H-means image segmentation to identify solar thermal features
van Dyk, DavidNote: Order does not necessarily reflect citation order of authors.
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CitationStein, N., Vinay Kashyap, Xiao-Li Meng and David van Dyk. 2013. H-means image segmentation to identify solar thermal features. In ICIP 2012: 19th IEEE International Conference on Image Processing: Proceedings, ed. E. Saber: September 30-October 3, 2012, Lake Buena Vista, Florida, USA, 1597-1600. Piscataway, NJ: Institute of Electrical and Electronics Engineers.
AbstractProperly segmenting multiband images of the Sun by their thermal properties will help determine the thermal structure of the solar corona. However, off-the-shelf segmentation algorithms are typically inappropriate because temperature information is captured by the relative intensities in different passbands, while the absolute levels are not relevant. Input features are therefore pixel-wise proportions of photons observed in each band. To segment solar images based on these proportions, we use a modification of k-means clustering that we call the H-means algorithm because it uses the Hellinger distance to compare probability vectors. H-means has a closed-form expression for cluster centroids, so computation is as fast as k-means. Tempering the input probability vectors reveals a broader class of H-means algorithms which include spherical k-means clustering. More generally, H-means can be used anytime the input feature is a probabilistic distribution, and hence is useful beyond image segmentation applications.
This file is one chapter of the bookICIP 2012 : 2012 [19th] IEEE International Conference on Image Processing : proceedings
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10886848
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