IEEE Transactions on Pattern Analysis and Machine Intelligence
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional Analysis of Images in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Straight Lines in Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Prospects in Line Detection by Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Line Segments by Stick Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeing People in the Dark: Face Recognition in Infrared Images
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A biologically motivated multiresolution approach to contour detection
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Automatic generation of consensus ground truth for the comparison of edge detection techniques
Image and Vision Computing
Classified road detection from satellite images based on perceptual organization
International Journal of Remote Sensing
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Irregular pyramid segmentations with stochastic graph decimation strategies
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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The edge-detection problem is posed as one of detecting step discontinuities in the observed correlated image, using directional derivatives estimated with a random field model. Specifically, the method consists of representing the pixels in a local window by a 2-D causal autoregressive (AR) model, whose parameters are adaptively estimated using a recursive least-squares algorithm. The directional derivatives are functions of parameter estimates. An edge is detected if the second derivative in the direction of the estimated maximum gradient is negatively sloped and the first directional derivative and a local estimate of variance satisfy some conditions. Because the ordered edge detector may not detect edges of all orientations well, the image scanned in four different directions, and the union of the four edge images is taken as the final output. The performance of the edge detector is illustrated using synthetic and real images. Comparisons to other edge detectors are given. A linear feature extractor that operates on the edges produced by the AR model is presented.