Minimum spanning tree-based segmentation
Image segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity. The higher-level region representation simplifies image analysis tasks such as counting objects or detecting changes, because region attributes (e.g. average intensity or shape) can be compared more readily than raw pixels.
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- enImage segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity. The higher-level region representation simplifies image analysis tasks such as counting objects or detecting changes, because region attributes (e.g. average intensity or shape) can be compared more readily than raw pixels.
- Has abstract
- enImage segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity. The higher-level region representation simplifies image analysis tasks such as counting objects or detecting changes, because region attributes (e.g. average intensity or shape) can be compared more readily than raw pixels.
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- Minimum spanning tree-based segmentation
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- enMinimum spanning tree-based segmentation
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- algo2.iti.kit.edu/wassenberg/phmsf/
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- Cardinal direction
- Category:Image segmentation
- Category:Spanning tree
- Connected-component labeling
- Counting sort
- CPU
- Disjoint-set data structure
- Graph (discrete mathematics)
- Grid graph
- Image stitching
- Kruskal's algorithm
- Minimum spanning tree
- Segmentation (image processing)
- Undirected graph
- SameAs
- 4rs2U
- m.09gpldm
- Q6865488
- Сегментация изображений на основе минимального остовного дерева
- Subject
- Category:Image segmentation
- Category:Spanning tree
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- Minimum spanning tree-based segmentation?oldid=1120319497&ns=0
- WikiPageLength
- 7818
- Wikipage page ID
- 24872872
- Wikipage revision ID
- 1120319497
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- Template:R
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