Improved Edge Detection in HSV Color Space

In this paper, we propose a new method to convert image data into HSV color space data separated into three color channels in order to implement merged edge detection techniques after a noise removal in the S channel. Methods/Statistical analysis: The proposed algorithm was used with Gaussian blur pre-processing to reduce the noise in the image, unlike how a Sobel operator is commonly applied to a grayscale image after 3-channel color space separation on the image where the edge is detected. After the color space has been converted to the HSV color space according to binary human vision from a conventional RGB color space, each of the channels (H,S,V) is separated using the Sobel Operator. Findings: The results for each channel are compared, and then the surrounding pixel and comparison operations are executed to remove contour noise occurring in the S Channel. Improvements/Applications: In this manner, the noise removal filter is applied, and finally, the S and V channels are combined applying a filter to the end result and showing that the edges have been effectively detected.