Threshold Superposition in Morphological Image Analysis Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute openings, thinnings, and granulometries
Computer Vision and Image Understanding
Vector Median Filters, Inf-Sup Operations, and Coupled PDE's: Theoretical Connections
Journal of Mathematical Imaging and Vision
A Comparison of Algorithms for Connected Set Openings and Closings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Antiextensive connected operators for image and sequence processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Scale space classification using area morphology
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An adaptive morphological filter for image processing
IEEE Transactions on Image Processing
Flat zones filtering, connected operators, and filters by reconstruction
IEEE Transactions on Image Processing
Three-dimensional median-related filters for color image sequence filtering
IEEE Transactions on Circuits and Systems for Video Technology
Component-Trees and Multi-value Images: A Comparative Study
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Component-Trees and Multivalued Images: Structural Properties
Journal of Mathematical Imaging and Vision
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Area morphological scale-spaces are widely used for hierarchical image analysis and segmentation. Despite their advantages, their extension to colour images has been restricted by the lack of an explicit order relationship for vector values. This paper presents a theoretical evaluation of two recently proposed colour sieves and their properties. It is also demonstrated that the extrema definition used by a colour sieve determines both the aggressiveness of its sieving action and its processing speed. A new colour sieve structure is introduced that attempts to capture the relative advantages of the two sieves previously studied. An objective study of the noise reduction performance of these colour sieves is presented. The segmentation performance is also analysed using the methodology provided by the Berkeley Segmentation Dataset and Benchmark, both in terms of the overall segmentation performance and its robustness to image noise. The new colour sieve is shown to have the best overall segmentation performance, and to be the most robust.