SAGA.imagery_segmentation.4: Superpixel Segmentation

{"id": "SAGA.imagery_segmentation.4", "title": "Superpixel Segmentation", "description": "The Superpixel Segmentation tool implements the 'Simple Linear Iterative Clustering' (SLIC) algorithm, an image segmentation method described in Achanta et al. (2010). <br/><br/>SLIC is a simple and efficient method to decompose an image in visually homogeneous regions. It is based on a spatially localized version of k-means clustering. Similar to mean shift or quick shift, each pixel is associated to a feature vector. <br/><br/>This tool is follows the SLIC implementation created by Vedaldi and Fulkerson as part of the VLFeat library. 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"schema": {"type": "integer", "default": 100, "nullable": true}, "id": "MAX_ITERATIONS"}, "REGULARIZATION": {"title": "Regularization", "description": "Regularization", "schema": {"type": "number", "default": 1, "format": "double", "nullable": true}, "id": "REGULARIZATION"}, "SIZE": {"title": "Region Size", "description": "Starting 'cell size' of the superpixels given as number of cells.", "schema": {"type": "integer", "default": 10, "nullable": true}, "id": "SIZE"}, "SIZE_MIN": {"title": "Minimum Region Size", "description": "In postprocessing join segments, which cover less cells than specified here, to a larger neighbour segment.", "schema": {"type": "integer", "default": 1, "nullable": true}, "id": "SIZE_MIN"}, "SUPERPIXELS_DO": {"title": "Create Superpixel Grids", "description": "Create Superpixel Grids", "schema": {"type": "boolean", "default": false, "nullable": true}, "id": "SUPERPIXELS_DO"}, "POSTPROCESSING": {"title": "Post-Processing", "description": "Post-Processing", 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