3D Image Segmentation via Sparse Local Feature Graphs
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Solution Overview
Problem
Conventional methods for non-linear depth adjustment in 3D image segmentation require significant computational resources, making them inefficient for real-time processing on devices with limited capabilities, such as mobile devices.
Innovation Solution
The method employs a sparse graph-based approach for 3D image segmentation, where local features are determined, a local feature graph is constructed, and edge weights are assigned, allowing for efficient segmentation by quantizing depth maps, identifying reliable texture regions, and computing histograms to segment the image into depth regions, thereby reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pixel-based methods are used for 3D image segmentation, then segmentation accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the image into superpixels first, then performs graph construction and segmentation only on these superpixels rather than individual pixels. This hierarchical segmentation approach maintains segmentation accuracy while dramatically reducing the number of elements processed in subsequent computational steps, thus lowering computational resource consumption.
Solution Approach 2:
The patent extracts and removes unreliable texture regions from further processing by calculating confidence values and eliminating regions below a threshold. This extraction of problematic regions prevents them from consuming computational resources while maintaining segmentation quality in reliable regions.
2Manufacturing precision
If non-linear depth adjustment is performed on individual objects, then depth perception quality is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by quantizing the depth map and identifying depth regions before performing the actual segmentation and depth adjustment. By preparing the depth structure in advance and organizing it into meaningful regions, the subsequent non-linear depth adjustment can be applied efficiently to entire regions rather than individual pixels, reducing processing time while maintaining depth perception quality.
3Reliability
If detailed texture region analysis is performed, then segmentation reliability is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by calculating confidence values specifically for texture regions and selectively eliminating only the unreliable ones. Instead of uniformly processing all regions with high computational complexity, the method locally identifies and handles only the problematic regions, maintaining segmentation reliability where needed while reducing overall computational complexity.
Data Source
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AI summary
The present invention provides a method for segmentation of 3D image data of a 3D image, the method comprising: determining (20) local features for each of a plurality of views (101, 102, 103) of the3D image;determining (30) a local feature graph based on the determined local features; and segmenting (40) the 3D image data into a plurality of depth regions based on the determined local feature graph and a depth map (110)of the 3D image.