3D Depth Image Feature Extraction via Level Set Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for extracting features from 3D depth images are complex and inefficient, particularly in high-speed calculations, and often fail to utilize 3D features effectively, especially when dealing with unknown image structures.
Innovation Solution
A feature extraction apparatus that generates level sets from depth images by dividing regions of interest into multiple levels along the X, Y, or Z axes, using a level set generator, and extracts features using a feature extractor that employs shape descriptors, allowing for invariant feature extraction with respect to rotation and depth direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calculation methods like RanSac are used for feature extraction, then precise feature extraction is achieved, but calculation speed decreases and constraints increase
Solution Approach 1:
The patent segments the depth image into multiple level sets based on depth thresholds, transforming a complex 3D feature extraction problem into multiple simpler 2D contour extraction problems. Each level set is processed independently to extract contours, which are then integrated to form the final feature representation. This segmentation approach reduces computational complexity while maintaining extraction precision.
Solution Approach 2:
The patent extracts only the essential depth information at specific threshold levels, rather than processing the entire 3D point cloud. By taking out specific level sets from the depth image and extracting contours from these simplified 2D representations, the method achieves fast calculation while preserving key structural features.
2Device complexity
If only depth information is used for contour extraction, then calculation is simplified, but 3D feature utilization is insufficient
Solution Approach 1:
The patent transforms the 3D depth information into multiple 2D level sets at different depth thresholds. By processing these 2D representations and integrating their contours, the method effectively utilizes 3D structural information while maintaining the simplicity of 2D contour extraction algorithms. This dimensionality transformation allows simple 2D processing to capture complex 3D features.
Data Source
AI summary
Provided is a feature extraction method and apparatus to extract a feature of a three-dimensional (3D) depth image. The feature extraction apparatus may generate a plurality of level sets using a depth image, and may extract a feature for each level depth image.