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Projectively invariant description of non-planar smooth figures. 2. On recognition of ovaloids of revolution

© 2018 P. P. Nikolaev

Institute for Information Transmission Problems 127051 Moscow, B. Karetny per., 19, Russia

Received 04 May 2018

We consider the formulation of the problem of affine-invariant recognition of axisymmetric smoothly convex bodies obtained by rotating an oval, O, of explicit axial symmetry and registered in orthographic projection at different angles to the axis of rotation, A, of the body in the scene. Using model numerical examples for six ovals-generators O (having one, two, and three axes), the results of detecting the a axis are shown. The detection is based on the developed procedure Rot, which gives an estimate of the position of a on the sensory projection of the body. In the space of the scene, the occluding contour, c, of the body, in its general form, is a nonplanar oval curve considered as the body boundary on the planar optical projection. From the detected input projection with the axis a, whose position is calculated on the basis of the Rot algorithm, four parameters being affine invariants of the body for a given viewing angle (the angle between the optical axis of the camera and the axis A) are estimated. The descriptor (a special curve in the 4D parameter space) is interpolated by a sequence of 11 viewing angles in the reference stage for each body (the set of their forms is defined), which allows us to identify the body for an arbitrary viewing angle (using a single projection). The peculiarities of the Rot scheme, versions of its implementation, and the idea of constructing a more complex descriptor suitable for identifying the body in the projective way of registration (a camera obscura model) are also discussed.

Key words: contour of ovaloid of revolution, affine invariant, body registration angle, symmetry plane, symmetry axis, affine and central plane projections, descriptor

DOI: 10.1134/S023500921804008X

Cite: Nikolaev P. P. Proektivno invariantnoe opisanie neploskikh gladkikh figur. 2. o racpoznavanii ovaloidov vrashcheniya [Projectively invariant description of non-planar smooth figures. 2. on recognition of ovaloids of revolution]. Sensornye sistemy [Sensory systems]. 2018. V. 32(4). P. 342-355 (in Russian). doi: 10.1134/S023500921804008X

References:

  • Blyaschke V. Krug i shar [Circle and ball]. Moscow. Nauka Publ. 1967. 232 p. (in Russian)
  • Do Carmo M.P. Differential Geometry of Curves and Surfaces. Prentice Hall. 1976.
  • Kartesi F. Vvedenie v konechnye geometrii [Introduction to finite geometries]. Moscow. Nauka Publ. 1980. 320 p. (in Russian)
  • Nikolaev P.P. Recognition of projectively transformed planar figures. IV. Methods for forming a projective-invariant description of axisymmetric ovals. Sensornye sistemy [Sensory systems]. 2012. 26 (4). P. 280–303. (in Russian).
  • Nikolaev P.P. A method for projectively-invariant description of ovals having axial or central symmetry. Informacionnye tekhnologii i vychislitel’nye sistemy [Information technologies and computer systems]. 2014. № 2. P. 46–59. (in Russian).
  • Nikolaev P.P. Recognition of projectively transformed planar figures. VIII. On computation of the ensemble of correspondence for “rotationally symmetric” ovals. Sensornye sistemy [Sensory systems]. 2015. 29 (1). P. 28–55. (in Russian).
  • Nikolaev P.P. Projectively invariant description of non-planar smooth figures. 1. Preliminary analysis of the problem. Sensornye sistemy [Sensory systems]. 2016. 30 (4). P. 290–301. (in Russian).
  • Finikov S. P. Proektivno-differencial’naya geometriya [Projective-differential geometry]. Moscow. KomKniga, 2010. 264 p. (in Russian). Forsyth D.A., Ponce J. Computer Vision: A Modern Approach. Prentice Hall, 2002.
  • Forsyth D.A., Ponce J. Computer Vision: A Modern Approach. Prentice Hall, 2002.
  • Hejfec A.L., Loginovskij A.N. Parametrizaciya kak sredstvo resheniya zadach 3D komp’yuternogo geometricheskogo modelirovaniya [Parameterization as a tool for solving problems of 3D computer geometric modeling]. Trudy konferencii “Informacionnye sredstva i tekhnologii” [Proc. Conf. “Information tools and technologies”]. 2012. V. 1. P. 72–80 (in Russian).
  • Chebykin V. Ocenka obtekaemosti ovaloidov i ovaloidopodobnyh tel vrashcheniya [Estimation of aerodynamics of ovaloids and ovaloid-like bodies of rotation]. CAPR I grafika [CAD and graphics]. № 7. P. 76–77 (in Russian).
  • Alt H., Godau M. Computing the Frechet distance between two polygonal curves. Int. J. Computat. Geom. Applicat. 1995. V. 5. I. 1–2. P. 75–91.
  • Beygelzimer A., Kakade S., Langford J. Cover trees for nearest neighbor. Proc. Int. Conf. Machine Learning. 2006. P. 97–104.
  • Boutin M. Polygon recognition and symmetry detection. Found. Comput. Math. 2003. V. 3. P. 227–271. DOI: 10.1007/s10208-001-0027-5.
  • Cox D., Katz Sh. Mirror symmetry and algebraic geometry. Amer. Math. Soc. 1999. 464 p. DOI: 10.1090/surv/068.
  • Gross M. Mirror symmetry for P2 and tropical geometry. Adv. Math. 2010. 224 (1). P. 169–245.
  • Guttman A. R-trees: A dynamic index structure for spatial searching. Proc. ACM SIGMOD Int. Conf. Management data. 1984. 14 (2). P. 47–57.
  • Koenderink J. What does the occluding contour tell us about solid shape? Perception. 1984. 13 (3). P. 321–330.
  • Horn B.K.P. Obtaining shape from shading information. Winston P.H. (Ed.). Psychology of Computer Vision. McGraw-Hill. New York. 1975. P. 115–155.
  • Marr D., Poggio T. A computational theory of human stereo vision. Proc. R. Soc. Biological Sciences. 1979. V. 204 (1156). P. 321–328.
  • Morrison D. Mirror symmetry and Rational Curves on Quintic Threefolds: A Guide for Mathematicians. J. Amer. Math. Soc. 1993. 6 (1). P. 223–247.
  • Muja M., Lowe D.G. Scalable Nearest Neighbor Algorithms for High Dimensional Data. IEEE Transactions Pattern Analysis Machine Intelligence. 2014. 36 (11). P. 2227–2240. DOI: 10.1109/TPAMI.2014.2321376.
  • Olver P.J. Geometric foundations of numerical algorithms and symmetry. Appl. Alg. Engin. Comp. Commun. 2001. 11 (5). P. 417–436.
  • Stark U., Mueller A. Effektive Methoden zur Messung der Korngroesse und Kornform. Aufbereitungs Technik. 2004. 45 (6). P. 32–37.
  • Ullman S. The interpretation of structure from motion. Proc. R. Soc. Biological Sciences. 1979. 203 (1153). P. 405–426.
  • Yianilos P.N. Data structures and algorithms for nearest neighbor search in general metric spaces. Proc. ACMSIAM symp. discrete algorithms. 1993. P. 311–321.