Mean probability boundary hierarchy¶
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Creates a region adjacency graph (rag) with the oriented watershed transform. |
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Mean probability boundary hierarchy. |
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Multiscale mean probability boundary hierarchy. |
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oriented_watershed
(graph, edge_weights, shape, edge_orientations=None)[source]¶ Creates a region adjacency graph (rag) with the oriented watershed transform.
The method is described in:
P. Arbelaez, M. Maire, C. Fowlkes and J. Malik, “Contour Detection and Hierarchical Image Segmentation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, May 2011. doi: 10.1109/TPAMI.2010.161
If no edge orientations are provided, then the weight of a rag edge between region i and j is the mean weight of the edges linking a vertex of i to a vertex of j.
This does not include gradient estimation.
- Parameters:
graph – must be a 4 adjacency graph (Concept
CptGridGraph
)edge_weights – gradient value on edges
shape – shape of the graph, i.e. a pair (height, width) (deduced from
CptGridGraph
)edge_orientations – estimated orientation of the gradient on edges (optional)
- Returns:
a pair (rag, rag_edge_weights): the region adjacency graph (Concept
CptRegionAdjacencyGraph
) and its estimated edge_weights
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mean_pb_hierarchy
(graph, edge_weights, shape, edge_orientations=None)[source]¶ Mean probability boundary hierarchy.
The method is described in:
P. Arbelaez, M. Maire, C. Fowlkes and J. Malik, “Contour Detection and Hierarchical Image Segmentation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, May 2011. doi: 10.1109/TPAMI.2010.161
This does not include gradient estimation.
The returned hierarchy is defined on the gradient watershed super-pixels.
The final sigmoid scaling of the hierarchy altitude is not performed.
- Parameters:
graph – must be a 4 adjacency graph (Concept
CptGridGraph
)edge_weights – gradient value on edges
shape – shape of the graph, i.e. a pair (height, width) (deduced from
CptGridGraph
)edge_orientations – estimated orientation of the gradient on edges (optional)
- Returns:
a tree (Concept
CptHierarchy
) and its node altitudes
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multiscale_mean_pb_hierarchy
(graph, fine_edge_weights, others_edge_weights, shape, edge_orientations=None)[source]¶ Multiscale mean probability boundary hierarchy.
The method is described in:
J. Pont-Tuset, P. Arbeláez, J. Barron, F. Marques, and J. Malik Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 39, no. 1, pp. 128 - 140, 2017.
and in:
K.K. Maninis, J. Pont-Tuset, P. Arbeláez and L. Van Gool Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 40, no. 4, pp. 819 - 833, 2018.
This does not include gradient estimation.
The returned hierarchy is defined on the gradient watershed super-pixels.
The final sigmoid scaling of the hierarchy altitude is not performed.
- Parameters:
graph – must be a 4 adjacency graph (Concept
CptGridGraph
)fine_edge_weights – edge weights of the finest gradient
others_edge_weights – tuple of gradient value on edges
shape – shape of the graph, i.e. a pair (height, width) (deduced from
CptGridGraph
)edge_orientations – estimated orientation of the gradient on edges (optional)
- Returns:
a tree (Concept
CptHierarchy
) and its node altitudes