Image Segmentation Using Depth-Aware Superpixel Alignment
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Solution Overview
Problem
Current image segmentation methods face challenges in achieving precise segmentation, particularly in accurately distinguishing foreground from background due to inaccuracies in depth detection at image boundaries.
Innovation Solution
An image segmentation method and device that utilize superpixels and aligned depth maps to segment images based on depth information, averaging depth values to minimize boundary inaccuracies and enhance segmentation accuracy, while also allowing for user input to refine the segmentation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation methods are used, then the segmentation process is simple, but the segmentation precision deteriorates due to depth detection inaccuracies at image boundaries
Solution Approach 1:
The patent applies segmentation by dividing the image into superpixels as basic processing units. This allows the segmentation algorithm to operate on coherent regions rather than individual pixels, reducing boundary inaccuracies while maintaining computational efficiency. The superpixel segmentation serves as a preprocessing step that groups pixels with similar characteristics, improving subsequent depth-based segmentation precision.
Solution Approach 2:
The patent introduces depth information as an additional dimension to enhance 2D image segmentation. By incorporating the depth map and considering 3D spatial relationships (x, y, depth), the method achieves more accurate foreground-background separation. The depth dimension provides extra discriminatory power at boundaries where 2D color information alone is insufficient.
2Measurement precision
If depth information is used for segmentation, then segmentation accuracy is improved, but the processing complexity increases due to depth map alignment and superpixel generation
Solution Approach 1:
The patent performs preliminary actions by generating superpixels and aligning the depth map to superpixel boundaries before executing the main segmentation algorithm. The depth map is resampled and averaged within each superpixel region in advance, which simplifies subsequent processing and reduces computational complexity during the actual segmentation phase. This preprocessing ensures that depth values are already organized in a structured manner.
Solution Approach 2:
The patent merges color information from the 2D image with depth information from the depth map within each superpixel region. By combining these different types of information (color + depth) at the superpixel level, the method achieves more robust segmentation while reducing the overall processing complexity compared to handling individual pixels separately.
3Reliability
If depth values are averaged within superpixels, then boundary inaccuracies are reduced, but loss of detailed depth information occurs
Solution Approach 1:
The patent applies local quality by treating different regions of the image differently. Within each superpixel, depth values are averaged to provide a representative value for that region, improving boundary reliability. However, the original detailed depth information is preserved in the input depth map and can be referenced when needed, allowing the system to maintain both summarized and detailed information as required by different processing stages.
Data Source
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AI summary
An image segmentation method includes displaying, through a display component, an image; receiving, through a user input component, a user command relative to an original designation region of the image; shrinking the original designation region according to first depth information corresponding to the image to generate a shrunk designation region of the image; generating a plurality of seeds according to the shrunk designation region and the user command; and segmenting the image according to the seeds to obtain at least one segmented region.