Stereo Disparity Estimation via Histogram Segmentation
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Solution Overview
Problem
In stereoscopic imaging, determining accurate and reliable disparity values for objects is challenging due to issues like occlusions, perspective deformations, and varying texture, leading to inconsistent accuracy and reliability in depth estimates, which can cause visual discomfort and fatigue, especially for graphics and text placement in 3D environments.
Innovation Solution
A method and apparatus that analyze a stereoscopic image pair by determining an area of interest, building a histogram from disparity estimates within that area, searching for contiguous ranges with sufficient pixels, and selecting a robust disparity estimate based on confidence measures to ensure accurate and reliable depth information, thereby removing false estimates and enhancing the 3D effect while minimizing visual discomfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional stereo matching is used to determine disparity values, then depth information can be obtained, but the accuracy and reliability vary significantly due to occlusions, perspective deformations, and texture variations
Solution Approach 1:
The patent segments the disparity estimation process by dividing the image into multiple regions and analyzing histograms separately for each region. This allows local characteristics to be captured while filtering out global inconsistencies, thereby improving both accuracy and reliability of disparity estimates in different image areas
Solution Approach 2:
The patent implements a feedback mechanism where confidence values are computed for each disparity estimate and used to weight or filter subsequent estimations. Regions with low confidence (indicating occlusions or textureless areas) are identified and handled differently, improving the overall reliability of the disparity map
2Ease of manufacture
If graphics elements are placed close to video objects to maximize 3D effect, then visual impact is improved, but visual fatigue increases due to accommodation-vergence conflict
Solution Approach 1:
The patent replaces subjective visual comfort assessment with an automated computational system that uses disparity analysis and confidence evaluation to objectively determine optimal graphic element placement. This substitution of mechanical/physiological judgment with computational analysis enables precise control of depth positioning to maximize 3D effect while minimizing accommodation-vergence conflict
3Object-affected harmful factors
If a large safety margin is maintained in front of closest objects to avoid pop-out effect, then visual comfort is improved, but the 3D effect and immersion are reduced
Solution Approach 1:
The patent dynamically adjusts the safety margin parameter based on local confidence values and disparity reliability metrics. In high-confidence regions, smaller safety margins are applied to enhance 3D immersion, while in low-confidence regions (prone to pop-out effects), larger margins are automatically increased. This parameter adaptation resolves the contradiction between comfort and immersion
4Reliability
If histogram analysis is performed on the entire image to determine disparity values, then comprehensive coverage is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the image into multiple processing regions and performs histogram analysis independently on each region rather than on the entire image. This segmentation reduces the computational complexity of each individual histogram operation while maintaining comprehensive coverage through the aggregation of regional results, effectively resolving the contradiction between reliability and device complexity
Data Source
AI summary
The invention relates to a method and an apparatus for determining a disparity value for an object located in or to be placed into a stereoscopic image pair having an associated disparity map. First an area to be analyzed in one of the stereoscopic images is determined Then a histogram is built from disparity estimates of the associated disparity map that fall within the determined area. Subsequently a contiguous range of bins is searched in the histogram that also contains a sufficient number of pixels. Finally, a disparity estimate for the determined area is selected from the contiguous range of histogram bins.