On Morphological Color Texture Characterization Erchan Aptoula and Sébastien Lefèvre Image Sciences, Computer Sciences and Remote Sensing Laboratory Louis Pasteur University Strasbourg, France {aptoula,lefevre}@lsiit.u-strasbg.fr October 12, 2007 ISMM

Contents Morphological tools for texture analysis Granulometry & covariance A combination of SE size-direction-distance Implementation on color images Application results MB ve doku çözümleme İki ana işlecin özellikleri Boyut-yön-uzaklık karışımı Renkli görüntülere uygulaması Ve deney sonuçları 1/15

Morphological texture description Texture characteristics: regularity (periodicity), directionality, complexity, overall color and color purity Burayı olduğugibi okuyoruz. A rich variety of tools: granulometry, morphological covariance-variogram, orientation maps, etc Main advantage of morphological approaches: their inherent capacity to exploit spatial pixel relations 2/15

Granulometry Standard granulometry of an image f : Extracts information on the granularity of its input Has several extensions: attribute based, multivariate, spatial, etc Evet nedir granulometry? 3/15

Morphological covariance Morphological covariance of an image f : P2,v: a pair of points separated by a vector v Extracts information on the regularity, directionality and coarseness of its input Morphological equivalent of the autocorrelation operator 4/15

Covariance + granulometry = ? granularity Covariance regularity directionality coarseness They extract complementary information, how should they be combined, by concatenation, ... or? 5/15

Covariance + granulometry = ? Employ 3 structuring element variables: size, direction and distance. Size granularity Distance regularity Direction directionality 6/15

Covariance + “granulometry” Pλ,v: a pair of SEs of size λ, separated by a vector v However: SE is not convex pseudo granulometry Aşınım yerine açınım düzenli biçimde daha iyi sonuç verdiğinden yerine o yerleştirildi..ayrıca süzgeç ilkesine sadık kalmak için de. Ancak SE convx olmadığından zaten pseudo granulometry oluyor sonuç. Strongly ordered texture Disordered texture 7/15

Extending to color images Requirements: A suitable color space A color ordering scheme (preferably total), to impose a lattice structure R G B 8/15

Color space choice Color space choice : perceptual, polar, etc... Polar color spaces : (+) intuitive components (-) manipulation of hue (-) multiple implementations Hangi uzayı neden seçiyoruz. Ayrıca bu aşamada neden olduğu sorunlardan kaçınmak için renközünü de kullanmamayı seçiyoruz. Luminance Saturation 9/15

Color ordering Luminance: contains the majority of variational information Color: auxiliary component For which levels of luminance does color become more important ? 2. How should the balance between luminance and “color” use be determined ? 10/15

Color ordering For which levels of luminance does color become more important ? Luminance is roughly separated into three regions. Main problem: saturation luminance relation. a b c 11/15

Color ordering 2. How should the balance between luminance and “color” use be determined ? Image or vector specific configurations are better suited for intra-image applications Here, an image database specific approach is used, by means of genetic optimization: Genelde görüntüye özel veya yöneye özel oluyor, ancak bu durum intra-image uygulamalar için uygun, örneğin bozukluk giderme gibi. Burada inter-image olduğu için görüntü tabanına özel birşey istiyoruz. 12/15

Application Outex13 texture database: 1360 images (128 x 128) of 68 colour textures Four directions (0°,45°,90°,135°), 15 different SE sizes (k:1 to 30, 2k+1) and 20 distances Results in a feature of size 20x4x15, which was reduced to 20x4x2 by PCA kNN classifier (k=1) and the Euclidean distance 13/15

Classification accuracies Features Grayscale Color Optimized Color Granulometry Covariance Concatenated Combined 67.53 73.82 77.75 83.53 68.78 76.92 79.93 85.49 72.03 80.46 83.74 88.13 14/15

Conclusion and perspectives A way of combining the complementary information provided by granulometry and covariance However, it leads to a pseudo granulometry Genetic optimization aids in exploiting color Shape variations, as well as the role of hue remain to be investigated 15/15

Thank you for your attention E. Aptoula and S. Lefèvre {aptoula,lefevre}@lsiit.u-strasbg.fr