Segmenting by Compression Using Linear Scale-Space and Watersheds
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Scale-Spaces, PDE's, and Scale-Invariance
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Using Catastrophe Theory to Derive Trees from Images
Journal of Mathematical Imaging and Vision
An Explanation for the Logarithmic Connection between Linear and Morphological System Theory
International Journal of Computer Vision
Technique for automated recognition of sunspots on full-disk solar images
EURASIP Journal on Applied Signal Processing
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
Object density-based image segmentation and its applications in biomedical image analysis
Computer Methods and Programs in Biomedicine
An explanation for the logarithmic connection between linear and morphological systems
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
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After introducing several results relating to the modification of the homotopy of gradient functions based on extrema in the base image and building on earlier results in morphological scale-space, we introduce a scale-space monotonicity theorem for regions of an image defined by watersheds of a gradient function modified to retain only the local minima or maxima of its smoothed parent image. We then illustrate the theorem with an example of the scale-space extraction of texture features from the nuclei of cervical cells