A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination and color in computer generated imagery
Illumination and color in computer generated imagery
Modeling Light Reflection for Computer Color Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A physical approach to color image understanding
International Journal of Computer Vision
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Measuring and modeling anisotropic reflection
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
A framework for the construction of reflectance maps for machine vision
CVGIP: Image Understanding
Toward color image segmentation in analog VLSI: algorithm and hardware
International Journal of Computer Vision
Suppression of false edge detection due to specular reflection in color images
Pattern Recognition Letters
Improved Diffuse Reflection Models for Computer Vision
International Journal of Computer Vision
Illumination for computer generated pictures
Communications of the ACM
Realistic image synthesis using photon mapping
Realistic image synthesis using photon mapping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Lighting and Rendering
Digital Lighting and Rendering
Adaptive Image Segmentation by Combining Photometric Invariant Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating Reflection Parameters from a Single Color Image
IEEE Computer Graphics and Applications
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Restoration and Edge Detection for Color Images
Journal of Mathematical Imaging and Vision
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
Robust Color Segmentation Using the Dichromatic Reflection Model
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of empirical discrepancy evaluation measures
Pattern Recognition Letters
Edge and Corner Detection by Photometric Quasi-Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Hierarchical Graph-based Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Nonlinear Scale Space with Spatially Varying Stopping Time
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation in the Presence of Shadows and Highlights
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
High-accuracy edge detection with Blurred Edge Model
Image and Vision Computing
Classifying color edges in video into shadow-geometry, highlight, or material transitions
IEEE Transactions on Multimedia
A morphological gradient approach to color edge detection
IEEE Transactions on Image Processing
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The Dichromatic Reflection Model (DRM) introduced by Shafer in 1985 is surely the most referenced physics-based model of image formation. A preliminary analysis of this model derives in the conclusion that colour channels remain coupled by the reflectance of objects surface material. Taking this observation as a basis, this paper shows that this coupling manifests itself at the signal level in the form of a set of properties only satisfied in uniform reflectance areas. Such properties are deeply analysed and formalized throughout the paper, and, eventually, they are stated geometrically. As will be seen, a compatibility relationship stems from this geometric interpretation, which allows checking at a very low cost whether two pixels correspond to the same scene reflectance or not. Finally, an edge detector based on the use of the previous relationship is presented as an example of application. This edge detector inherits all the properties of the compatibility relationship: simplicity, low computational power requirements, sensor noise adaptivity and insensitivity to image intensity changes due to scene objects curvature.