Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Journal of Mathematical Imaging and Vision
Generic Events for the Gradient Squared with Application to Multi-Scale Segmentation
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Temporal structure tree in digital linear scale space
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Gradient Structure of Image in Scale Space
Journal of Mathematical Imaging and Vision
Critical Scale for Unsupervised Cluster Discovery
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Unsupervised cluster discovery using statistics in scale space
Engineering Applications of Artificial Intelligence
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This paper deals with the figure field, which is defined as the negative of the gradient vector field of the linear scale-space image. The scale-space hierarchy is obtained from the connectivity of stationary points determined by figure-field fluxes and trajectories of the stationary points in the scale space. A point at infinity plays an important role in this theory. The figure-field fluxes and the configuration of stationary points at each scale define a graph in the scale-space image. This graph describes the topological relations of segments in the original image. We employ the Voronoi tessellation to extract boundaries of the segments from the blurred linear scale-space image.