Abstract

This paper describes algorithms for correlating images of arbitrary three-dimensional scenes by clusterizing correlated key points, using the Hough transform. The method is based on the well-known method of detecting objects, but an alternative approach is proposed for verifying clusters of correlated key points. Experimental results are given for different types of key points, confirming that the proposed method has a significant advantage over the use of the fundamental matrix.

© 2014 Optical Society of America

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  1. A.  Loui, M.  Das, “Matching of complex scenes based on constrained clustering,” in AAAI Fall Symposium: Multimedia Information Extraction, vol. FS-08-05, (2008), pp. 28–30.
  2. V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).
  3. 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).
  4. A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).
  5. D. H.  Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111 (1981).
  6. D. G.  Lowe, “Object recognition from local scale-invariant features,” in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, Kerkyra, Greece, September20–27, 1999, pp. 1150–1157.
  7. H.  Bay, T.  Tuytelaars, L.  Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May7–13, 2006, pp. 404–417.
  8. D.  Lowe, “Local feature view clustering for 3D object recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, December2001, pp. 682–688.
  9. R.  Raguram, J. M.  Frahm, M.  Pollefeys, “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus,” in Proceedings of the European Conference on Computer Vision, Marseille, France, October12–18, 2008, pp. 500–513.
  10. ERSP 3.1. Robotic Development Platform, http://www.mobile-vision-technologies.eu/archiv/download/MVT_ersp.pdf .
  11. S.  Leutenegger, M.  Chli, R.  Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” in Proceedings of the International Conference on Computer Vision, Barcelona, Spain, November8–11, 2011, pp. 2548–2555.

2010

A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).

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).

2005

V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).

1981

D. H.  Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111 (1981).

Averkin, A. N.

A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).

Ballard, D. H.

D. H.  Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111 (1981).

Bay, H.

H.  Bay, T.  Tuytelaars, L.  Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May7–13, 2006, pp. 404–417.

Chli, M.

S.  Leutenegger, M.  Chli, R.  Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” in Proceedings of the International Conference on Computer Vision, Barcelona, Spain, November8–11, 2011, pp. 2548–2555.

Das, M.

A.  Loui, M.  Das, “Matching of complex scenes based on constrained clustering,” in AAAI Fall Symposium: Multimedia Information Extraction, vol. FS-08-05, (2008), pp. 28–30.

Frahm, J. M.

R.  Raguram, J. M.  Frahm, M.  Pollefeys, “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus,” in Proceedings of the European Conference on Computer Vision, Marseille, France, October12–18, 2008, pp. 500–513.

Lapina, N.

V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).

Leutenegger, S.

S.  Leutenegger, M.  Chli, R.  Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” in Proceedings of the International Conference on Computer Vision, Barcelona, Spain, November8–11, 2011, pp. 2548–2555.

Loui, A.

A.  Loui, M.  Das, “Matching of complex scenes based on constrained clustering,” in AAAI Fall Symposium: Multimedia Information Extraction, vol. FS-08-05, (2008), pp. 28–30.

Lowe, D.

D.  Lowe, “Local feature view clustering for 3D object recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, December2001, pp. 682–688.

Lowe, D. G.

D. G.  Lowe, “Object recognition from local scale-invariant features,” in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, Kerkyra, Greece, September20–27, 1999, pp. 1150–1157.

Lutsiv, V.

V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).

Malyshev, I. A.

A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).

Novikova, T.

V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).

Peterson, M. V.

Pollefeys, M.

R.  Raguram, J. M.  Frahm, M.  Pollefeys, “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus,” in Proceedings of the European Conference on Computer Vision, Marseille, France, October12–18, 2008, pp. 500–513.

Potapov, A.

V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).

Potapov, A. S.

A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).

Puysha, A. E.

A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).

Raguram, R.

R.  Raguram, J. M.  Frahm, M.  Pollefeys, “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus,” in Proceedings of the European Conference on Computer Vision, Marseille, France, October12–18, 2008, pp. 500–513.

Siegwart, R.

S.  Leutenegger, M.  Chli, R.  Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” in Proceedings of the International Conference on Computer Vision, Barcelona, Spain, November8–11, 2011, pp. 2548–2555.

Tuytelaars, T.

H.  Bay, T.  Tuytelaars, L.  Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May7–13, 2006, pp. 404–417.

Van Gool, L.

H.  Bay, T.  Tuytelaars, L.  Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May7–13, 2006, pp. 404–417.

J. Opt. Technol.

Pattern Recogn.

D. H.  Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111 (1981).

Proc. SPIE

A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).

V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).

Other

D. G.  Lowe, “Object recognition from local scale-invariant features,” in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, Kerkyra, Greece, September20–27, 1999, pp. 1150–1157.

H.  Bay, T.  Tuytelaars, L.  Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May7–13, 2006, pp. 404–417.

D.  Lowe, “Local feature view clustering for 3D object recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, December2001, pp. 682–688.

R.  Raguram, J. M.  Frahm, M.  Pollefeys, “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus,” in Proceedings of the European Conference on Computer Vision, Marseille, France, October12–18, 2008, pp. 500–513.

ERSP 3.1. Robotic Development Platform, http://www.mobile-vision-technologies.eu/archiv/download/MVT_ersp.pdf .

S.  Leutenegger, M.  Chli, R.  Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” in Proceedings of the International Conference on Computer Vision, Barcelona, Spain, November8–11, 2011, pp. 2548–2555.

A.  Loui, M.  Das, “Matching of complex scenes based on constrained clustering,” in AAAI Fall Symposium: Multimedia Information Extraction, vol. FS-08-05, (2008), pp. 28–30.

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