{"title":"One Dimensional Object Segmentation and Statistical Features of an Image for Texture Image Recognition System","authors":"Nang Thwe Thwe Oo","volume":26,"journal":"International Journal of Mathematical and Computational Sciences","pagesStart":69,"pagesEnd":74,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/1501","abstract":"<p>Traditional object segmentation methods are time consuming and computationally difficult. In this paper, onedimensional object detection along the secant lines is applied. Statistical features of texture images are computed for the recognition process. Example matrices of these features and formulae for calculation of similarities between two feature patterns are expressed. And experiments are also carried out using these features.<\/p>\r\n","references":"[1] Rafael C. Gonzalez, Richard E. Woods.,Digital\r\nImage Processing 1993, Wesley Publishing\r\nCompany, Inc. U.S.A.\r\n[2] J. K. Hawkins, Textural Properties for Pattern\r\nRecognition. Academic Press, New York, 1970,\r\n\u00d0\u00fc. 347 - 370.\r\n[3] William K. Pratt, Digital Image Processing:\r\nPIKS Inside, Third Edition. Los Altos,\r\nCalifornia, c. 519 - 548, 2001.\r\n[4] C. H. Chen, L. F. Pau. The Handbook of Pattern\r\nRecognition and Computer Vision (2nd\r\nEdition). P. S. P. Wang (eds.), World Scientific\r\nPublishing Co., c. 207 - 248, 1998.\r\n[5] Li Yi Wei. Texture synthesis by fixed\r\nneighborhood searching. PhD. Stanford\r\nuniversity, 2001.\r\n[6] Mishulina O.A., Labinskaya \u00f0\u00c9. \u00f0\u00c9., Sharbinina\r\n\u00f0\u00a3.\u00f0\u00c6. Practical for the course \"Introduction to\r\ntheory of neural network\". \u00f0\u00a3.: MEPhI, 2000.\r\n[7] Win Htay, Histological image recognition\r\nmethod in the medical diagnostic problem,\r\nScience conference, MEPhI-2006, T3.\r\n[8] Mishulina \u00f0\u00d7.\u00f0\u00c9., Win Htay, Texture image\r\nclassification using vector neural network. XV\r\nInternational technological science seminar,\r\nAlushta,18-25 September 2006.\r\n[9] Mishulina \u00f0\u00d7.\u00f0\u00c9., Win Htay, Feature image\r\nrecognition system. Science conference MEPhI-\r\n2007, Russia, \u00f0\u00a3.:\u00f0\u00a3\u00f0\u00ff\u00f0\u00f1\u00f0\u00ff, 2007\r\n[10] Mishulina \u00f0\u00d7.\u00f0\u00c9., Win Htay, Texture image\r\nrecognition in the vector neural network, IX All\r\nRussian techno-science conference \u00abNeuro-\r\n2007\u00bb. \u00f0\u00a3.:\u00f0\u00a3EPhI, 2007. p. 146-157.\r\n[11] Win Htay, Secant line technology for the\r\ntexture image processing and recognition.\r\nMEPhI-2007, Russia, \u00f0\u00a3.:\u00f0\u00a3\u00f0\u00ff\u00f0\u00f1\u00f0\u00ff, 2007","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 26, 2009"}