Publications/xu.15.prl.inc

Materials

Mumford-Shah Simplification on the Color Tree of Shapes

You can download the x86_64 binary to compute the Mumford-Shah simplification running on the color tree of shapes Here.

Usage: ./mumford_shah_on_ctos input[rgb] α₀ α₁ λ output[rgb]
α₀	Grain filter size before merging trees (0 to disable)
α₁	Grain filter size on the color ToS (0 to disable)
λ	Mumford-shah regularisation weight (e.g. 5000)

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  Input RGB Image

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  Output Simplified Image

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  Input RGB Image

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  Output Simplified Image

Saliency Map Computation Relying on Mumford-Shah-Salient Level Line Selection

You can download the x86_64 binary to compute the saliency map representing hierarchical image simplification and segmentation Here. This application outputs the saliency map as a float image. The simplification and segmentation result can be obtained by thresholding this float image. Note that the image is twice as big as the original one and has a border for topogical and algorithmic purposes. Thus, any pixel with coordinates (x,y) in the original image is now at coordinates (2*(x+1), 2*(y+1)) in the saliency map.

Usage: ./saliency_map_mumford_ctos input[rgb] α₀ α₁ output[float]
α₀	Grain filter size before merging trees (0 to disable)
α₁	Grain filter size on the color ToS (0 to disable)

In the previous binary, we use the absolute difference between values of neighboring pixels to compute the average of gradient's magnitude, which is used to sort the shapes. An alternative is to use a gradient image given by a sophisticated contour detection method (e.g., the Structured Edge) to compute the average of gradient's magnitude. You can download this x86_64 binary Here.

Usage: ./saliency_map_mumford_ctos_grad input[rgb] α₀ α₁ gradSE.pgm output[float]
α₀	Grain filter size before merging trees (0 to disable)
α₁	Grain filter size on the color ToS (0 to disable)
gradSE 	Gradient image given by a contour detection method (Gpb or SE)

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  Input RGB Image

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  Saliency map

Illustrations

Saliency map computation on BSDS500 database

The first test was performed on the BSDS500 database. Some samples are given below and full results are available in this archive. The results are obtained relying on the gradient image of Structured Edge.

• From top to bottom: input images; saliency maps; slight/moderate/strong simplification.
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Saliency map computation on Weizmann database

The second test was performed on the Weizmann database. Some samples are given below and full results are available in this archive. The results are obtained relying on the gradient image of Structured Edge.

• From top to bottom: input images; saliency maps; slight/moderate/strong simplification.
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