Common Representational Format

  • H. B. Mitchell


The subject of this chapter is the common representational format. Conversionof all sensor observations to a common format is a basic requirement for all multisensor data fusion systems. The reason for this is that only after conversion to a common format are the sensor observations compatible and sensor fusion may be performed. The following example, taken from the field of brain research, illustrates the concept of a common representational format.


Linear Discriminant Analysis Local Binary Pattern Scale Invariant Feature Transform Kernel Principal Component Analysis Mosaic Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded-Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Behloul, F., Lelieveldt, B.P.E., Boudraa, A., Janier, M., Revel, D., Reiber, J.H.C.: Neuro-fuzzy systems for computer-aided myocardial viability assessment. IEEE Trans. Med. Imag. 20, 1302–1313 (2001)CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intell. 19, 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape context. IEEE Trans. Patt. Anal. Mach. Intell. 24, 509–522 (2002)CrossRefGoogle Scholar
  5. 5.
    Bulanon, D.M., Burks, T.F., Alchanatis, V.: Image fusion of visible and thermal images for fruit detection. Biosystems Engng. 103, 12–22 (2009)CrossRefGoogle Scholar
  6. 6.
    Blackman, S.S., Popoli, R.F.: Design and analysis of modern tracking Systems. Artech House, Norwood (1999)zbMATHGoogle Scholar
  7. 7.
    Chen, S.C., Zhu, Y.L., Zhang, D.Q., Yang, J.Y.: Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Patt. Recogn. Lett. 26, 1157–1167 (2005)CrossRefGoogle Scholar
  8. 8.
    Cressie, N.A.C.: Statistics for spatial data. John Wiley and Sons (1993)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Int. Conf. Comp Vis Patt. Recogn, CVPR 2005 (2005)Google Scholar
  10. 10.
    Duan, Z., Han, C., Li, X.R.: Comments on “ Unbiased converted measurements for tracking”. IEEE Trans. Aero Elect. Sys. 40, 1374–1376 (2004)CrossRefGoogle Scholar
  11. 11.
    Durrant-Whyte, H.F.: Consistent integration and propagation of disparate sensor observations. Int. J. Robotics Res. 6, 3–24 (1987)Google Scholar
  12. 12.
    Durrant-Whyte, H.F.: Sensor models and multisensor integration. Int. J. Robotics Res. 7, 97–113 (1988)CrossRefGoogle Scholar
  13. 13.
    Fern, X.Z., Brodley, C.E.: Random projection for high dimensional data clustering: a cluster ensemble approach. In: Proc. 20th Int. Conf. Mach. Learn., pp. 186–193 (2003)Google Scholar
  14. 14.
    Fletcher, F.K., Kershaw, D.J.: Performance analysis of unbiased and classical measurement conversion techniques. IEEE Trans. Aero Elect. Sys. 38, 1441–1444 (2002)CrossRefGoogle Scholar
  15. 15.
    Fraiman, D., Justel, A., Svarc, M.: Pattern recognition via projection-based KNN rules. Comp. Stat. Data Anal. 54, 1390–1403 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Gevers, T., De Weijer, J., van Stockman, H.: Color feature detection. In: Lukac, R., Plataniotis, N. (eds.) Color Image Processing: Emerging Applications. CRC Press (2006)Google Scholar
  17. 17.
    Gevers, T., Stockman, H.: Robust histogram construction from color invariants for object recognition. IEEE Trans. Patt. Anal. Mach. Intell. 25, 113–118 (2004)CrossRefGoogle Scholar
  18. 18.
    Geusebroek, J.-M., van den Boomgaard, R., Smeulders, A.W.M., Geerts, H.: Color invariance. IEEE Trans. Patt. Anal. Mach. Intell. 23, 1338–1350 (2001)CrossRefGoogle Scholar
  19. 19.
    Goovaerts, P.: Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89, 1–45 (1999)CrossRefGoogle Scholar
  20. 20.
    Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Hydrology 228, 113–129 (2000)CrossRefGoogle Scholar
  21. 21.
    Hardoon, D.R., Szedmak, S., Shaw-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comp. 16, 2639–2664 (2004)zbMATHCrossRefGoogle Scholar
  22. 22.
    Hastie, T., Tibshirani, R.: Penalized discriminant analysis. Ann. Stat. 23, 73–102 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Patt. Anal. Mach. Intell. 20, 832–844 (1998)CrossRefGoogle Scholar
  24. 24.
    Howland, P., Wang, J., Park, H.: Solving the small sample size problem in face recognition using generalized discriminant analysis. Patt. Recogn. 39, 277–287 (2006)CrossRefGoogle Scholar
  25. 25.
    Hyvarinen, A., Karhunen, A., Oja, E.: Independent Component Analysis. John Wiley and Sons (2001)Google Scholar
  26. 26.
