Mean probability boundary hierarchy¶

Creates a region adjacency graph (rag) with the oriented watershed transform. 

Mean probability boundary hierarchy. 

Multiscale mean probability boundary hierarchy. 

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. 898916, 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

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. 898916, May 2011. doi: 10.1109/TPAMI.2010.161
This does not include gradient estimation.
The returned hierarchy is defined on the gradient watershed superpixels.
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

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. PontTuset, 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. PontTuset, P. Arbeláez and L. Van Gool Convolutional Oriented Boundaries: From Image Segmentation to HighLevel 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 superpixels.
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