Skip to main content
SpringerLink
Log in
Book cover

Computer Vision in Control Systems-2 pp 1–10Cite as

Practical Matters in Computer Vision

  • Chapter
  • First Online:

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 75))

Abstract

A brief description of researches close to implementation in technical systems is represented in this chapter. Human action recognition and audience analysis systems as well as smart software tool for panorama construction help for a well-being of a human. The application of novel methods in robot navigation systems and the perception of audio visual information for mobile robots are the issues of other innovative investigations. The adaptive comprehensive surveillance algorithms for situation analysis, the enhanced, synthetic, and combined vision technologies for civil aviation, and the navigation techniques reflect the recent achievements in machine vision for robotics and autonomous vehicles. Also the efficient denoising algorithms and the image segmentation based on 2D Markov chains are useful in intelligent recognition systems.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput. Surv. 43(3):16:1–16:43

    Google Scholar 

  2. Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: European conference on computer vision (ECCV 2006), pp 428–441

    Google Scholar 

  3. Amraji N, Mu L, Milanova M (2011) Shape–based human actions recognition in videos. In: 14th international conference on human-computer interaction: design and development approaches, vol. 1, pp 539–546

    Google Scholar 

  4. Li SZ, Jain AK (2005) Handbook of face recognition. Springer, Berlin

    MATH  Google Scholar 

  5. Makinen E, Raisamo R (2008) An experimental comparison of gender classification methods. Pattern Recogn Lett 29(10):1544–1556

    Article  Google Scholar 

  6. Fu Y, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32(11):1955–1976

    Article  Google Scholar 

  7. Khryashchev V, Ganin A, Golubev M, Shmaglit L (2013) Audience analysis system on the basis of face detection, tracking and classification techniques. In: International multi-conference of engineers and computer scientists (IMECS 2013) 1:446–450

    Google Scholar 

  8. Haenselmann T, Busse M, Kopf S, King T, Effelsberg W (2009) Multi perspective panoramic imaging. Image Vis Comput 27(4):391–401

    Article  Google Scholar 

  9. Kwon OS, Ha YH (2010) Panoramic video using Scale Invariant Feature Transform with embedded color-Invariant values. IEEE Trans Consum Electron 56(2):792–798

    Article  Google Scholar 

  10. Favorskaya M, Pakhirka A (2012) A way for color image enhancement under complex luminance conditions. In: Watanabe T, Watada J, Takahashi N, Howlett RJ, Jain LC (eds) Intelligent interactive multimedia: systems and services. Springer, Berlin

    Google Scholar 

  11. Zhao G, Lin L, Tang Y (2013) A new optimal seam finding method based on tensor analysis for automatic panorama construction. Pattern Recogn Lett 34(3):308–314

    Article  Google Scholar 

  12. Bonin-Font F, Ortiz A, Oliver G (2008) Visual navigation for mobile robots: a survey. J Intell Robot Syst 53(1):263–296

    Article  Google Scholar 

  13. Kiy KI, Dickmanns ED (2004) A color vision system for analysis of road scenes. In: IEEE intelligent vehicle’04 symposium, pp 54–59

    Google Scholar 

  14. Kiy KI (2010) A new real-time method for description and segmentation of color images. Pattern Recogn Image Anal Adv Math Theory Appl 20(2): 169–176

    Google Scholar 

  15. Jarvis R (2008) Intelligent robotics: past, present and future. Int J Comput Sci Appl Technomathematics Res Found 5(3):23–35

    Google Scholar 

  16. Bekiarski Al, Pleshkova Sn (2009) Microphone array beamforming for mobile robot. In: 8th WSEAS international conference on circuits, systems, electronics, control and signal processing (CSECS’2009), pp 146–149

    Google Scholar 

  17. Dehkharghani SSh, Bekiarski Al, Pleshkova Sn (2012) Application of probabilistic methods in mobile robots audio visual motion control combined with laser range finder distance measurements. In: Biolek D, Volkov K, Ng KM (eds) Advances in circuits, systems, automation and mechanics. WSEAS Press, Greece

