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
Optimal Edge Detector Design I: Parameter Selection and Noise Effects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature detection from local energy
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subpixel Measurements Using a Moment-Based Edge Operator
IEEE Transactions on Pattern Analysis and Machine Intelligence
Group theoretical methods in image processing
Group theoretical methods in image processing
Refining edges detected by a LoG operator
Computer Vision, Graphics, and Image Processing
Edge detection by associative mapping
Pattern Recognition
Optimal Edge Detectors for Ramp Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
On Achievable Accuracy in Edge Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum-likelihood edge detection in digital signals
CVGIP: Image Understanding
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
CVGIP: Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
A lie group approach to steerable filters
Pattern Recognition Letters
On the Precision in Estimating the Location of Edges and Corners
Journal of Mathematical Imaging and Vision
Self-organizing maps
Dimension reduction by local principal component analysis
Neural Computation
Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Canonical Decomposition of Steerable Functions
Journal of Mathematical Imaging and Vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
An Operator Which Locates Edges in Digitized Pictures
Journal of the ACM (JACM)
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
On the Location Error of Curved Edges in Low-Pass Filtered 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Clustering Appearances of 3D Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Nonlinear manifold learning for visual speech recognition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Population Codes for Orientation Estimation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
An Algorithm for Finding Intrinsic Dimensionality of Data
IEEE Transactions on Computers
Parameter extraction from population codes: A critical assessment
Neural Computation
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Manifold models for signals and images
Computer Vision and Image Understanding
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Edge detection depends not only upon the assumed model of what an edge is, but also on how this model is represented. The problem of how to represent the edge model is typically neglected, despite the fact that the representation is a bottleneck for both computational cost and accuracy. We propose to represent edge models by a partition of the edge manifold corresponding to the edge model, where each local element of the partition is described by its principal components. We describe the construction of this representation and demonstrate its benefits for various edge models.