Gradient histogram: Thresholding in a region of interest for edge detection
Selecting a threshold from the gradient histogram, a histogram of gradient magnitudes, of an image plays a crucial role in a gradient based edge detection system. This paper presents a methodology to determine the threshold from a gradient histogram generated using any kind of linear gradient operator on an image. We consider the image as a random process with dependent samples, model the gradient histogram using theories of random process and random input to a system, and determine a region of interest in the gradient histogram using certain properties of a probability density function. Standard histogram thresholding techniques are then used within the region of interest to get the threshold value. To obtain the edges, this threshold value is then used as the upper threshold of the hysteresis thresholding technique that follows the non-maximum suppression operation applied on the gradient magnitude image. The proposed methodology of determining a threshold in a gradient histogram is deduced through rigorous analysis and hence it helps in achieving consistently appreciable edge detection performance. Experimental results using different real-life and benchmark images are shown to demonstrate the effectiveness of the proposed technique.
gradient histogram edge detection gradient operators random process hysteresis thresholding non-maximum suppression skewness and kurtosis threshold determination