A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Edge detection and motion detection
Image and Vision Computing
Visual reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing space for practical reasoning
Image and Vision Computing
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Singularity Theory and Phantom Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localization and Noise in Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Authenticating Edges Produced by Zero-Crossing Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing
Some Defects in Finite-Difference Edge Finders
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Operator Which Locates Edges in Digitized Pictures
Journal of the ACM (JACM)
A Local Visual Operator Which Recognizes Edges and Lines
Journal of the ACM (JACM)
Computer Vision
Finding Edges and Lines in Images
Finding Edges and Lines in Images
A probabilistic approach to low-level vision
A probabilistic approach to low-level vision
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Some Defects in Finite-Difference Edge Finders
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Feature controlled adaptive difference operators
Discrete Applied Mathematics
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
Improving difference operators by local feature detection
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
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A finite difference edge finder in which the finite difference is computed at a range of widths, i.e. a range of distances between data points, is introduced. Wide operators report low-amplitude responses more reliably than narrow operators, so if wide operators are used to fill gaps in narrow operator responses, each operator can be restricted to report only statistically reliable responses without losing many real features. This sharply reduces the noise in the final output. Theoretical bounds on spurious responses in the finite difference outputs, given only weak assumptions about the signal and noise, are presented. The expected response of the edge finder to an ideal straight step edge is also analyzed. These performance measures are compared with those of a standard algorithm based on Gaussian smoothing and those of a second algorithm that also considers the spatial structure of noise. The algorithms prove equally good at suppressing noise, but are better able to detect faint or blurred features. These predictions are confirmed by empirical tests on real images.