Image Distance Segmentation Using Color Boundaries
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Solution Overview
Problem
Existing imaging systems face accuracy issues in distance measurement due to subjects with low contrast or high noise levels, leading to erroneous correlation evaluations and reduced distance accuracy.
Innovation Solution
An image processing apparatus that utilizes a CMOS image sensor with a photoelectric conversion function and an imaging surface phase difference method, combined with a millimeter wave radar, to enhance distance measurement accuracy by detecting boundaries based on color differences and suppressing distance errors through region-based classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distance information is calculated for the entire image at once, then processing efficiency is maintained, but measurement precision deteriorates due to distance variation across different regions
Solution Approach 1:
The image is divided into multiple divisional regions based on color information, and each region is further subdivided into sub-regions separated by boundaries detected through gradient calculations. This segmentation allows distance information to be calculated separately for each sub-region, improving measurement precision by accounting for local distance variations while maintaining manageable processing complexity through systematic division.
2Measurement precision
If the image is divided into multiple regions for accurate distance measurement, then measurement precision is improved, but processing time increases
Solution Approach 1:
Color information is extracted and used to divide the image into divisional regions before distance information calculation. Boundaries are detected in advance using gradient calculations, and sub-regions are predetermined based on these boundaries. This preliminary action organizes the processing structure upfront, allowing efficient parallel or sequential processing of each sub-region, thereby reducing overall processing time while maintaining high measurement precision.
3Measurement precision
If distance information is calculated without considering color boundaries, then processing simplicity is maintained, but measurement precision deteriorates at region boundaries
Solution Approach 1:
The patent applies different processing approaches to different regions based on their local characteristics. Boundaries are detected by calculating gradient magnitudes and identifying transitions between different color regions. Within each sub-region separated by these boundaries, distance information is calculated with local optimization, ensuring high measurement precision at boundaries while managing complexity through localized processing strategies.
Data Source
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AI summary
An image processing apparatus capable of reducing the influence of a distance variation includes a boundary detection unit configured to detect a boundary in each divisional region obtained by dividing image information into a plurality of divisional regions on the basis of color information of each pixel of the image information, and a combined distance information determination unit configured to determine combined distance information for each sub-region separated by the boundary in the divisional region on the basis of distance information of each pixel of the image information.