Skip to main content
SpringerLink
Log in

Shape Matching for Rigid Objects by Aligning Sequences Based on Boundary Change Points

  • Conference paper
  • First Online:
Combinatorial Image Analysis (IWCIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10256))

Included in the following conference series:

Abstract

This paper presents a new boundary (shape) matching algorithm for 2D rigid objects without voids. Our new algorithm presents a new shape representation that uses the outcome from an active contour (AC) model. An object’s shape is partitioned into a clockwise ordered sequence of edges, where every edge is a boundary segment enclosed by reference points. These points are convex hull vertices which lie on boundary corners. Further, the reference points are used to generate angles. Hence, a boundary shape maps to a sequence of angles, turning the shape matching problem to alignment of cyclic sequences of angles. The latter makes our method scaling and rotational invariant. Experiments validate the theoretical concept, and provide qualitative comparison with other methods in the field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anandan, J., Fry, E., Monschke, Arslan, A.N.: A fast algorithm for finding largest common substructures in multiple RNAs. In: Proceeedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB’07, Honolulu, HI, USA, 20–22 March, pp. 51–57 (2017)

    Google Scholar 

  2. Antani, S., Xu, X., Long, L.R., Thoma, G.R.: Partial shape matching for CBIR of spine X-ray images. In: SPIE Proceedings of Storage and Retrieval Methods and Applications for Multimedia, vol. 5307, pp. 1–8 (2004)

    Google Scholar 

  3. Arslan, A.N., Sirakov, N.M., Attardo, S.: Weapon ontology annotation using boundary describing sequences. In: Proceedings of IEEE SSIAI 2012, pp. 101–104, Santa Fe, 22–24 April 2012

    Google Scholar 

  4. Arslan, A.N., Hempelmann, C.F., Attardo, S., Blount, G.P., Sirakova, N.N., Sirakov, N.M.: Identification of partially occluded firearms through partonomy. In: Sadjadi, M. (ed.) Proceedings of SPIE 2015 (2015). doi:10.1117/12.2184102

  5. Arslan, A.N., Hempelmann, C.F., Attardo, S., Blount, G.P., Sirakov, N.M.: Threat assessment using visual hierarchy and conceptual firearms ontology. Opt. Eng. 54(5), 053109 (2015). doi:10.1117/1.OE.54.5.053109

    Article  Google Scholar 

  6. Arslan, A.N., Anandan, J., Fry, E., Pandey, R., Monschke, K.: A new structure representation for RNA and fast RNA substructure search. In: Proceedings of CSCI 2016, Las Vegas, USA, 14–17 December, pp. 1226–1231. IEEE CPS (2016). doi:10.1109/CSCI.2016.230

  7. Aslan, C., Tari, S.: An axis based representation for recognition. In: Proceedings of ICCV, pp. 1339–1346 (2005)

    Google Scholar 

  8. Bai, X.Y., Yui, D., Latecki, L.J.: Skeleton-based classification using path similarity. Int. J. Pattern Recogn. Artif. Intell. 22(4), 733–746 (2008)

    Article  Google Scholar 

  9. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. Trans. PAMI 24, 209–222 (2002)

    Article  Google Scholar 

  10. Bronstein, A.M., Bronstein, M.M., Kimmel, R., Mahmoudi, M., Sapiro, G.: A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching. Int. J. Comput. Vis. 89(2), 266–286 (2010)

    Article  Google Scholar 

  11. Chekmarev, D.S., Kholodovych, V., Balakin, K.V., Ivanenkov, Y., Ekins, S., Welsh, W.J.: Shape signatures: new descriptors for predicting cardiotoxicity in silico. Chem. Res. Toxicol. 21, 1304–1314 (2008)

    Article  Google Scholar 

  12. Chen, L., Feris, R.S., Turk, M.: Efficient partial shape matching using the Smith-Waterman algorithm. In: Proceedings of CVPR Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, Anchorage, Alaska, June 2008. doi:10.1109/CVPRW.2008.4563078

  13. Cour, T., Shi, J.: Recognizing objects by piecing together the segmentation puzzle. In: Proceedings of CVPR (2008). doi:10.1109/CVPR.2007.383051

  14. Badawy, O., Kamel, M.: Matching concavity trees. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR/SPR 2004. LNCS, vol. 3138, pp. 556–564. Springer, Heidelberg (2004). doi:10.1007/978-3-540-27868-9_60

    Chapter  Google Scholar 

  15. Felzenszwalb, P., Schwartz, J.: Hierarchical matching of deformable shapes. In: Proceedings of CVPR, pp. 1–8. IEEE (2007)

    Google Scholar 

  16. Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000). doi:10.1007/3-540-45053-X_3

    Chapter  Google Scholar 

  17. Gonzalez, T.F.: Clustering to minimize the maximum intercluster distance. Theoret. Comput. Sci. 38(2–3), 293–306 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  18. Grega, M., Matiolański, A., Guzik, P., Leszczuk, M.: Automated detection of firearms and knives in a CCTV image. Sensors 16(1), 47 (2016). doi:10.3390/s16010047

