Skip to main content
SpringerLink
Log in

Role of linguistic quantifier and digitally approximated Laplace operator in infrared based ship detection

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Due to high sensitivity of strategical importance of sea border, detection of motion of enemy’s military troops and arms may play a crucial role. Attack of 26, November, 2008 on Mumbai, commercial capital of India, is the result of such type of piercing of water territory. Hence, this paper proposed new Threshold based ship detection method. Infrared image of ship in sea water used as input. To specify appropriate threshold Yager’s Linguistic quantifier ‘most’, ‘at least half’, and ‘as many as possible’ are used. Moreover edge based segmentation of thresholding images is carried out by using Laplacian operator. It is observed threshold generated by ‘at least half’ and ‘as many as possible’ quantifier are closer to visual heuristic threshold. It is also found linguistic quantifier based thresholding has generated clear edge to separate ship from background along with digital approximation of Laplace operator than other methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Bezdek J, Chandrasekhar R, Attikouzel Y (1998) A geometric approach to edge detection. IEEE Trans Fuzzy Syst 6(1):52–75

    Article  Google Scholar 

  • Caselles V, Kimmel R, Sapiro G (1997) Minimal surfaces based object segmentation. IEEE Trans Pattern Anal Mach Intell 19(4):394–397

    Article  Google Scholar 

  • Faghih F, Michael M (2002) Combining special and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm. IEEE Trans Image Process 11:1062–1071

    Article  MathSciNet  Google Scholar 

  • Gonzalez RC, Wood RE (2007) Digital Image Procesing, 2nd edn. Prentice Hall Publication, Upper Saddle River

    Google Scholar 

  • Hanshan LI, Zhiyong L, Zemin W (2009) Principle and analysis of high altitude projectile location measurement using multi-screen target method. Chin J Sci Instrum 30(3):621–624

    Google Scholar 

  • Jansen M, Malfait M, Bultheel A (1997) Generalization cross validation for wavelet thresholding. Signal Process 56(1):463–479

    Article  MATH  Google Scholar 

  • Konishi S, Yulle AL, Coughlan JM, Zhu SC (2003) Statistical edge detection: learning and evaluating edge cues. IEEE Trans Pattern Anal Mach Intell 25(1):57–74

    Article  Google Scholar 

  • Lopez-Molina C, Bustince H, Fernández J, Barrenechea E et al (2009) A t-norm based approach to edge detection. In: IWANN 2009, part I, LNCS 5517, pp 302–309

  • Lu S, Wang Z, Shen J (2003) Neuro-fuzzy synergism to the intelligent system for detection and enhancement. Pattern Recognit 36(10):2395–2409

    Article  MATH  Google Scholar 

  • Maragos PA, Schafer RW, Mersereau RM (1984) Two dimensional linear prediction and its application to adaptive predictive coding of images. IEEE Trans Acoust Speech Signal Process ASSP-32(6):1213–1229

    Article  Google Scholar 

  • Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI 26:530–549

    Article  Google Scholar 

  • Shapiro LG, Stockman GC (2007) Computer vision. Prentice Hall, Upper Saddle River (ISB/n 0-1-030796)

    Google Scholar 

  • Stoica R, Zerubia J, FrancosM (1998) The two dimensional word decomposition for segmentation and indexing in image libraries. In: Proceedings of the 1998 IEEE international conference on acoustics, speech, and signal processing, vol 5, pp 2917–2980

  • Sun G, Liu Q, Ji C, Li X (2007) A novel approach for edge detection based on the theory of universal gravity. Pattern Recognit 40(10):2766–2775

    Article  MATH  Google Scholar 

  • Suzuki K, Horiba I, Sugie N (2003) Neural edge enhancer for supervised edge enhancement from noisy image. IEEE Trans Pattern Anal Mach Intell 25(12):1582–1596

    Article  Google Scholar 

  • Tang H, Wu EX, Ma QY, Gallagher D, Perera GM, Zhuang T (2000) brain image segmentation by multi-resolution edge detection and region. Comput Med Imaging Graph 24(6):349–357

    Article  Google Scholar 

  • Vasiliev VF (1998) Recognition of 2-D object on geometrical quasi-similarity of polygonal representations. IEEE Int Conf Syst Man Cybern 5:4396–4400

    Google Scholar 

  • Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings international conference on computer vision and pattern recognition (CVPR), vol 1, pp 511–518

  • Yager RR (1988) On OWA aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18:183–190

    Article  Google Scholar 

  • Yansun Xu (1994) Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Trans Image Process 3(6):747–758

    Article  Google Scholar 

  • Yu J, Wang Y, Shen Y (2008) Noise reduction and edge detection via kernel anisotropic diffusion. Pattern Recognit Lett 29:1496–1503

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Krishna Institute of Engineering and Technology, Ghaziabad, India

    Ruchika Singh

  2. Department of Electronics Engineering, YMCA University of Science and Technology, Faridabad, India

    Munish Vashisht

  3. Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, India

    S. Qamar

Authors
  1. Ruchika Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Munish Vashisht
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Qamar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruchika Singh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, R., Vashisht, M. & Qamar, S. Role of linguistic quantifier and digitally approximated Laplace operator in infrared based ship detection. Int J Syst Assur Eng Manag 8 (Suppl 2), 1336–1342 (2017). https://doi.org/10.1007/s13198-017-0604-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-017-0604-x

Keywords

Search

Navigation