201
Views
9
CrossRef citations to date
0
Altmetric
Original Article

Feature-based recognition approaches for infrared anti-ship missile seekers

, , , , &
Pages 305-320 | Accepted 15 Apr 2012, Published online: 12 Nov 2013

REFERENCES

  • Adamy D. EW 102: A Second Course in Electronic Warfare, 2004 (Artech House Books, London).
  • Titterton DH. The interaction in the development of optical missile seekers and jammer technology. Imaging Sci. J., 2010, 58, 276–285.
  • Chemring Countermeasures Ltd. Modelling and Simulation [online], 2012 (Salisbury, Chemring Countermeasures Ltd) [accessed 12 January 2012]. Available at: <http://www.chemringcm.com/Products/TechnologyServices/ModellingandSimulati/>
  • Hu MK. Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory, 1962, 8, 179–187.
  • Withagen PJ, Schutte K, Vossepoel AM, Breuers MG. Automatic classification of ships from infrared (FLIR) images. Proc. SPIE, 1999, 3720, 180–187.
  • Tremblay C, Valin P. Experiments on individual classifiers and on fusion of a set of classifiers, Proc. 5th Int. Conf. on Information fusion, Annapolis, MD, USA, July 2002, ISIF, Vol. 1, pp272–277.
  • Park Y, Sklansky J. Automated design of linear tree classifiers. Pattern Recogn., 1990, 23, 1393–1412.
  • Alves J. ‘Recognition of ship types from an infrared image using moment invariants and neural networks’, MSc thesis, US Naval Postgraduate School, Monterey, CA, USA, 2001.
  • Alves J, Herman J, Rowe NC. Robust recognition of ship types from an infrared silhouette, Proc. Command and Control Research and Technology Symp., San Diego, CA, USA, June 2004, CCRPS, Paper no. 070.
  • Fernandez HL, de Seixas J.M, Neves SR, Souza Filho J.B.O. Combining morphological mapping and principal curves for ship classification, Proc. Int. Symp. on Signals, circuits and systems: ISSCS 2005, Lasi, Romania, July 2005, IEEE, Vol. 2, pp. 605–608.
  • Hastie T, Stuetzle W. Principal curves. J. Am. Stat. Assoc., 1989, 84, 502–516.
  • Li H, Wang X. Automatic recognition of ship types from infrared images using support vector machines, Proc. Int. Conf. on Computer science and software engineering: CSSE 2008, Wuhan, China, December 2008, IEEE, Vol. 6, pp. 483–486.
  • Lowe DG. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 2004, 60, 91–110.
  • Feineigle PA, Morris DD, Snyder FD. Ship recognition using optical imagery for harbor surveillance, Proc. Association for Unmanned Vehicle Systems International AUVSI, Washington, DC, USA, June 2007, AUVSI, pp. 1–15.
  • Mouthaan MM, van den Broek SP, Hendriks EA, Schwering PBW. Region descriptors for automatic classification of small sea targets in infrared video. Opt. Eng., 2011, 50, 037002.
  • Harris C, Stephens M. A combined corner and edge detection, Proc. 4th Alvey Vision Conf., Manchester, UK, September 1988, University of Manchester, pp. 147–151.
  • Bay H, Ess A, Tuytelaars T, van Gool L. SURF: speeded up robust features. Comput. Vis. Image Understand., 2008, 110, 346–359.
  • Vedaldi A, Fulkerson B. VLFeat: an open and portable library of computer vision algorithms (ver. 0·9·9), 2010 [accessed 12 January 2012]. Available at: <http://www.vlfeat.org>
  • Viola PA, Jones MJ. Rapid object detection using a boosted cascade of simple features, Proc. IEEE Conf. on Computer vision and pattern recognition: CVPR 2001, Hawaii, HI, USA, December 2001, IEEE Computer Society, pp. 511–518.
  • Kroon DJ. Matlab OpenSURF, 2012 (Natick, MA, MathWorks) [accessed 12 January 2012]. Available at: <http://www.mathworks.com/matlabcentral/fileexchange/28300-opensurf-including-image-warp>
  • Evans C. OpenSURF, 2012 [accessed 12 January 2012]. Available at: <http://www.chrisevansdev.com/computer-vision-opensurf.html>
  • Hough PVC. ‘Method and means for recognizing complex patterns’, US Patent 3,069,654, 1962.
  • Duda RO, Hart PE. Use of the Hough Transformation to detect lines and curves in pictures. Commun. ACM, 1972, 15, 11–15.
  • Ballard DH. Generalizing the Hough Transform to detect arbitrary shapes. Pattern Recogn., 1981, 13, 111–122.
  • El-Maraghi TF. Matlab Sift Tutorial [online], 2004 (Toronto, Ont., University of Toronto) [accessed 12 January 2012]. Available at: <ftp://ftp.cs.utoronto.ca/pub/jepson/teaching/vision/2503/SIFTtutorial.zip>
  • Theodoridis S, Pikrakis A, Koutroumbas K, Cavouras D. An Introduction to Pattern Recognition: A Matlab Approach, 2010 (Elsevier Inc., Amsterdam).
  • Smaragdis P, Radhakrishnan R, Wilson KW. In Multimedia Content Analysis: Theory and Applications (Ed. A. Divakaran), 2009, pp. 1–34 (Springer, New York).
  • de Luna AE, Miravet C, Otaduy D, Dorronsoro C. A decision support system for ship identification based on the curvature scale space representation. Proc. SPIE, 2005, 5988, 59880K.
  • Wu C. SiftGPU: A GPU implementation of scale invariant feature transform (SIFT) [online], 2007 (The Department of Computer Science, Chapel Hill, NC) [accessed 12 January 2012]. Available at: <http://cs.unc.edu/∼ccwu/siftgpu>
  • Brown WM, Swonger CW. A prospectus for automatic target recognition. IEEE Trans. Aerospace Electron. Syst., 1989, 25, 401–410.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.