A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Readings in computer vision: issues, problems, principles, and paradigms
The effect of noise on edge orientation computations
Pattern Recognition Letters
Precision Edge Contrast and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Computer Vision
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale estimation of vector field anisotropy application to texture characterization
Signal Processing - From signal processing theory to implementation
Extracting image orientation feature by using integration operator
Pattern Recognition
A biologically motivated multiresolution approach to contour detection
EURASIP Journal on Applied Signal Processing
Performance analysis of a cooperative multiple access relaying scheme
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Edge Drawing: A combined real-time edge and segment detector
Journal of Visual Communication and Image Representation
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Edges in a scene generally project to smooth continuous curves nearly everywhere in the image, which results in low angular deviation of the intensity gradients in small neighborhoods straddling edges. Angular deviation is shown to be a measure of SNR. A theoretical analysis of angular deviation arising due to independent and identically distributed N(0, sigma /sup 2/) random noise is presented. Angular deviation thresholds for neighborhood sizes from 3*3 to 11*11 pixels are determined both from this analysis and numerical examples. The proposed gradient angular dispersion detection algorithm detects edge elements by comparing the measured angular deviation with values computed for the minimum acceptable SNR. Low values of deviation violate the 'no edge' hypothesis. The algorithm is shown to make good use of the limited dynamic range of the imaging system. The sensitivity and selectivity of the strategy are both shown to be high.