skimage 0.17.2

ParametersReturns
multiblock_lbp(int_image, r, c, width, height)

The features are calculated similarly to local binary patterns (LBPs), (See local_binary_pattern ) except that summed blocks are used instead of individual pixel values.

MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. Nine equally-sized rectangles are used to compute a feature. For each rectangle, the sum of the pixel intensities is computed. Comparisons of these sums to that of the central rectangle determine the feature, similarly to LBP.

Parameters

int_image : (N, M) array

Integral image.

r : int

Row-coordinate of top left corner of a rectangle containing feature.

c : int

Column-coordinate of top left corner of a rectangle containing feature.

width : int

Width of one of the 9 equal rectangles that will be used to compute a feature.

height : int

Height of one of the 9 equal rectangles that will be used to compute a feature.

Returns

output : int

8-bit MB-LBP feature descriptor.

Multi-block local binary pattern (MB-LBP).

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


File: /skimage/feature/texture.py#340
type: <class 'function'>
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