    Article  Google Scholar 

  19. Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the Hausdorff distance. Trans. PAMI 15(9), 850–863 (1993)

    Article  Google Scholar 

  20. Huang, R., Pavlovic, V., Metaxas, D.N.: A profile Hidden Markov Model framework for modeling and analysis of shape. In: Proceedings of International Conference on Image Processing, Atlanta, GA. IEEE, October 2006. doi:10.1109/ICIP.2006.312827

  21. Kim, H.-S., Chang, H.-W., Liu, H., Lee, J., Lee, D.: BIM: Image matching using biological gene sequence alignment. In: Proceedings of International Conference on Image Processing, pp. 205–108. IEEE ICIP (2009)

    Google Scholar 

  22. Kortagere, S., Chekmarev, D., Welsh, W.J., Ekins, S.: Hybrid scoring and shape based classification approaches for human pregnane X receptor. Pharm. Res. 26(4), 1001–1011 (2009)

    Article  Google Scholar 

  23. Kortagere, S., Krasowski, M.D., Sean Ekins, S.: The importance of discerning shape in molecular pharmacology. Trends Pharmacol. Sci. 30(3), 138–147 (2009)

    Article  Google Scholar 

  24. Korotkov, K.: Automatic change detection in multiple skin lesions. Ph.D. thesis, Universitat de Girona (2014)

    Google Scholar 

  25. Ling, H., Jacobs, D.: Using the Inner-Distance for classification of articulated shapes. In: Proceedings of Conference on Computer Vision and Pattern Recognition, vol. II, pp. 719–726. IEEE CVPR (2005)

    Google Scholar 

  26. Mori, G., Malik, J.: Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. In: Proccedings of Computer Vision and Pattern Recognition, pp. 134–141. IEEE CVPR (2003)

    Google Scholar 

  27. Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–53 (1970)

    Article  Google Scholar 

  28. Opelt, A., Pinz, A., Zisserman, A.: A boundary-fragment-model for object detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006). doi:10.1007/11744047_44

    Chapter  Google Scholar 

  29. Shotton, J., Blake, A., Cipolla, R.: Contour-based learning for object detection. In: Proceedings of ICCV (2005). doi:10.1109/ICCV.2005.63

  30. Sirakov, N.M.: A new active convex hull model for image regions. J. Math. Imaging Vis. 26(3), 309–325 (2006)

    Article  MathSciNet  Google Scholar 

  31. Sirakov, N.M., Simonelli, I.: A new automatic concavity extraction model. In: Proceedings of SSIAI, pp. 178–182 (2006)

    Google Scholar 

  32. Sirakov, N.M., Ushkala, K.: An integral active contour model for convex hull and boundary extraction. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 1031–1040. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10520-3_99

    Chapter  Google Scholar 

  33. Sklansky, J.: Measuring concavity on a rectangular mosaic. IEEE Trans. Comput. C-21, 1355–1364 (1972)

    Google Scholar 

  34. Sun, K.B., Super, B.J.: Classification of contour shapes using class segment sets. In: Proceedings of CVPR, pp. 727–733 (2005). doi:10.1109/CVPR.2005.98

  35. Thayananthan, A., Stenger, B., Torr, P., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: Proceedings of CVPR, pp. 127–134 (2003). doi:10.1109/CVPR.2003.1211346

  36. Wang, H., Oliensis, J.: Rigid shape matching by segmentation averaging. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 619–635 (2010). doi:10.1109/TPAMI.2009.199

    Article  Google Scholar 

  37. Xu, X., Lee, D.-J., Antani, S., Long, L.R.: A spine x-ray image retrieval system using partial shape matching. IEEE Trans. Inf Technol. Biomed. 12(1), 100–108 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

We are thankful to reviewers for their insightful and constructive comments. Addressing them yielded valuable additions to the present work. This work is partially supported by USA NSF Award No: IIS-1528027.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, Texas A&M University – Commerce, Commerce, TX, 75428, USA

    Abdullah N. Arslan & Nikolay M. Sirakov

  2. Department of Mathematics, Texas A&M University – Commerce, Commerce, TX, 75428, USA

    Nikolay M. Sirakov

Authors
  1. Abdullah N. Arslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikolay M. Sirakov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdullah N. Arslan .

Editor information

Editors and Affiliations

  1. SUNY Buffalo State, Buffalo, New York, USA

    Valentin E. Brimkov

  2. Department of Applied Professional Studies, SUNY Fredonia, Fredonia, New York, USA

    Reneta P. Barneva

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Arslan, A.N., Sirakov, N.M. (2017). Shape Matching for Rigid Objects by Aligning Sequences Based on Boundary Change Points. In: Brimkov, V., Barneva, R. (eds) Combinatorial Image Analysis. IWCIA 2017. Lecture Notes in Computer Science(), vol 10256. Springer, Cham. https://doi.org/10.1007/978-3-319-59108-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59108-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59107-0

  • Online ISBN: 978-3-319-59108-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation