Edge detection Using Histogram Localization - Mohamed Asharudeen; Hema P Menon (Institute of Advanced Engineering and Science, 2018)
Detection of edges under noisy environments has been gaining lot of prominence in the recent past in most of the image and video processing applications. In this work a novel approach based on the distribution of intensity values and their corresponding positions has been proposed for distinguishing the edge pixels from the grey scale images. Separate histogram has been maintained for X and Y coordinates. The first order derivative is applied over these histograms to distinguish the edge pixels. The pixel with gradient distribution below a specific threshold value is selected as an edge pixel. This method is found to work well in case of both noiseless and noisy images. Hence this method is able to perceive the underlying information in case of noisy images also. The proposed algorithm can be used for both low and high resolution images. However, the performance of the algorithm is more evident in high resolution image. A general analysis of the proposed method has been conducted for arbitrary images. The major application of the proposed work can be used for the applications that doesn’t need any preprocessing or to avoid any loss of information like in medical image analysis as it contemplate towards every intensity bin to trace the edges present in the histogram of the image rather than the overall image concerning for direct edge tracing. The results have been compared with canny algorithm which is most commonly used for edge detection.