Hybrid Depth-Color Segmentation for Image Processing
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Solution Overview
Problem
Conventional background-foreground segmentation methods are computationally heavy and inaccurate, especially when dealing with images where the background and foreground have similar colors or when depth data is noisy, leading to inconsistent results.
Innovation Solution
A hybrid approach that uses depth data to generate a coarse foreground mask without color data, followed by high-resolution color-based refinement at the border between segments, reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color-based segmentation is used to separate background and foreground, then segmentation can be performed on pixel-by-pixel basis, but the process becomes computationally heavy and time consuming with very large resolutions
Solution Approach 1:
The patent divides the segmentation task into two distinct phases: a coarse segmentation phase using depth data at low resolution, and a refinement phase using color data only at the border region. This segmentation of the processing task reduces the computational burden while maintaining accuracy.
Solution Approach 2:
The patent applies different segmentation strategies to different regions of the image. Depth-based coarse segmentation is applied to the entire image at low resolution, while color-based refinement is applied only to the border region at high resolution. This local differentiation optimizes computational resources.
2Productivity
If color-based segmentation is used, then segmentation can be performed on entire image area, but it becomes inaccurate when background and foreground have the same colors or when color patterns have strong color differences
Solution Approach 1:
The patent segments the segmentation process into two stages: first using depth data for coarse segmentation (which is accurate regardless of color similarities), then using color data only for refining the border region. This resolves the contradiction by using the appropriate data type for each stage.
Solution Approach 2:
The patent uses depth data as an intermediary to establish the initial segmentation framework, which then guides where color-based refinement is needed. The depth-based coarse mask acts as a mediator that prevents color-based methods from failing on color-similar regions.
3Productivity
If depth data is used for background-foreground segmentation, then computational load is reduced due to smaller resolution, but the results become inconsistent and inaccurate when depth data is noisy or when clear relationship between color and depth does not exist
Solution Approach 1:
The patent segments the reliance on depth data by using it only for coarse segmentation at low resolution, then switching to color-based refinement at the border. This reduces the impact of depth noise on final accuracy while maintaining the computational efficiency benefits.
Solution Approach 2:
The patent applies high-resolution color-based refinement specifically at the border region where depth data is most critical for accuracy, while using low-resolution depth-based segmentation for the rest of the image. This local quality differentiation optimizes both speed and accuracy.
4Device complexity
If weighted combination of color and depth data is used to determine background or foreground, then segmentation can be performed, but the combination is inconsistent and inaccurate since it is an artificial combination where clear relationship between color and depth does not necessarily exist
Solution Approach 1:
The patent segments the data fusion approach by using depth data for coarse segmentation and color data for refinement, avoiding the need for complex weighted combinations. This separation of concerns eliminates the artificial combination problem while maintaining segmentation capability.
Solution Approach 2:
The patent performs preliminary segmentation using depth data before applying color-based refinement. This preliminary action establishes a framework that guides the subsequent refinement process, eliminating the need for complex simultaneous fusion of both data types.
Data Source
AI summary
Techniques for a system, article, and method of background-foreground segmentation for image processing may include obtaining pixel data including both non-depth data and depth data for at least one image, where the non-depth data includes color data or luminance data or both and associated with the pixels; determining whether a portion of the image is part of a background or foreground of the image based on the depth data and without using the non-depth data; and determining whether a border area between the background and foreground formed by using the depth data are part of the background or foreground depending on the non-depth data without using the depth data.