A statistical framework based on a family of full range autoregressive models for edge extraction

In this paper, a novel technique is proposed based on a Family of Full Range Autoregressive (FRAR) models to extract edges in 2D monochrome images. The model parameters are estimated based on Bayesian approach and is used to smooth the input images. At each pixel location, residual value is calculated by differentiating the original image and its smoothed version. Edge magnitudes and its directions are measured based on the residual. The edge magnitudes are squared to enhance the edges whereas the other values are suppressed by using confidence limit is based on the global descriptive statistics. Threshold value is fixed automatically based on the autocorrelation value calculated on the smoothed image. This extracts the thick edges. To obtain thin and continuous edges, the nonmaxima suppression algorithm is applied with the confidence limit based on the local descriptive statistics. Then the performance of the proposed technique is compared with that of the existing standard algorithms including Canny’s algorithm. Since Canny’s algorithm oversmoothes across the edges, it detects the spurious and weak edges. This problem is overcome in the proposed technique because it smoothes minimally across the edges. The extracted edge map is superimposed on its original image to justify that the proposed technique is locally characterize the edges correctly. Also, the proposed technique is experimented on synthetic images such as concentric circle and square images to prove that it detects the edges in all directions and edge junctions.