Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Scale-Space From Nonlinear Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Nonlinear Decomposition: The Sieve Decomposition Theorem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Geometry in C
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
From High Energy Physics to Low Level Vision
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Scale-Space Filters and Their Robustness
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Flat zones filtering, connected operators, and filters by reconstruction
IEEE Transactions on Image Processing
An evaluation of area morphology scale-spaces for colour images
Computer Vision and Image Understanding
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Sieves and their variants are established processors for simplifying greyscale images. Because combined outputs of these filters satisfy the scale-space causality property they are often referred to as scale-space filters although they have quite different characteristics compared to systems based around diffusion. In this paper we implement several possible extensions of sieves for colour images which include: applying the processor on separate channels; and enforcing an ordering on the colour vectors. We show that a new definition, based on convex hulls in colour space, can lead to an effective algorithm. As with the greyscale method, the colour sieve produces a tree-based representation of image that form the first step to a meaningful hierarchical decomposition.