Background Subtraction Using Focus Differences
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Solution Overview
Problem
Current image analysis methods lack an efficient and simple way to distinguish background from foreground, leading to increased processing load and complexity when analyzing images with significant background elements.
Innovation Solution
A method and apparatus that utilize images captured with different focal lengths to compare relative focus, categorizing element sets as background, midground, or foreground based on focus relationships, allowing for the subtraction of background elements from further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all elements in the image are analyzed, then complete image analysis is achieved, but processing load increases
Solution Approach 1:
The patent segments the image into multiple element sets and categorizes them by depth (foreground, midground, background) using focus analysis. This segmentation allows selective processing where background elements can be subtracted or processed with lower priority, reducing overall processing load while maintaining analysis completeness for important foreground elements.
Solution Approach 2:
The patent extracts and identifies background element sets from the full image using focus comparison techniques. By separating background elements from foreground elements, the system can exclude or minimize processing of background portions, thereby reducing processing load while preserving complete analysis capability for relevant foreground content.
2Measurement precision
If focus comparison between multiple images is performed, then background identification accuracy improves, but device complexity increases
Solution Approach 1:
The patent employs a single image capture device that takes multiple images at different focal lengths, making the device multi-functional. The same device performs both foreground and background imaging, and the processing system handles multiple images through a unified element set comparison framework. This universal approach improves background identification accuracy without requiring separate specialized devices for each function.
Data Source
AI summary
First and second images are captured at first and second focal lengths, the second focal length being longer than the first focal length. Element sets are defined with a first element of the first image and a corresponding second element of the second image. Element sets are identified as background if the second element thereof is more in-focus than or as in-focus as the first element. Background elements are subtracted from further analysis. Comparisons are based on relative focus, e.g. whether image elements are more or less in-focus. Measurement of absolute focus is not necessary, nor is measurement of absolute focus change; images need not be in-focus. More than two images, multiple element sets, and/or multiple categories and relative focus relationships also may be used.