Sparse glcm: gray-level co-occurrence matrix computation for point cloud processing
AssigneesSony Group Corporation, Sony Corporation of America
InventorsAlexandre Zaghetto, Danillo Graziosi, Ali Tabatabai
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Abstract
A novel method of classifying point cloud data by extending the Gray-level Co-occurrence Matrix (GLCM) technique from the 2D to the sparse 3D domain is described herein. The method is able to be applied to point clouds derived from a mesh collection/meshes (such as, the Real-World Textured Things (RWTT) mesh collection). Implementations designed for multiple purposes are described herein: sampling and quantization of RWTT meshes, generation of GLCMs and corresponding texture descriptors, and the selection of potential candidate point clouds based on these extracted descriptors.
Claims23
A method programmed in a non-transitory memory of a device comprising:
- finding a set of voxels;
- computing a Gray-level Co-occurrence Matrix (GLCM) based on colors of two furthest voxels in the set of voxels for each GLCM channel;
- and
- calculating texture metrics from the GLCM.
An apparatus comprising:
- a non-transitory memory for storing an application, the application for:
- finding a set of voxels;
- computing a Gray-level Co-occurrence Matrix (GLCM) based on colors of two furthest voxels in the set of voxels for each GLCM channel;
- and
- calculating texture metrics from the GLCM;
- and
- a processor coupled to the memory, the processor configured for processing the application.
A system comprising:
- an encoder configured for:
- finding a set of voxels;
- computing a Gray-level Co-occurrence Matrix (GLCM) based on colors of two furthest voxels in the set of voxels for each GLCM channel;
- calculating texture metrics from the GLCM;
- and
- performing a point cloud classification based on the texture metrics;
- and
- a decoder configured for receiving the point cloud classification.
Dependencies are inferred from the claim text.