    Google Scholar 

  18. Tulum K, Durak U, Yder SK (2009) Situation aware UAV mission route planning. In: IEEE aerospace conference, pp 1–12

    Google Scholar 

  19. Osipov GS, Smirnov IV, Tikhomirov IA (2012) Formal methods of situational analysis: experience from their use. Autom Doc Math Linguist 46(5):183–194

    Google Scholar 

  20. Leishman RC, McLain TW, Beard RW (2014) Relative navigation approach for vision-based aerial GPS-denied navigation. J Intell Rob Syst 74(1–2):97–111

    Article  Google Scholar 

  21. Bailey RE (2012) Awareness and detection of traffic and obstacles using synthetic and enhanced vision systems. NASA technical memorandum, 2012-217324 NASA, pp 54–60

    Google Scholar 

  22. Kumar SV, Kashyap SK, Kumar NS (2014) Detection of runway and obstacles using electro-optical and infrared sensors before landing. Defense Sci J 64(1):67–76

    Article  Google Scholar 

  23. Vizilter Yu, Zheltov SY (2012) Geometrical correlation and matching of 2D image shapes. ISPRS Ann Photogrammetry Remote Sens Spat Inf Sci 1–3:191–196

    Article  Google Scholar 

  24. Sangekar M, Thornton B, Ura T (2012) Wide area seafloor observation using an autonomous landing vehicle with adaptive resolution capability. Oceans 2012:1–9

    Google Scholar 

  25. Burdinsky IN (2012) Guidance algorithm for an autonomous unmanned underwater vehicle to a given target. Optoelectron Instrum Data Process 48(1):69–74

    Article  Google Scholar 

  26. Bezruchko F, Burdinky I, Myagotin A (2011) Global extremum searching algorithm for the AUV guidance toward an acoustic buoy. IEEE OCEANS’2011, pp 1–7

    Google Scholar 

  27. Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4:490–530

    Article  MathSciNet  MATH  Google Scholar 

  28. Katkovnik V, Foi A, Egiazarian K, Astola J (2010) From local kernel to nonlocal multiple-model image denoising. Int J Comput Vision 86(8):1–32

    Article  MathSciNet  Google Scholar 

  29. Priorov A, Tumanov K, Volokhov V, Sergeev E, Mochalov I (2013) Applications of image filtration based on principal component analysis and nonlocal image processing. IAENG Int J Comput Sci 40(2):62–80

    Google Scholar 

  30. Martin D, Fowlkes C, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530–549

    Article  Google Scholar 

  31. Wang H, Dong Y (2008) an improved image segmentation algorithm based on otsu method. In: International symposium on photoelectronic detection and imaging: related technologies and applications, SPIE 6625, pp 1–8

    Google Scholar 

  32. Petrov EP, Trubin IS, Medvedeva EV, Smolskiy SM (2013) Mathematical models of video-sequences of digital half-tone images. In: Atayero AA, Sheluhin OI (eds) Integrated models for information communication system and networks: design and development. IGI Global, Hershey

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, ACT, 2601, Australia

    Lakhmi C. Jain

  2. Institute of Informatics and Telecommunications, Siberian State Aerospace University, 31 Krasnoyarsky Rabochy, Krasnoyarsk, 660014, Russian Federation

    Margarita N. Favorskaya

Authors
  1. Lakhmi C. Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Margarita N. Favorskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lakhmi C. Jain .

Editor information

Editors and Affiliations

  1. Department of Informatics and Computer Techniques, Siberian State Aerospace University, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Jain, L.C., Favorskaya, M.N. (2015). Practical Matters in Computer Vision. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems-2. Intelligent Systems Reference Library, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-319-11430-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11430-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11429-3

  • Online ISBN: 978-3-319-11430-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation