3D Image Segmentation via Local Feature Correspondences
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Solution Overview
Problem
Conventional methods for 3D image segmentation require significant computational resources, making them inefficient for real-time processing on devices with limited capabilities, such as small 3D devices, and fail to effectively adapt 3D video content to different viewing contexts.
Innovation Solution
The method segments 3D image data by determining local features in multiple views and merging corresponding depth regions based on spatial displacement and correlation values, reducing computational load through the use of local features instead of individual pixel analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods for 3D image segmentation are used, then segmentation accuracy is maintained, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the 3D image data into multiple views and processing local features within each view separately. This allows the system to maintain segmentation accuracy while reducing overall computational load by working with smaller, divided portions of the image data simultaneously.
Solution Approach 2:
The patent extracts and processes only local features from the 3D image data rather than analyzing all pixels. By taking out and focusing on relevant local features, the system maintains segmentation quality while significantly reducing computational resource consumption.
2Measurement precision
If conventional 3D image segmentation methods are used, then depth region identification is achieved, but processing speed decreases due to high computational requirements
Solution Approach 1:
The patent segments 3D image data into multiple views and processes local features in parallel across views. This segmentation approach enables real-time processing speed while maintaining accurate depth region identification by distributing computational tasks across multiple processing streams.
Solution Approach 2:
The patent performs partial action by processing only local features rather than complete pixel-by-pixel analysis. This partial processing approach achieves sufficient depth region identification accuracy at speeds suitable for real-time applications.
3Use of energy by moving object
If local features are used instead of individual pixel analysis, then computational load is reduced, but segmentation detail may be lost
Solution Approach 1:
The patent segments 3D image data into multiple views and processes local features within each view. By maintaining multiple view segments and processing local features in each segment, the system reduces computational load while preserving segmentation detail through the combined information from all views.
Solution Approach 2:
The patent merges corresponding depth regions across multiple views based on spatial displacement and correlation values. This merging process combines information from local features across views to maintain segmentation detail while working with computationally efficient local feature representations.
Data Source
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AI summary
The present invention provides a method and an apparatus for real time object segmentation of 3D image data based on local feature correspondences between a plurality of views. In order to reduce the computational effort of object segmentation of 3D image data, the segmentation process is performed based on correspondences relating to local features of the image data and a depth map. In this way, computational effort can be significantly reduced and the image segmentation can be carried out very fast.