Edge Detection and Enhancement of Color Images Based on Bilateral Filtering Method Using K-Means Clustering Algorithm

In the recent, object recognition and classification has become an emerging area of research in robotics. In many image processing applications accurate edge detection is very crucial and plays an important role. The continuous and connected edges detection of color images is important in many applications such as satellite imagery and discover cancers in medical images, etc. The detection of these edges is very difficult and most of the edge detection algorithms do not perform well against broken and thick edges in color images. This paper proposed an edge detection and enhancement technique using bilateral method to decrease the broken edges of an optimization model in order to detect the edges. Combining bilateral filtering with convolution mask show all edges that are necessary by analyzing window one-by-one without overlapping. The proposed scheme is applied over the color image for manipulating the pixels to produce better output. The simulation results are performed using both noisy images and noise-free images. For producing the experimental results Standard deviation, Arithmetic mean are calculated. With the use of these parameters’ quality assessment of corrupted and noisy images and the effectiveness of the proposed approach is evaluated.