    Ji, S., Ye, J.: A unified framework for generalized linear discriminant analysis. IEEE Conf. Comp. Vis. Patt. Recogn. CVPR (2008)Google Scholar
  27. 27.
    Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)Google Scholar
  28. 28.
    Li, M., Yuan, B.: 2D-LDA: A novel statistical linear discriminant analysis for image matrix. Patt. Recogn. Lett. 26, 527–532 (2005)CrossRefGoogle Scholar
  29. 29.
    Li, H., Zhang, K., Jiang, T.: Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans. Neural Networks (2006)Google Scholar
  30. 30.
    Liao, S., Law, W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Proc. 18, 1107–1118 (2009)CrossRefGoogle Scholar
  31. 31.
    Lin, Y., Yu, Q., Medioni, G.: Efficient detection and tracking of moving objects in geo-coordinates. Mach. Vis. Appl. 22, 505–520 (2011)CrossRefGoogle Scholar
  32. 32.
    Lindhal, D., Palmer, J., Pettersson, J., White, T., Lundin, A., Edenbrandt, L.: Scintigraphic diagnosis of coronary artery disease: myocardial bull’s-eye images contain the important information. Clinical Physiology 18, 554–561 (1998)CrossRefGoogle Scholar
  33. 33.
    Ling, H., Jacobs, D.W.: Shape classification using the inner distance. IEEE Trans. Patt. Analy. Mach. Intell. 29, 286–299 (2007)CrossRefGoogle Scholar
  34. 34.
    Liu, J., Chen, S., Tan, X.: A study on three linear discriminant analysis based methods in small sample size problems. Patt. Recogn. 41, 102–111 (2008)zbMATHCrossRefGoogle Scholar
  35. 35.
    Loog, M., Duin, R.P.W.: Linear dimensionality reduction via a heteroscedastic extension of LDA: The Chernoff criterion. IEEE Trans. Patt. Anal. Mach. Intell. 26, 732–739 (2004)CrossRefGoogle Scholar
  36. 36.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vision 20, 91–110 (2004)CrossRefGoogle Scholar
  37. 37.
    Marcialis, G.L., Roli, F.: Decision-level fusion of PCA and LDA-band recognition algorithms. Int. J. Imag. Graphics 6, 293–311 (2006)CrossRefGoogle Scholar
  38. 38.
    Mazziotta, J., Toga, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-Gallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher, G., Feidler, J., Smith, K., Boomsma, D., Pol, H.H., Cannon, T., Kawashima, R., Mazoyer, B.: A four-dimensional probabilistic atlas of the human brain. Am. Med. Inform. Assoc. 8, 401–430 (2001)CrossRefGoogle Scholar
  39. 39.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Patt. Anal. Mach. Intell. 27, 1615–1630 (2005)CrossRefGoogle Scholar
  40. 40.
    Miller, M.D., Drummond, O.E.: Coordinate transformation bias in target tracking. In: Proc. SPIE Conf. Sig. Data Proc. Small Targets, vol. 3809, pp. 409–424 (1999)Google Scholar
  41. 41.
    Moore, J.R., Blair, W.D.: Practical aspects of multisensor tracking. In: Bar-Shalom, Y., Blair, W.D. (eds.) Multitarget-Multisensor Tracking: Applications and Advances, vol. III, pp. 1–76. Artech House (2000)Google Scholar
  42. 42.
    Morena, P., Bernardino, Santos-Victor, J.: Improving the SIFT descriptor with smooth derivative filters. Patt. Recogn. Lett. 30, 18–26 (2009)CrossRefGoogle Scholar
  43. 43.
    Moro, C.M.C., Moura, L., Robilotta, C.C.: Improving reliability of Bull’s Eye method. Computers in Cardiology, 485–487 (1994)Google Scholar
  44. 44.
    Nunn, W.R.: Position finding with prior knowledge of covariance parameters. IEEE Trans. Aero. Elect. Sys. 15, 204–208 (1979)CrossRefGoogle Scholar
  45. 45.
    Pele, O., Werman, M.: A Linear Time Histogram Metric for Improved SIFT Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 495–508. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  46. 46.
    Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Trans. Patt. Anal. Mach. Intell. 28, 1619–1630 (2006)CrossRefGoogle Scholar
  47. 47.
    Schabenberger, O., Gotway, C.A.: Statistical Methods for Spatial Data Analysis. Chapman and Hall (2005)Google Scholar
  48. 48.
    Sanchez-Brea, L.M., Bernabeu, E.: On the standard deviation in charge-coupled device cameras: A variogram-based technique for non-uniform images. Elect. Imag. 11, 121–126 (2002)CrossRefGoogle Scholar
  49. 49.
