PERBANDINGAN PEWARNAAN CITRA GRAYSCALE MENGGUNAKAN METODE K-MEANS CLUSTERING DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING83
software that serves to draw. This jobs are very expensive and takes a lot of time. This study aims to improve the quality of grayscale images so that the results obtained have a better quality than the initial image, implementing techniques to improve image quality by creating a colorless image (gr...
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2012
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| Summary: | software that serves to draw. This jobs are very expensive and takes a lot of
time. This study aims to improve the quality of grayscale images so that the
results obtained have a better quality than the initial image, implementing
techniques to improve image quality by creating a colorless image (grayscale
image) into a color image, build a system that can be utilized to improve the
quality of grayscale images, and compare of k-means clustering method and
hierarchical agglomerative clustering methods for coloring grayscale images.
Expected to be the basis of scientific development and implementation of image
processing systems for improving the quality of grayscale image into a more
complex color images.
color The image is converted into the � ! color spaces. Luminance �! and
gray value of each image are grouped based on proximity of each element. Color
mapping is done by transferring the value of chrome !! to be added in the gray
value of each element grayscale image with similarity between each centroid color
images and.
The results obtained in this study is a grayscale image color depends on the
amount of a given cluster, the selection of the reference image categories and the
influence of each color image in the same category. Testing staining based on
subjective analysis and objective analysis that has been done can be concluded
that the application of k-means clustering method is better than the application of
agglomerative hierarchical clustering methods in coloring grayscale images. |
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