The Relevance of Non-Generic Events in Scale Space Models
International Journal of Computer Vision
On detecting all saddle points in 2D images
Pattern Recognition Letters
Using Catastrophe Theory to Derive Trees from Images
Journal of Mathematical Imaging and Vision
Exploring and exploiting the structure of saddle points in Gaussian scale space
Computer Vision and Image Understanding
Multi-scale Stacked Sequential Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Mutual information aspects of scale space images
Pattern Recognition
On manifolds in Gaussian scale space
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Combining different types of scale space interest points using canonical sets
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Towards a new paradigm for motion extraction
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Tree-Based tracking of temporal image
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Deep structure from a geometric point of view
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Combinatorial properties of scale space singular points
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
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Using a Gaussian scale space, one can use the extra dimension, viz. scale, for investigation of "built-in" properties of the image in scale space. We show that one of such induced properties is the nesting of special iso-intensity manifolds, which yield an implicitly present hierarchy of the critical points and regions of their influence, in the original image. Its very nature allows one not only to segment the original image automatically, but also to apply "logical filters" to it, obtaining simplified images. We give an algorithm deriving this hierarchy and show its effectiveness on two different kinds of images, both with respect to segmentation and simplification.