Data Fusion: Concepts and Ideas pp 51-81 | Cite as
Common Representational Format
Chapter
First Online:
Introduction
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.
Keywords
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.
Preview
Unable to display preview. Download preview PDF.
References
- 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.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.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.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.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.Blackman, S.S., Popoli, R.F.: Design and analysis of modern tracking Systems. Artech House, Norwood (1999)MATHGoogle Scholar
- 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.Cressie, N.A.C.: Statistics for spatial data. John Wiley and Sons (1993)Google Scholar
- 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.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.Durrant-Whyte, H.F.: Consistent integration and propagation of disparate sensor observations. Int. J. Robotics Res. 6, 3–24 (1987)Google Scholar
- 12.Durrant-Whyte, H.F.: Sensor models and multisensor integration. Int. J. Robotics Res. 7, 97–113 (1988)CrossRefGoogle Scholar
- 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.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.Fraiman, D., Justel, A., Svarc, M.: Pattern recognition via projection-based KNN rules. Comp. Stat. Data Anal. 54, 1390–1403 (2010)MathSciNetMATHCrossRefGoogle Scholar
- 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.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.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.Goovaerts, P.: Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89, 1–45 (1999)CrossRefGoogle Scholar
- 20.Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Hydrology 228, 113–129 (2000)CrossRefGoogle Scholar
- 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)MATHCrossRefGoogle Scholar
- 22.Hastie, T., Tibshirani, R.: Penalized discriminant analysis. Ann. Stat. 23, 73–102 (1995)MathSciNetMATHCrossRefGoogle Scholar
- 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.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.Hyvarinen, A., Karhunen, A., Oja, E.: Independent Component Analysis. John Wiley and Sons (2001)Google Scholar
- 26.Ji, S., Ye, J.: A unified framework for generalized linear discriminant analysis. IEEE Conf. Comp. Vis. Patt. Recogn. CVPR (2008)Google Scholar
- 27.Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)Google Scholar
- 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.Li, H., Zhang, K., Jiang, T.: Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans. Neural Networks (2006)Google Scholar
- 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.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.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.Ling, H., Jacobs, D.W.: Shape classification using the inner distance. IEEE Trans. Patt. Analy. Mach. Intell. 29, 286–299 (2007)CrossRefGoogle Scholar
- 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)MATHCrossRefGoogle Scholar
- 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.Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vision 20, 91–110 (2004)CrossRefGoogle Scholar
- 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.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.Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Patt. Anal. Mach. Intell. 27, 1615–1630 (2005)CrossRefGoogle Scholar
- 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.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.Morena, P., Bernardino, Santos-Victor, J.: Improving the SIFT descriptor with smooth derivative filters. Patt. Recogn. Lett. 30, 18–26 (2009)CrossRefGoogle Scholar
- 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.Nunn, W.R.: Position finding with prior knowledge of covariance parameters. IEEE Trans. Aero. Elect. Sys. 15, 204–208 (1979)CrossRefGoogle Scholar
- 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.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.Schabenberger, O., Gotway, C.A.: Statistical Methods for Spatial Data Analysis. Chapman and Hall (2005)Google Scholar
- 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.Skocaj, D.: Robust subspace approaches to visual learning and recognition. PhD thesis, University of Ljubljana (2003)Google Scholar
- 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.Soille, P.: Morphological image compositing. IEEE Trans. Patt. Anal. Mach. Intell. 28, 673–683 (2006)CrossRefGoogle Scholar
- 52.Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, Heidelberg (2011)MATHGoogle Scholar
- 53.Tan, N., Huang, L., Liu, C.: Proc. Int. Conf. Image Proc (ICIP), pp. 1237–1240 (2009)Google Scholar
- 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.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.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.Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Art. Intell. 99, 21–71 (1998)MATHCrossRefGoogle Scholar
- 58.Thrun, S.: Learning occupancy grids with forward sensor models. Autonomous Robots 15, 111–127 (2003)CrossRefGoogle Scholar
- 59.Topi, M.: The local binary pattern approach to texture analysis - extensions and application. PhD thesis, University of Oulu (2003)Google Scholar
- 60.Tumer, K., Oza, N.C.: Input decimated ensembles. Patt. Anal. Appl. 6, 65–77 (2003)MathSciNetMATHCrossRefGoogle Scholar
- 61.Qiu, G., Fang, J.: Classification inan informative sample subspace. Patt. Recogn. 41, 949–960 (2008)MATHCrossRefGoogle Scholar
- 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.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.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.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.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.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.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.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.Zhang, D., Zhou, Z.-H., Chen, S.: Diagonal principal component analysis for face recognition. Patt. Recogn. 39, 140–142Google Scholar
- 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.Zheng, W., Zhao, L., Zou, C.: An efficient algorithm to solve the small sample size problem for LDA. Patt. Recogn. 37, 1077–1079 (2004)MATHCrossRefGoogle Scholar
- 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