Difference between revisions of "Publications/xu.15.prl.inc"
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=== Saliency Map Computation Relying on Mumford-Shah-Salient Level Line Selection === |
=== Saliency Map Computation Relying on Mumford-Shah-Salient Level Line Selection === |
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| + | You can download the x86_64 binary to compute the saliency map representing hierarchical image simplification and segmentation [https://lrde.epita.fr/~xu/bin/saliency_map_on_ctos 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 has 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. |
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| − | You can download the x86_64 binaries to compute the Multivariate Tree of Shapes [https://lrde.epita.fr/~carlinet/thesis/bin/compute_ctos-demo Here]. This application outputs 16-bits |
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| − | image where each pixel stores the depth of the node it belongs to. To recover the MToS from |
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| − | this image, one just has to compute its max-tree. Note that the image is twice has big has the original |
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| − | one and has a border for topogical and algorithmic purposes. Thus, any pixel with coordinates (x,y) in |
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| − | the original image is now at coordinates (2*(x+1), 2*(y+1)) in the depth image. The application also |
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| − | outputs a 8bits grayscale version of the depth image that can be used to vizualise the shapes by thresholding this image. |
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<pre> |
<pre> |
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Revision as of 11:26, 21 September 2015
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)
Input RGB Image
Output Simplified Image
Input RGB Image
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 has 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: ./compute_ctos-demo [options] input depth16.tiff depth8.png
Input RGB Image
8-bit depth Image
Illustrations
Natural image simplification with the Mumford-Shah functional optimized on the MToS
The method minimizes the Mumford-Shah cartoon model constrained by the tree topology. It removes nodes from the tree until the energy doest not decrease anymore. The tests were performed on the Weizmann database. Some samples are given below and full results are available in this archive.
- Top: original images. Bottom: simplified images.
