RT Book, Section T1 Performance analysis of homomorphic systems for image change detection A1 Pajares Martinsanz, Gonzalo A1 Ruz Ortíz, José Jaime A1 Cruz García, Jesús Manuel de la AB Under illumination variations image change detection becomes a difficult task. Some existing image change detection methods try to compensate this effect. It is assumed that an image can be expressed in terms of its illumination and reflectance components. Detection of changes in the reflectance component is directly related to scene changes. In general, scene illumination varies slowly over space, whereas the reflectance component contains mainly spatially high frequency details. The intention is to apply the image change detection algorithm to the reflectance component only. The aim of this work is to analyze the performance of different homomorphic pre-filtering schemes for extracting the reflectance component so that the image change detection algorithm is applied only to this component. This scheme is not suitable for scenes without spatial high frequency details. PB Springer-Verlag Berlín SN 3-540-26153-2 YR 2005 FD 2005 LK https://hdl.handle.net/20.500.14352/53342 UL https://hdl.handle.net/20.500.14352/53342 LA eng NO 1. Aach, T., Kaup, A.: Bayesian algorithms for adaptive change detection in image sequences using Markov Random fields. Signal Processing: Image Communication. 7 (1995) 147-160 2. Bruzzone, L., Fernández-Prieto, D.: Automatic Analysis of the difference Image for unsupervised change detection. IEEE Trans. Geoscience Remote Sensing. 38(3) (2000) 1171-1182.3. Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A Systematic Survey. Submitted to IEEE Trans. Image Processing, (available on-line http://www.ecse.rpi.edu/homepages/rjradke/pages/research.html, 20044. Toth, D., Aach, T., Metzler, V.: Bayesian Spatio-Temporal Motion detection under varying illumination. In: M. Gabbouj, P. Kuosmanen, (eds.): Proc. European Signal Processing Conference (EUSIPCO), Tampere, Finland, (2000) 2081-20845. Gonzalez, R.C. and Woods, E.R. 1993, Digital Image Processing. Addison-Wesley, Reading, MA (1993)6. Kovesi, P.: MATLAB functions for Computer Vision and Image Analysis (available on-line http://www.csse.uwa.edu.au/~pk/Research/MatlabFns.tar.gz (2004)7. Gómez-Moreno, H., Maldonado-Bascón, S., López-Ferreras, F. Martín.Martín, P. and Villafranca-Continente, J.M. 2000. Motion detection using support vector machines. In: Proc. International Conf. Signal Processing and Communications (available 0n-line)http://www2.uah.es/teose/webpersonal/Hilario/Personal/Pagina_files/Publications.html8. Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 11(7)(1989) 674-6939. Liu, S.C., Fu, C.W., Chang, S.: Statistical Change Detection with Moments under Time-Varying Illumination. IEEE Trans. Image Processing. 7(9) (1998) 1258-1268 NO © Springer-Verlag Berlin Heidelberg 2005.Iberian Conference on Pattern Recongnition and Image Analysis (2nd. Jun 07-09, 2005. Estoril, Portugal) DS Docta Complutense RD 2 may 2024