A novel edge detection method based on the maximizing objective function
Pattern Recognition
A new edge detector based on Fresnel diffraction
Pattern Recognition Letters
A statistical framework based on a family of full range autoregressive models for edge extraction
Pattern Recognition Letters
Texture-gradient-based contour detection
EURASIP Journal on Applied Signal Processing
A method based on rank-ordered filter to detect edges in cellular image
Pattern Recognition Letters
On candidates selection for hysteresis thresholds in edge detection
Pattern Recognition
Text extraction from images captured via mobile and digital devices
International Journal of Computational Vision and Robotics
Solving the process of hysteresis without determining the optimal thresholds
Pattern Recognition
Gradient histogram: Thresholding in a region of interest for edge detection
Image and Vision Computing
IEEE Transactions on Image Processing
A gravitational approach to edge detection based on triangular norms
Pattern Recognition
A novel method to look for the hysteresis thresholds for the Canny edge detector
Pattern Recognition
VLSI architecture for real time edge detection of monochrome video sequences
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Unsupervised edge detection and noise detection from a single image
Pattern Recognition
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Many edge detectors are available in image processing literature where the choices of input parameters are to be made by the user. Most of the time, such choices are made on an ad-hoc basis. In this article, an edge detector is proposed where thresholding is performed using statistical principles. Local standardization of thresholds for each individual pixel (local thresholding), which depends upon the statistical variability of the gradient vector at that pixel, is done. Such a standardized statistic based on the gradient vector at each pixel is used to determine the eligibility of the pixel to be an edge pixel. The results obtained from the proposed method are found to be comparable to those from many well-known edge detectors. However, the values of the input parameters providing the appreciable results in the proposed detector are found to be more stable than other edge detectors and possess statistical interpretation.