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This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chaincode pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.

The study was aimed at determining the feature of a motif found in a Songket image in order to make the object detectable and readable. The method used was image color segmentation in the form of a process of segmentation of the image area based on the similarity in colors, which was continued with the binary process that aims to change the image into binary form (0 and 1), so that it only has two colors namely black and white. This study also used mathematical morphology in detecting objects, by using dilation operation and filling holes. After the process of mathematical morphology was completed, the next process was motif extraction by applying moore contour tracking algorithms and the development of chain code algorithms. The results of the process carried out showed that the development chain code algorithm can generate the number of objects, the length of chain code, and probable value of rate of appearances of each chain code in a motif, despite there are some objects in a motif. Then the values are stored into the database as The Feature of Songket Motifs.

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This study aims to facilitate the identification of proximal caries in the Panoramic Dental X-Ray image. Twenty-seven X-Ray images of proximal caries were elaborated. The images in digital form were processed using Matlab and Multiple Morphological Gradients. The process produced sharper images and clarifies the edges of the objects in the images. This makes the characteristics of the proximal caries and the caries severity can be identified precisely.