23 lines
2.0 KiB
Diff
23 lines
2.0 KiB
Diff
From 9a9f67416683d69c1c8d2362ba6e50c4848803b2 Mon Sep 17 00:00:00 2001
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From: Bradley Lowekamp <blowekamp@mail.nih.gov>
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Date: Wed, 20 Nov 2019 15:30:50 -0500
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Subject: [PATCH] Update SLICImageFilter's module
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---
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Code/BasicFilters/json/SLICImageFilter.json | 2 +-
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1 file changed, 1 insertion(+), 1 deletion(-)
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diff --git a/Code/BasicFilters/json/SLICImageFilter.json b/Code/BasicFilters/json/SLICImageFilter.json
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index 2ffba2f4c..0e9ced7d7 100644
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--- a/Code/BasicFilters/json/SLICImageFilter.json
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+++ b/Code/BasicFilters/json/SLICImageFilter.json
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@@ -156,7 +156,7 @@
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]
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}
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],
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- "itk_module" : "SimpleITKFilters",
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+ "itk_module" : "ITKSuperPixel",
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"detaileddescription" : "The Simple Linear Iterative Clustering (SLIC) algorithm groups pixels into a set of labeled regions or super-pixels. Super-pixels follow natural image boundaries, are compact, and are nearly uniform regions which can be used as a larger primitive for more efficient computation. The SLIC algorithm can be viewed as a spatially constrained iterative k-means method.\n\nThe original algorithm was designed to cluster on the joint domain of the images index space and it's CIELAB color space. This implementation works with images of arbitrary dimension as well as scalar, single channel, images and most multi-component image types including ITK's arbitrary length VectorImage .\n\nThe distance between a pixel and a cluster is the sum of squares of the difference between their joint range and domains ( index and value ). The computation is done in index space with scales provided by the SpatialProximityWeight parameters.\n\nThe output is a label image with each label representing a superpixel cluster. Every pixel in the output is labeled, and the starting label id is zero.\n\nThis code was contributed in the Insight Journal paper: \"Scalable Simple Linear Iterative Clustering (SSLIC) Using a\nGeneric and Parallel Approach\" by Lowekamp B. C., Chen D. T., Yaniv Z.",
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"briefdescription" : "Simple Linear Iterative Clustering (SLIC) super-pixel segmentation."
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}
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