Skip to main content
SpringerLink
Log in

Analysis of Objects Classification Approaches Using Vectors of Inflection Points

  • Conference paper
  • First Online:
Book cover Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1020))

Abstract

An increase of the automated video surveillance system operational performance was proposed by reducing the amount of data using the contour inflection points for object identification. The authors developed a structural statistic method to identify facial images. It allowed obtaining a set of vectors that consist of inflection points of every hierarchical layer of the processed object. The research described in this paper shows the comparison of such vectors identifying similarities and referring them to a particular class. Three objects classification approaches were proposed and analyzed: approaches based on centroid, sum and maximum. Two alternative schemes for the calculation of distance between inflection point vectors were also proposed and analyzed: the classical Euclidean meter and the modified one, based on distances from the coordinate origins.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Fleck S, Strasser W (2008) Smart camera based monitoring system and its application to assisted living. Proc IEEE 96(10):1698–1714

    Article  Google Scholar 

  2. Patrick R, Bourbakis N (2009) Surveillance systems for smart homes: a comparative survey. In: 21st IEEE international conference on tools with artificial intelligence, pp 248–252

    Google Scholar 

  3. Brezovan M, Badica C (2013) A review on vision surveillance techniques in smart home environments. In: Proceedings of the 19th international conference on control systems and computer science, pp 471–478

    Google Scholar 

  4. Kale PV, Sharma SD (2014) Review of securing home using video surveillance. Int J Sci Res (IJSR) 3(5):1150–1154

    Google Scholar 

  5. Caputo AC (2014) Digital video surveillance and security, 2nd edn. Butterworth-Heinemann, Oxford, 440 p

    Chapter  Google Scholar 

  6. Schimid C, Mohr R, Bauckhane C (2000) Evaluation of interest point detectors. Int J Comput Vis 37:151–172 2nd edn

    Article  Google Scholar 

  7. Rodehorst V, Koschan A (2006) Comparison and evaluation of feature point detectors. In: Proceedings of 5th International Symposium Turkish-German Joint Geodetic Days “Geodesy and Geoinformation in the Service of our Daily Life”. Technical University of Berlin, Germany, p 8, March 2006

    Google Scholar 

  8. Nain N, Laxmi V, Bhadviya B, Deepak BM, Mushtaq A (2008) Fast feature point detector. In: IEEE international conference on signal image technology and internet based systems, pp 301–306

    Google Scholar 

  9. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends Comput Graph Vis 3(3):177–280

    Article  Google Scholar 

  10. Dornaika F, Chakik F (2010) Efficient object detection and matching using feature classification. In: 20th international conference on pattern recognition, Istanbul, Turkey, 23–26 August, pp 3073–3076

    Google Scholar 

  11. Jiang D, Yiy J (2012) Comparison and study of classic feature point detection algorithm. In: Proceedings of the international conference on computer science and service system, pp 2307–2309

    Google Scholar 

  12. Kurt Z, Özkan K (2013) Description of contour with meaningful points. In: 21st signal processing and communications applications conference (SIU), Haspolat, Turkey, 24–26 April, pp 1–4

    Google Scholar 

  13. Liang J, Zhang Y, Maybank S, Zhang X (2014) Salient feature point detection for image matching. In: IEEE China summit & international conference on signal and information processing (ChinaSIP), pp 485–489

    Google Scholar 

  14. Liang C-W, Juang C-F (2015) Moving object classification using a combination of static appearance features and spatial and temporal entropy values of optical flows. IEEE Trans Intell Trans Syst 16(6):3453–3464

    Article  Google Scholar 

  15. Zhu R, Dornaika F, Ruichek Y (2018) Flexible and discriminative non-linear embedding with feature selection for image classification. In: 24th international conference on pattern recognition (ICPR), Beijing, China, 20–24 August, pp 3192–3197

    Google Scholar 

  16. Tarabalka Y, Tilton JC (2012) Improved hierarchical optimization-based classification of hyperspectral images using shape analysis. In: IEEE international geoscience and remote sensing symposium, pp 1409–1412

    Google Scholar 

  17. Brik Y, Zerrouki N, Bouchaffra D (2013) Combining pixel- and object-based approaches for multispectral image classification using Dempster-Shafer theory. In: International conference on signal-image technology & internet-based systems, Kyoto, Japan, 2–5 December, pp 448–453

    Google Scholar 

  18. Kim J, Kim D (2014) Static region classification using hierarchical finite state machine. In: IEEE international conference on image processing (ICIP), Paris, France, 27–30 October, pp 2358–2362

