The Topological Structure of Scale-Space Images
Journal of Mathematical Imaging and Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Profile scale spaces for statistical image match in bayesian segmentation
Profile scale spaces for statistical image match in bayesian segmentation
Statistical Multi-Object Shape Models
International Journal of Computer Vision
Multi-figure anatomical objects for shape statistics
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Estimating the statistics of multi-object anatomic geometry using inter-object relationships
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Histogram statistics of local model-relative image regions
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Estimating the statistics of multi-object anatomic geometry using inter-object relationships
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Histogram statistics of local model-relative image regions
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
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We face the question of how to produce a scale space of image intensities relative to a scale space of objects or other characteristic image regions filling up the image space, when both images and objects are understood to come from a population. We argue for a schema combining a multi-scale image representation with a multi-scale representation of objects or regions. The objects or regions at one scale level are produced using soft-edged apertures, which are subdivided into sub-regions. The intensities in the regions are represented using histograms. Relevant probabilities of region shape and inter-relations between region geometry and of histograms are described, and the means is given of inter-relating the intensity probabilities and geometric probabilities by producing the probabilities of intensities conditioned on geometry.