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"ABSTRACTThe goals of the present study were to evaluate, in vitro, the staining of different composite resins submitted to different common beverages, and to compare the staining effect of each of these solutions. A total of 288 specimens were randomly divided into six groups and immersed for 4 weeks in five staining solutions represented by red wine, orange juice, coke, tea and coffee or in artificial saliva as a control group. When analyzed over a black background, mean E00 values varied from 0.8 for Venus Diamond, Saremco Microhybrid and ELS in saliva and Estelite Posterior in coke to 37.6 for Filtek Supreme in red wine. When analyzed over a white background, mean ΔE00 values varied from 0.5 for Saremco Microhybrid in saliva to 51.1 for Filtek Supreme in red wine. All materials showed significant changes in color after 4 weeks of immersion in staining solutions. Significant differences were found between the tested composite resins and also between the staining solutions."

"Image clustering adalah pengelompokan citra berdasarkan kesamaan ciri tententu pada sekumpulan citra. Image clustering yang dilakukan berdasarkan konten citra dapat menggunakan komponen warna, tekstur, garis tepi, bentuk, dan lainnya, atau berupa gabungan dari beberapa komponen. Pada penelitian ini dilakukan image clustering berdasarkan komponen warna. Tiga hal yang diperhatikan dalam proses clustering ini adalah penggunaan ruang warna, representasi citra, dan metode clustering. Ruang warna yang digunakan dalam penelitian ini adalah RGB, HSV, dan L*a*b*. Representasi citra atau feature extraction menggunakan histogram dan Gaussian Mixture Model, sedangkan metode clustering yang digunakan adalah K-Means dan Agglomerative Hierarchical. Pada ruang warna RGB dan L*a*b*, kinerja clustering terbaik berhasil dilakukan dengan menggunakan representasi citra GMM, sedangkan pada ruang warna HSV, citra yang berhasil dikelompokan dengan kinerja paling baik menggunakan representasi citra histogram. Kemudian, metode K-Means clustering bekerja lebih baik daripada Agglomerative Hierarchical pada image clustering yang menggunakan komposisi warna.

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Image clustering is a process of grouping the image based on their similarity. Image clustering based on image content usually uses the color component, texture, edge, shape, or mixture of two components, etc. This research focuses in image clustering uses color component. Three main concepts concerned on this research are color space, image representation (feature extraction), and clustering method. RGB, HSV, and L*a*b* are used in color spaces. The image representations use Histogram and Gaussian Mixture Model (GMM), whereas the clustering methods are K-Means and Agglomerative Hierarchical Clustering. The result of the experiment show that GMM representation is better used for RGB and L*a*b* color space, whereas Histogram is better used for HSV. The experiment also show that K-Means better than Agglomerative Hierarchical for clustering method."