Implementation of Edge Detection using Quadrant Tree Classifier Method on Object Separation Based on Digital Image Processing (Case Study of State Flag Objects)
Abstract
Edge detection is segmentation of image input that aims to determine the edge by marking the detail part of an image. From some previous studies it has not been shown the results of detection to be able to separate objects from the center of the image input image itself.
The purpose of this study is to perform an edge detection function by dividing into nodes using the concept of the Quadran Tree Classifier method to be applied to the case study of the object colored image of the using national flag. Some input images have different levels of complexity and pixels, including the Korean flag, Wales flag, and the flying Indonesian flag.
The method is the adoption of tree data structure, where each has 4 nodes the same number of child nodes. If the node has children, number of nodes must be 4, recursively doing the loop. The working concept of this method split and merge segmentation. The results of object segmentation combined in accordance with the homogeneity of colors, especially those with confusion.
This research show which is able to observe the scanning pixel on image Korean flag and the flying Indonesian flag, however level pixel 520 x 347 such as Wales flag, this method is unable to separate between line object that is not nudge. The pixel resolution has an effect with total time execution segmentation (minute/sec), total segmentation identified and the total colour.
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