    Skocaj, D.: Robust subspace approaches to visual learning and recognition. PhD thesis, University of Ljubljana (2003)Google Scholar
  50. 50.
    Skurichina, M., Duin, R.P.W.: Combining Feature Subsets in Feature Selection. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 165–175. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  51. 51.
    Soille, P.: Morphological image compositing. IEEE Trans. Patt. Anal. Mach. Intell. 28, 673–683 (2006)CrossRefGoogle Scholar
  52. 52.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  53. 53.
    Tan, N., Huang, L., Liu, C.: Proc. Int. Conf. Image Proc (ICIP), pp. 1237–1240 (2009)Google Scholar
  54. 54.
    Thomaz, C.E., Gillies, D.F.: A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition. In: 18th Brazilian Symp. Comp. Graph Imag. Proc. SIG-GRAPI 2005, pp. 89–96 (2005)Google Scholar
  55. 55.
    Thomaz, C.E., Giraldi, G.A.: A new ranking method for principal component analysis and its application to face image analysis. Im. Vis. Comp. 28, 902–913 (2010)CrossRefGoogle Scholar
  56. 56.
    Thompson, P.M., Mega, M.S., Narr, K.L., Sowell, E.R., Blanton, R.E., Toga, A.W.: Brain image analysis and atlas construction. In: Handbook of Medical Imaging. Medical Image Processing and Analysis, vol. 2. SPIE Press (2000)Google Scholar
  57. 57.
    Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Art. Intell. 99, 21–71 (1998)zbMATHCrossRefGoogle Scholar
  58. 58.
    Thrun, S.: Learning occupancy grids with forward sensor models. Autonomous Robots 15, 111–127 (2003)CrossRefGoogle Scholar
  59. 59.
    Topi, M.: The local binary pattern approach to texture analysis - extensions and application. PhD thesis, University of Oulu (2003)Google Scholar
  60. 60.
    Tumer, K., Oza, N.C.: Input decimated ensembles. Patt. Anal. Appl. 6, 65–77 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  61. 61.
    Qiu, G., Fang, J.: Classification inan informative sample subspace. Patt. Recogn. 41, 949–960 (2008)zbMATHCrossRefGoogle Scholar
  62. 62.
    Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review. In: IEE Proc. - Vis. Imag. Sig. Proc., vol. 152, pp. 192–204 (2005)Google Scholar
  63. 63.
    Wang, M., Perera, A., Gutierrez-Osuna, R.: Principal discriminant analysis for small-sample-size problems: application to chemical sensing. In: Proc. 3rd IEEE Conf. Sensors, Vienna, Austria (2004)Google Scholar
  64. 64.
    Wang, X., Han, T., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: Int. Conf. Comp. Vis. ICCV 2009 (2009)Google Scholar
  65. 65.
    Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for human detection. IPSJ Trans. Comp. Vis. Appl. 2, 39–47 (2010)CrossRefGoogle Scholar
  66. 66.
    Wu, H., Siegel, M., Khosla, P.: Vehicle sound signature recognition by frequency vector principal component analysis. IEEE Trans. Instrument Meas. 48, 1005–1009 (1999)CrossRefGoogle Scholar
  67. 67.
    Xiang, C., Fan, X.A., Lee, T.H.: Face recognition using recursive Fisher linear discriminant. IEEE Trans. Image. Proc. 15, 2097–2105 (2006)CrossRefGoogle Scholar
  68. 68.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. Patt. Anal. Mach. Intell. 26, 131–137 (2004)CrossRefGoogle Scholar
  69. 69.
    Ye, J., Janardan, R., Li, Q., Park, H.: Feature reduction via generalized uncorrelated linear discriminant analysis. IEEE Trans. Knowledge Data Engng. 18, 1312–1322 (2006)CrossRefGoogle Scholar
  70. 70.
    Zhang, D., Zhou, Z.-H., Chen, S.: Diagonal principal component analysis for face recognition. Patt. Recogn. 39, 140–142Google Scholar
  71. 71.
    Zhang, P., Peng, J., Riedel: Discriminant analysis: a unified approach. In: Proc. 5th Int. Conf. Data Mining (ICDM 2005), Houston, Texas (2005)Google Scholar
  72. 72.
    Zheng, W., Zhao, L., Zou, C.: An efficient algorithm to solve the small sample size problem for LDA. Patt. Recogn. 37, 1077–1079 (2004)zbMATHCrossRefGoogle Scholar
  73. 73.
    Zomet, A., Levin, A., Peleg, S., Weiss, Y.: Seamless image stitching by minimizing false edges. IEEE Trans. Image Process. 15, 969–977 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Section 3424IAI Elta Electronics Ind. Ltd.AshdodIsrael

Personalised recommendations