A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Review
Multiscale Gradient Magnitude Watershed Segmentation
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
From High Energy Physics to Low Level Vision
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Applications of Locally Orderless Images
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Parallel Implementations of AOS Schemes: A Fast Way of Nonlinear Diffusion Filtering
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Recursive Gaussian Derivative Filters
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Exploring and exploiting the structure of saddle points in Gaussian scale space
Computer Vision and Image Understanding
Transitions of multi-scale singularity trees
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
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We consider images as manifolds embedded in a hybrid of a high dimensional space of coordinates and features. Using the proposed energy functional and mathematical landmarks, images are partitioned into segments. The nesting of image segments occurring at catastrophe points in the scale-space is used to construct image hierarchies called Multi-Scale Singularity Trees (MSSTs). We propose two kinds of mathematical landmarks: extrema and saddles. Unlike all other similar methods proposed hitherto, our method produces soft-linked image hierarchies in the sense that all possible connections are suggested along with their energies. The information added makes possible for directly estimating the stability of the connection and hence the costs of transitions. Aimed applications of MSSTs include multi-scale pre-segmentation, image matching, sub-object extraction, and hierarchical image retrieval.