Batik is one of Indonesian’s traditional cloth. Motif or pattern drawn on a piece of batik fabric has a specific name and philosopy. Although batik cloths are widely used in everyday life, but only few people understand its motif and philosophy. This research is intended to develop a batik motif recognition system which can be used to identify motif of Batik image automatically. First, a batik image is decomposed into sub-images using wavelet transform. Six texture descriptors, i.e. max probability, correlation, contrast, uniformity, homogenity and entropy, are extracted from gray-level co-occurrence matrix of each sub-image. The texture features are then matched to the template features using canberra distance. The experiment is performed on Batik Dataset consisting of 1088 batik images grouped into seven motifs. The best recognition rate, that is 92,1%, is achieved using feature extraction process with 5 level wavelet decomposition and 4 directional gray-level co-occurrence matrix.
In urban areas, the shadow cast by buildings, trees along the road, abundant objects and complex image texture make the extraction of the road on very high Resolution RGB aerial image very difficult and challenging. We propose a method of road extraction from RGB aerial image in the followings steps: Shadow removal, enhanced sobel transform, keypoints extraction based on Maximally Stable Extremal Regions (MSER), feature extraction based on Speeded Up Robust Features (SURF) and road construction based on multi-resolution segmentation. The experimental results show that the proposed method achieves a good result.
In recent years, an automatic urban road extraction, as part of Intelligent Transportation research, has attracted the researchers due to the important role for the next modern transportation where urban area plays the main role within the transportation system. In this work, we propose a new combination of fuzzy ART clustering, Region growing, Morphological Operations and Radon transform (ARMOR) for automatic extraction of urban road networks from the digital surface model (DSM). The DSM data, which is based-on the elevation of surface, overcome a serious building's shadow problem as in the aerial photo image. Due to the different elevation between the road and the buildings, the thresholding technique yields a fast initial road extraction. The threshold values are obtained from Fuzzy ART clustering of the geometrical points in the histogram. The initial road is then expanded using region growing. Though most of the road regions are extracted, it contains a lot of non-road areas and the edge is still rough. A fast way to smoothing the region is by employing the morphology closing operation. Furthermore, we perform the road line filter by opening operation with a line shape structuring element, where the line orientation is obtained from the Radon Transform. Finally, the road network is constructed based-on B-Spline from the extracted road skeleton. The experimental result shows that the proposed method running faster and increases the quality and the accuracy about 10% higher than the highest result of the compared method.
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