3D Edge Detection Refinement Using Stacked Slice Features
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Solution Overview
Problem
Current edge detection technologies are limited to two-dimensional images and lack effective methods for accurate three-dimensional edge detection, which is crucial for tasks like semantic segmentation and object tracking.
Innovation Solution
A method involving three-dimensional edge refinement detection network that stacks and encodes two-dimensional object and edge detection results, utilizing a multi-stage encoder and decoder structure with mutual learning modules and atrous convolutions to enhance and optimize edge detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional edge detection methods are used, then the detection process is simple, but the detection accuracy for three-dimensional images is low
Solution Approach 1:
The patent transitions from two-dimensional edge detection to three-dimensional edge detection by processing multiple two-dimensional slices along the depth dimension. The encoder-decoder network operates on three-dimensional data, stacking multiple two-dimensional detection results and performing three-dimensional convolution to capture spatial relationships in all three dimensions, thereby improving edge detection accuracy for three-dimensional images.
Solution Approach 2:
The patent employs a nested encoder-decoder structure where the encoder processes input features through multiple stages with increasing complexity, and the decoder reconstructs the output through corresponding stages. This nested architecture allows the system to progressively extract and refine three-dimensional edge features while maintaining manageable computational complexity at each stage.
2Measurement precision
If three-dimensional edge detection is implemented, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent divides three-dimensional image processing into multiple two-dimensional slice operations. By processing each slice independently through the encoder-decoder network and then aggregating results through three-dimensional convolution, the system reduces the computational burden compared to processing the entire three-dimensional volume at once, while still achieving accurate three-dimensional edge detection.
Solution Approach 2:
The patent performs multiple encoding and decoding operations on the same input data to extract features at different scales and representations. This partial repetition of processing stages allows the system to capture comprehensive three-dimensional edge information while distributing computational load across multiple passes rather than requiring a single computationally intensive operation.
3Loss of information
If multiple encoding and decoding stages are used, then the feature extraction is enhanced, but the processing time increases
Solution Approach 1:
The patent implements continuous feature extraction through the encoder-decoder network where each stage builds upon the previous stage's output without interruption. The multi-stage encoding progressively extracts features at different levels of abstraction, and the corresponding decoding stages continuously reconstruct the feature maps, ensuring that useful information is preserved and refined throughout the entire processing pipeline without loss of critical edge features.
Data Source
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AI summary
A three-dimensional edge detection method and apparatus, a computer-readable storage medium and a computer device. Said method comprises: acquiring two-dimensional object detection results and two-dimensional edge detection results of two-dimensional fragments of a three-dimensional image; stacking the two-dimensional object detection results into a three-dimensional object detection result, and stacking the two-dimensional edge detection results into a three-dimensional edge detection result; performing encoding according to a feature map of the three-dimensional image, the three-dimensional object detection result and the three-dimensional edge detection result, so as to obtain an encoded result; and performing decoding according to the encoded result, the three-dimensional object detection result and the three-dimensional edge detection result, so as to obtain an optimized three-dimensional edge detection result of the three-dimensional image.