Component tree

higra.component_tree_min_tree(graph, …)	Min Tree hierarchy from the input vertex weighted graph.
higra.component_tree_max_tree(graph, …)	Max Tree hierarchy from the input vertex weighted graph.

component_tree_min_tree(graph, vertex_weights)

Min Tree hierarchy from the input vertex weighted graph.

The Min/Max Tree structure were proposed in [1], [2]. The algorithm used in this implementation was first described in [3].

[1] (1,2)

Ph. Salembier, A. Oliveras, and L. Garrido, “Anti-extensive connected operators for image and sequence processing,” IEEE Trans. Image Process., vol. 7, no. 4, pp. 555-570, Apr. 1998.

[2] (1,2)

Ro. Jones, “Connected filtering and segmentation using component trees,” Comput. Vis. Image Understand., vol. 75, no. 3, pp. 215-228, Sep. 1999.

[3] (1,2)

Ch. Berger, T. Geraud, R. Levillain, N. Widynski, A. Baillard, and E. Bertin, “Effective Component Tree Computation with Application to Pattern Recognition in Astronomical Imaging,” IEEE ICIP 2007.

Parameters:

• graph – input graph

• vertex_weights – vertex weights of the input graph

Returns:

a tree (Concept CptHierarchy) and its node altitudes

component_tree_max_tree(graph, vertex_weights)

Max Tree hierarchy from the input vertex weighted graph.

The Min/Max Tree structure were proposed in [1], [2]. The algorithm used in this implementation was first described in [3].

Parameters:

• graph – input graph

• vertex_weights – vertex weights of the input graph

Returns:

a tree (Concept CptHierarchy) and its node altitudes