Interpreting the Tri Mandala Concept on the Motif of Gringsing Wayang Kebo Woven Cloth

Gringsing wayang kebo woven cloth is one of specialty cloths owned by Tenganan Pegringsingan Village, Manggis Sub-district, Karangasem Regency, Bali. This research aims to interpreting the Tri Mandala concept on the motif of gringsing wayang kebo woven cloth. The approach of this research is descriptive qualitative. Techniques for collecting data are observation, interview, and documentation, data analysis is using interactive analysis model and the validity of the data will be confirmed with triangulation technique. The result of this research shows that the motif of gringsing wayang kebo follows the of Tri Mandala concept that consists of 3 parts, Main pattern, Center pattern, and edge pattern. The motifs consisted of each pattern are 1). The motif on the main pattern consists of the plus, swastika, building, and scorpion motif; 2). The motif on the center pattern consists of an animal motif, floral, mountain, wayang; and; 3). The motif on the edge pattern consists of plus sign (+) and sun.

Using Local Edge Pattern Descriptors for Edge Detection

Edge detection is an active and critical topic in the field of image processing, and plays a vital role for some important applications such as image segmentation, pattern classification, object tracking, etc. In this paper, an edge detection approach is proposed using local edge pattern descriptor which possesses multiscale and multiresolution property, and is named varied local edge pattern (VLEP) descriptor. This method contains the following steps: firstly, Gaussian filter is used to smooth the original image. Secondly, the edge strength values, which are used to calculate the edge gradient values and can be obtained by one or more groups of VLEPs. Then, weighted fusion idea is considered when multiple groups of VLEP descriptors are used. Finally, the appropriate threshold is set to perform binarization processing on the gradient version of the image. Experimental results show that the proposed edge detection method achieved better performance than other state-of-the-art edge detection methods.