Boonwat Attachoo

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Problem statement: For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. Approach: In this study, we proposed a new method to enhance the satellite image which compares two procedures using two different(More)
A unique method of image filtering has been developed that enhances the detail and sharpens the edges of colored satellite images. Histogram equalization coupled with a two stage data filtering process that applies convolution with laplacian and sharpening with laplacaian through the 3 color bands that produce the colored satellite images has yielded(More)
An effective method with enhancement procedures is proposed for image sharpening. Histogram equalization and edge detecting procedures are applied to original images. The mean value, standard deviation, and signal to noise ratio are defined as the statistical index which specifies the brightness, resolution, as well as the sharpness of the image. From the(More)
In this research, we proposed an alternative method to retrieve images from its clusters which is determined by its color histogram. In other approach, it is called cluster based image retrieval model. One common problem with conventional content based image retrieval (CBIR) system is that the output data contains too many non-relevant feedbacks due to the(More)
This paper, we proposed a novel framework for combining and weighting all of three i.e. color, shape and texture features to achieve higher retrieval efficiency. The color feature is extracted by quantifying the YUV color space and the color attributes like the mean value, the standard deviation, and the image bitmap of YUV color space is represented. The(More)
Learning Classifier Systems (LCSs) are rule-based systems that have widely been used in data mining over the last few years. This paper employs UCS, a supervised learning classifier system, that was a version of LCSs for classification in data mining tasks. In this paper, we propose an adaptive framework of a rule-based competitive learning environment. In(More)