COMPARISON OF SIGNAL TO NOISE RATIO OF COLORED AND GRAY SCALE IMAGE IN CLUSTERED CONDITION FROM THE CONTOURS OF THE IMAGES WITH THE HELP OF DIFFERENT IMAGE FILTERING METHOD
Description
As we know the image can be processed with the help of different types of coding for example mat-lab. Here in this paper we are primarily focusing on some common filtering methodologies[5] related to image contour in clustered conditions. For filtering purpose in this paper we have used three different filtering technologies such as prewitt[3], sobel[3], canny[3] filtering. But on the other hand we have used both colored[1] and non-colored[3] images for clustering operations. Our main aim in this paper to show variations of signal to noise ratios for the colored and non-colored contour images with and without filtering. As per my request study the discussion of results very carefully to realize the deeper meaning of the journal.
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Steps to reproduce
[For all programming purpose we use only primary colored pixel image that is RGB image.] 1. Upload the original colored[1] image in matlab 2. Find out the LAB, GRAY[2], version of the original colored image[2].[ If we use RGB image directly then after clustering[3] we will get some blurred version of images of which contour[1],[2] creation cannot be done because as per definition of contour of an image color intensity[7] must be same although out the image to get a fine contour of the image. Syntaxes are easily available in matlab for image color conversion of for contour detection[8].] 3. Perform prewitt[3], sobel[3], canny3] , median, coefficient filtering. [ we perform all the filtering technology after performing clustering of images in order to get sharp contour of clustered images only. Here also filtering is necessary to make the contour of the image clearly visible. But matlab syntaxes are available as per the matlab help options.] 4. Find out the contour of the images for the case of filtered and non- filtered version of the converted images, Before that perform clustering , here we have used K MEANS CLUSTERING[3] as an example as such so many clustering processes are there, but remember contour must be obtained in clustered conditions[6] of gray scale images only because LAB image will give contour in blurred[6] version even if images are in clustered condition. 5. Find out the signal to noise ratio of the contour[1],[2] both filtered and non- filtered images. 6. Prepare a table of signal to noise ratio[1],[2] of both the contours of filtered and non-filtered images in order to make a comparison.