    Google Scholar 

  19. Banerjee P, Patel A, Das S, Seraogi B, Roy R, Majumder H et al (2018) A robust system for visual pattern recognition in engineering drawing documents. In: TENCON 2018 - 2018 IEEE region 10 conference, Jeju, Korea, 28–31 October, pp 2050–2055

    Google Scholar 

  20. Paliy I, Dovgan V, Boumbarov O, Panev S, Sachenko A, Kurylyak Y, Zagorodnya D (2011) Fast and robust face detection and tracking framework. In: Proceedings of the 6th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS 2011), vol 1, Prague, Czech Republic, 15–17 September. IEEE, pp 430–434

    Google Scholar 

  21. Zahorodnia D, Pigovsky Y, Bykovyy P, Krylov V, Paliy I, Dobrotvor I (2015) Structural statistic method identifying facial images by contour inflection points. In: Proceedings of the 8th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS 2015), vol 1, Warsaw, Poland, 24–26 September, pp 293–297

    Google Scholar 

  22. Zahorodnia D, Pigovsky Y, Bykovyy P, Krylov V, Rusyn B, Koval V (2017) Criteria to estimate quality of methods selecting contour inflection points. In: Proceedings of the 9th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS 2017), vol 2, Bucharest, Romania, 21–23 September. IEEE, pp 969–973

    Google Scholar 

  23. Bykovyy P, Kochan V, Sachenko A, Markowsky G (2007) Genetic algorithm implementation for perimeter security systems CAD. In: 4th IEEE workshop on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS 2007), Dortmund, Germany, 06–08 September, pp 634–638

    Google Scholar 

  24. Zahorodnia D, Pigovsky Y, Bykovyy P, Krylov V, Sachenko A (2018) Information technology for structural and statistical identification of hierarchical objects. In: Proceedings of the 14th international conference on advanced trends in radioelecrtronics, telecommunications and computer engineering (TCSET), Lviv-Slavske, Ukraine, 20–24 February, pp 272–275

    Google Scholar 

  25. Zahorodnia D, Pigovsky Y, Bykovyy P, Krylov V, Sachenko A, Molga A (2018) Automated video surveillance system based on hierarchical object identification. In: 14th international conference on development and application systems (DAS), Suceava, Romania, 24–26 May, pp 194–199

    Google Scholar 

  26. Starck J-L, Murtagh FD, Bijaoui A (1998) Image processing and data analysis: the multiscale approach. Cambridge University Press, Cambridge, 287 p

    Google Scholar 

  27. Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edn. Pearson, London, 976 p

    Google Scholar 

  28. Pratt WK (2013) Introduction to digital image processing. CRC Press, Boca Raton, 750 p

    Book  Google Scholar 

  29. Image database: AT&T Laboratories Cambridge. http://www.cl.cam.ac.uk/research/dtg/attarchive/facesataglance.html

  30. Mansfield AJ, Wayman JL (2002) Best practices in testing and reporting performance of biometric devices. NPL Report CMSC 14/02, 32 p, August 2002

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ternopil National Economic University, Ternopil, Ukraine

    Diana Zahorodnia, Pavlo Bykovyy, Ivan Kit, Andriy Kaniovskyi & Mykola Dacko

  2. Research Institute for Intelligent Computer Systems, Ternopil, Ukraine

    Diana Zahorodnia, Pavlo Bykovyy, Anatoliy Sachenko, Viktor Krylov, Ivan Kit, Andriy Kaniovskyi & Mykola Dacko

  3. Department of Computer Science, Kazimierz Pulaski University of Technology and Humanities, Radom, Poland

    Anatoliy Sachenko

  4. Odessa National Polytechnic University, Odessa, Ukraine

    Viktor Krylov & Galina Shcherbakova

Authors
  1. Diana Zahorodnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pavlo Bykovyy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anatoliy Sachenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Viktor Krylov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Galina Shcherbakova
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ivan Kit
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andriy Kaniovskyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mykola Dacko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pavlo Bykovyy .

Editor information

Editors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

  2. Jan Evangelista Purkyně University in Ústi and Labem, Usti and Labem, Czech Republic

    Sergii Babichev

  3. Lublin University of Technology, Lublin, Poland

    Waldemar Wójcik

  4. IT Step University, Lviv, Ukraine

    Olena Vynokurova

  5. Kherson National Technical University, Kherson, Ukraine

    Svetlana Vyshemyrskaya

  6. Kherson National Technical University, Kherson, Ukraine

    Svetlana Radetskaya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zahorodnia, D. et al. (2020). Analysis of Objects Classification Approaches Using Vectors of Inflection Points. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_11

Download citation

Publish with us

Policies and ethics

Search

Navigation