An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection., {"references":["S. Beag and N. Kehtarnavaz, \"Texture based Classification of Mass\nAbnormalities in Mammograms,\" The 13th IEEE Symposium on\nComputer-Based Medical Systems (CBMS 2000), pp.163-168, 2000.","R. M. Haralick, ÔÇÿÔÇÿStatistical and structural approaches to texture,--\nProc. IEEE 67, 786-804, 1979.","L. Van Gool, P. Dewaele, and A. Oosterlinck, ÔÇÿÔÇÿSURVEY: Texture\nanalysis anno 1983,-- Comput. Vis. Graph. Image Process. 29,\npp.336-357, 1985i","R. Gonzalez and P. Wintz, Digital Image Processing, 2th edition,\nAddison Wesley, 1998i","M. N. Shirazi, H. Noda, and N. Takao, ÔÇÿÔÇÿTexture classification based\non Markov modeling in wavelet features space,-- Image Vis. Comput.\n18, pp.967-973, 2000.","A. Ai-Janobi, ÔÇÿÔÇÿPerformance evaluation of cross-diagonal texture\nmatrix method of texture analysis,-- Pattern Recognition, 34, pp.171-\n180, 2001.","J. G. Leu, ÔÇÿÔÇÿOn indexing the periodicity of image textures,-- Image\nVis. Comput. 19, pp.987-1000, 2001.","S. Baheerathan, F. Albregstsen, and H. E. Danielsen, ÔÇÿÔÇÿNew texture\nfeatures based on the complexity curve,-- Pattern Recognition, 32,\npp.605- 618, 1999.","D. A. Clausi and M. E. Jernigan, ÔÇÿÔÇÿDesigning Gabor filters for optimal\ntexture separability,-- Pattern Recognition, 33, pp.1835-1849, 2000.\n[10] A. M. Pun and M. C. Lee, ÔÇÿÔÇÿRotation-invariant texture classification\nusing a two-stage wavelet packet features approach,-- IEE Proc.\nVision Image Signal Process. 148, pp.422-428, 2001.\n[11] M. H. Horng, Y. -N. Sun, and X. -Z. Lin, \"Texture Feature Coding\nMethod for Classification of Liver Sonography\", the 4th European\nConference on Computer Vision (ECCV96), Lecture Notes in\nComputer Science 1064, pp.209-218, 1996.\n[12] M. H. Horng, \"Texture feature coding method for texture\nclassification,\" Opt. Eng., Vol 42 (1), pp. 228-238, 2003.\n[13] V. Vapnik, Statistical Learning Theory. New York: Wiley 1998.\n[14] R.M. Harlaick, K. Shanmugam, Itshak Dinstein, \"Textural features for\nimage classification,\" IEEE Trans. System, Man and Cybernetics, vol.\n3, pp. 610-621, 1973.\n[15] L. Wang and D. C. He, ÔÇÿTexture classification using texture\nspectrum,\" Pattern Recognition, vol. 23, pp.905-910, 1990.\n[16] J. R. Carr and F. P. de Miranda, \"The semivariogram in comparison to\nthe co-occurrence matrix for classification of image texture,\" IEEE\nTrans. Geoscience and Remote Sensing, Vol. 36 (6), pp. 1945-1952,\nNo. 1998.\n[17] Rober Burbidge, Bernard Buxton, \"An introduction to Support Vector\nMachines for data mining,\" Keynote papers, young OR 12, University\nof Nottingham, M. Sheppee (ed), Operational research Society, pp.3-\n15, 2001.\n[18] O. Duda, E. Hart, and G. Stork, Pattern Recognition, John Wiley &\nSons, 2001."]}