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g Texture Feature Extraction in Image Analysis by Using Experimental Des S. A. Orjuela Vargas 1 , R. de Keyser 2 , and W. Philips 1 11th FIRW PhD symposium | December 2010 1.Department of Telecommunications and Information Processing (TELIN-IPI-IBBT), Gent University Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium. 2. Department of Electrical Energy, Systems and Automation (EeSA) , Gent University, Belgium http://telin.ugent.be/~seraleov [email protected] A planning phase using experimental design theory add reliability to image analysis results [2] S. A. Orjuela, E. Vansteenkiste, F. Rooms, S. De Meulemeester, R. De Keyser, and W. Philips. Evaluationof the wear label description in carpets by using local binary pattern techniques. Textile Research Journal, 2010. To extract texture features several techniques are evaluated High amount of data must be analyzed Optimal set of features are commonly chosen by “best guess approach” Techniques dependences are not evaluated Characteristics from image outcomes are quantified in response variables to compare techniques No guarantee of optimality No reliability of results and inferences Technique dependences are similarly detected Coocurrence Matrices Laws’ Energy Local Binary Patterns Wavelets Significant differences are identified when probability results are smaller than a given Technique A Technique B Technique C IMAGE Quantified Characteristics Response variables Linear Model Analysis of Variance ANOVA Comparison is performed by using ANalysis Of VAriance (ANOVA) The relationship between wear labels and features must be at least linear-ranked If so, the distinction between consecutive wear labels must be maximized Application: Automatic wear label assessment for carpets, comparison of three techniques Two response variables are quantified 1. Monotonicity (): Based on the Spearman’s rank correlation 2. Number of consecutive wear labels that can be statistically distinguished (): Based on the Tukey test A test for multiple comparison must be further performed LBPRMC technique performs better than the others for both response variables Significance differences detected for both response variables at 99% of confidence Wear Labels [1] S. A. Orjuela, Experimetal Design Theory as a Tool for Optimizing Feature Extraction in Image Analysis, Tutorial, STSIVA 2010. Features Texture is a pattern describing variations in a surface at scales smaller than the scale of interest Texture analysis has four major areas of interest medical imaging Industrial inspection Remote sensing Image retrieval Optimal combinations of techniques are found Carpets are certified according to their capability in retaining the original appearance Distinction of different degrees of wear by texture analysis in photographs Worn samples are compared to original samples assessing the change in appearance with wear labels Texture Analysis Experimental Design Theory in Image Analysis =
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