A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in correlated noise using Latin Square masks
Pattern Recognition
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
Residual Analysis for Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An edge detection technique using local smoothing and statistical hypothesis testing
Pattern Recognition Letters
Color edge extraction using orthogonal polynomials based zero crossings scheme
Information Sciences: an International Journal
An Operator Which Locates Edges in Digitized Pictures
Journal of the ACM (JACM)
Camera models and machine perception
Camera models and machine perception
Automatic edge detection using 3 × 3 ideal binary pixel patterns and fuzzy-based edge thresholding
Pattern Recognition Letters
A new efficient SVM-based edge detection method
Pattern Recognition Letters
Image compression based on a family of stochastic models
Signal Processing
The Max Roberts Operator is a Hueckel-Type Edge Detector
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive estimation of images using non-Gaussian autoregressive models
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
A simple unsupervised MRF model based image segmentation approach
IEEE Transactions on Image Processing
Roof-edge preserving image smoothing based on MRFs
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Thresholding in edge detection: a statistical approach
IEEE Transactions on Image Processing
A generalized Gaussian image model for edge-preserving MAP estimation
IEEE Transactions on Image Processing
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
IEEE Transactions on Image Processing
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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.