IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implementation Of A Theory Of Edge Detection
Implementation Of A Theory Of Edge Detection
Finding Edges and Lines in Images
Finding Edges and Lines in Images
The Recovery and Understanding of a Line Drawing from Indoor Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Multiple Widths Yield Reliable Finite Differences (Computer Vision)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Defects in Finite-Difference Edge Finders
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving edge detection by an objective edge evaluation
SAC '92 Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subpixel Edge Location in Binary Images Using Dithering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Maps of Free-Form Curve Segments via Wave Propagation
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Detection and characterization of junctions in a 2D image
Computer Vision and Image Understanding
Behavior of the Laplacian of Gaussian Extrema
Journal of Mathematical Imaging and Vision
Efficient, recursively implemented differential operator, with application to edge detection
Pattern Recognition Letters
A Mathematical Operator for Automatic and Real Time Analysis of Sequences of Vascular Images
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Rotating scans for systematic error removal
SGP '09 Proceedings of the Symposium on Geometry Processing
Quantitative error measures for edge detection
Pattern Recognition
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Two aspects of edge detection are analyzed, namely accuracy of localization and sensitivity to noise. The detection of corners and trihedral vertices is analyzed for gradient schemes and zero-crossing schemes. It is shown that neither scheme correctly detects corners of trihedral vertices, but that the gradient schemes are less sensitive to noise. A simple but important conclusion is that the noise present in digital images of typical indoor scenes is small and the signal-to-noise ratio is high. The noise present in digital images is so small as to make the performances of a variety of filters almost indistinguishable. As a consequence small filters can be used and the exact shape of the filter is not critical.