A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection and motion detection
Image and Vision Computing
Locating texture and object boundaries
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Residual Analysis for Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line detection using an optimal IIR filter
Pattern Recognition
Optimal Edge Detectors for Ramp Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimal linear operator for step edge detection
CVGIP: Graphical Models and Image Processing
Image Filtering, Edge Detection, and Edge Tracing Using Fuzzy Reasoning
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
Edge, Shade and Mixed Range Detection by Fuzzy Gaussian Filter for an Autonomous Robot
Journal of Intelligent and Robotic Systems
Detection of Displacement Vectors through Edge Segment Detection
IEICE - Transactions on Information and Systems
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
A wavelet-based multiresolution edge detection and tracking
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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Image segmentation based on contour extraction usually involves three stages of image operations: feature extraction, edge detection and edge linking. This paper is devoted to the first stage: a method to design feature extractors used to detect edges from noisy and/or blurred images. The method relies on a model that describes the existence of image discontinuities (e.g. edges) in terms of covariance functions. The feature extractor transforms the input image into a 驴log-likelihood ratio驴 image. Such an image is a good starting point of the edge detection stage since it represents a balanced trade-off between signal-to-noise ratio and the ability to resolve detailed structures. For 1-D signals, the performance of the edge detector based on this feature extractor is quantitatively assessed by the so called 驴average risk measure.驴 The results are compared with the performances of 1-D edge detectors known from literature. Generalizations to 2-D operators are given. Applications on real world images are presented showing the capability of the covariance model to build edge and line feature extractors. Finally it is shown that the covariance model can be coupled to a MRF-model of edge configurations so as to arrive at a maximum a posteriori estimate of the edges or lines in the image.