A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge extraction using entropy operator
Computer Vision, Graphics, and Image Processing
The local structure of image discontinuities in one dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Edge detection in multispectral images
CVGIP: Graphical Models and Image Processing
International Journal of Computer Vision
A computational approach for corner and vertex detection
International Journal of Computer Vision
Suppression of false edge detection due to specular reflection in color images
Pattern Recognition Letters
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
An Operator Which Locates Edges in Digitized Pictures
Journal of the ACM (JACM)
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy color edge extraction by inference rules quantitative study and evaluation of performances
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Color image edge detection using cluster analysis
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A Chromatic Contour Detector based on Abrupt Change Techniques
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Corner Detection in Textured Color Images
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Perceptual metrics for image database navigation
Perceptual metrics for image database navigation
Finding color and shape patterns in images
Finding color and shape patterns in images
Early vision using distributions
Early vision using distributions
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Vector order statistics operators as color edge detectors
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the detection of edges in vector images
IEEE Transactions on Image Processing
Edge detection of color images using directional operators
IEEE Transactions on Circuits and Systems for Video Technology
How the Spatial Filters of Area V1 Can Be Used for a Nearly Ideal Edge Detection
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
International Journal of Computer Vision
Texture design using a simplicial complex of morphable textures
ACM SIGGRAPH 2005 Papers
A model-based approach to junction detection using radial energy
Pattern Recognition
Change detection using a statistical model in an optimally selected color space
Computer Vision and Image Understanding
Combining Invariant and Corner-Like Features to Optimize Image Matching
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
An intuitive model of perceptual grouping for HCI design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Feature-based texture design using deformation techniques
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Robust color edge detection through tensor voting
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A new methodology for evaluation of edge detectors
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
The quadratic-chi histogram distance family
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Transportation Distances on the Circle
Journal of Mathematical Imaging and Vision
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Ardeco: automatic region detection and conversion
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Efficient closed-form solution to generalized boundary detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Video compression schemes using edge feature on wireless video sensor networks
Journal of Electrical and Computer Engineering
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
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For over 30 years researchers in computer vision have been proposing new methods for performing low-level vision tasks such as detecting edges and corners. One key element shared by most methods is that they represent local image neighborhoods as constant in color or intensity with deviations modeled as noise. Due to computational considerations that encourage the use of small neighborhoods where this assumption holds, these methods remain popular. This research models a neighborhood as a distribution of colors. Our goal is to show that the increase in accuracy of this representation translates into higher-quality results for low-level vision tasks on difficult, natural images, especially as neighborhood size increases. We emphasize large neighborhoods because small ones often do not contain enough information. We emphasize color because it subsumes gray scale as an image range and because it is the dominant form of human perception. We discuss distributions in the context of detecting edges, corners, and junctions, and we show results for each.