112 (1997).
56. V. R. Lutsiv, I. A. Malyshev, and V. A. Pepelka, “Automatic fusion of the multiple sensor and multiple season images,” Proc. SPIE 4380, 174 (2001).
57. V. R. Lutsiv, I. A. Malyshev, V. A. Pepelka, and A. S. Potapov, “The target-independent algorithms for description and structural matching of aerospace photographs,” Proc. SPIE 4741, 351 (2002).
58. V. R. Lutsiv, I. A. Malyshev, and A. S. Potapov, “Hierarchical structural matching algorithms for registration of aerospace images,” Proc. SPIE 5238, 164 (2003).
59. V. R. Lutsiv, N. N. Lapina, I. A. Malyshev, and A. S. Potapov, “Features of image comparison in problems of determining the location of a mobile robot,” Opt. Zh. 77, No. 11, 25 (2010) [J. Opt. Technol. 77, 677 (2010)].
60. V. Lutsiv, A. Potapov, T. Novikova, and N. Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).
61. V. R. Lutsiv, V. S. Andreev, A. F. Gubkin, A. S. Iljashenko, A. B. Kadykov, N. N. Lapina, I. A. Malyshev, T. A. Novikova, and A. S. Potapov, “Algorithms for automatically processing and analyzing aerospace pictures,” J. Opt. Technol. 74, 307 (2007).
62. V. R. Lutsiv, “Object-independent approach to the structural analysis of images,” Opt. Zh. 75, No. 11, 26 (2008) [J. Opt. Technol. 75, 708 (2008)].
63. V. Lutsiv and I. Malyshev, “Image structural analysis in the tasks of automatic navigation of unmanned vehicles and inspection of Earth surface,” Proc. SPIE 8897, 88970F (2013).

64. A. S. Potapov and O. S. Gamayunova, “Information criterion for constructing the hierarchical structural representations of images,” Proc. SPIE 5807, 443 (2005).
65. A. S. Potapov, “Comparative analysis of structural representations of images based on the principle of representational minimum description length,” J. Opt. Technol. 75, 715 (2008).
66. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, No. 2, 91 (2004).
67. H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May 7–13, 2006, pp. 404–417.
68. M. Rudinac, B. Lenseigne, and P. Jonker, “Keypoint extraction and selection for object recognition,” in Proceedings of the Conference on Machine Vision Applications (MVA2009IAPR), Yokohama, Japan, May 20–22, 2009, pp. 191–194.
69. J. Qui, T. Huang, Y. Huang, and T. Ikenaga, “A hardware accelerator with variable pixel representation & skip mode prediction for feature point detection. Part of SIFT algorithm,” in Proceedings of the Conference on Machine Vision Applications (MVA2009IAPR), Yokohama, Japan, May 20–22, 2009, pp. 239–242.
70. S. W. Choi, J. S. Yoon, V. Redkov, H. S. Son, S. I. Na, Y. G. Kim, S. S. Lee, S. M. Baek, A. Potapov, Y. J. Choi, D. H. Yi, J. H. Lee, Y. B. Kim, and V. Lutsiv, “Apparatus and method for detecting rotation-invariant features for localization of a mobile robot,” Korean Patent No. KR20110009547 (2011).
71. V. R. Lutsiv and I. A. Malyshev, “Automatic analysis and comparison of images in problems of navigation of unmanned craft and monitoring of the earth’s surface,” in Transactions of the Special All-Russia Scientific–Engineering Conference on the Modelling of Aviation Systems, April 12–14, 2011, Moscow, pp. 197–206.
72. A. Ascani, E. Frontoni, A. Mancini, and P. Zingaretti, “Feature group matching for appearance-based localization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, Sept. 22–26, 2008, pp. 3933–3938.
73. A. Casetti, E. Frontoni, A. Mancini, A. Ascani, P. Zingaretti, and S. Longhi, “A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications,” J. Intell. Robot. Syst. 61, 157 (2011).
74. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, 1988, pp. 147–151.
75. K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis. 60, No. 1, 63 (2004).
76. K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek, “Evaluating color descriptors for object and scene recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1582 (2010).
77. T. Lindeberg and J. Garding, “Shape-adapted smoothing in estimation of 3D shape cues from affine deformations of local 2D brightness structure,” Image Vis. Comput. 15, 415 (1997).
78. T. Kadir, A. Zisserman, and M. Brady, “An affine invariant salient region detector,” in Proceedings of the Eighth European Conference on Computer Vision, Prague, Czech Republic, 2004, pp. 404–416.
79. T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affine invariant regions,” Int. J. Comput. Vis. 59, No. 1, 61 (2004).
80. J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide-baseline stereo from maximally stable extremal regions,” in British Machine Vision Conference, 2002, pp. 384–393.
81. A. S. Potapov, I. A. Malyshev, A. E. Puysha, and A. N. Averkin, “New paradigm of learnable computer vision algorithms based on representational MDL principle,” Proc. SPIE 7696, 769606 (2010).
82. J. M. Morel and G. Yu, “ASIFT: A new framework for fully affine invariant image comparison,” SIAM J. Imaging Sci. 2, 438 (2009).
83. G. Yu and J. M. Morel, “A fully affine invariant image comparison method,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, 2009, pp. 1597–1600.
84. M. V. Peterson, “Clustering of a set of identified points on images of dynamic scenes, based on the principle of minimum description length,” J. Opt. Technol. 77, 701 (2010).
85. P. Viola and M. J. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the IEEE Computer Vision and Pattern Recognition Conference, Kauai, Hawaii, December 8–14, 2001, pp. I-501–I-518.
86. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, Cal., 2005, pp. 886–893.
87. Q. Zhu, S. Avidan, M. Yeh, and K. Cheng, “Fast human detection using a cascade of histograms of oriented gradients,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York City, 2006, vol. 2, pp. 1491–1498.
88. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part based models,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627 (2010).
89. http://www.image‑net.org.
90. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Neural Information Processing Systems, 2012, pp. 1106–1114.