Stereo Vision Depth Computation via Shape Interval Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Stereo vision systems face challenges in achieving high accuracy and speed due to computational complexity, particularly in resource-limited systems such as low-power devices, where the correspondence problem is exacerbated by occlusions and slanted surfaces, making it difficult to obtain real-time three-dimensional information.
Innovation Solution
The method involves filtering stereo images to obtain intensity profiles, identifying and pairing peaks, determining shape intervals, calculating disparity offsets, and smoothing the disparity map, which enables efficient and accurate depth computation even in resource-constrained environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional stereo vision algorithms are used to match pixels between left and right images, then depth information can be obtained, but the computational complexity increases significantly, making real-time processing difficult in resource-limited systems
Solution Approach 1:
The patent segments the image processing task by dividing images into superpixels (groups of pixels) and further into shape intervals. Instead of matching individual pixels across the entire image, the algorithm matches shape intervals defined by boundary pixels, reducing the number of comparisons needed while maintaining depth measurement accuracy through hierarchical processing.
Solution Approach 2:
The patent extracts only the essential features needed for depth calculation by identifying boundary pixels and shape intervals that define object contours. By taking out and matching only these critical shape-defining elements rather than all pixels, the computational complexity is reduced while preserving the ability to calculate accurate disparity and depth information.
2Measurement precision
If comprehensive pixel matching is performed to handle occlusions and slanted surfaces, then measurement accuracy improves, but processing speed decreases
Solution Approach 1:
The patent performs preliminary actions by first identifying boundary pixels and defining shape intervals before conducting the actual matching process. By pre-processing the images to extract shape characteristics and organizing pixels into meaningful groups, the algorithm reduces the search space for matching while ensuring that occlusions and slanted surfaces are handled through the shape-based correspondence approach.
Solution Approach 2:
The patent changes the matching parameters from individual pixel intensity comparisons to shape interval boundary matching. By transforming the problem from pixel-level to shape-level parameters, the algorithm achieves robustness against occlusions and slanted surfaces while maintaining processing efficiency through reduced computational complexity.
3Productivity
If more computational resources are allocated to stereo vision processing, then accuracy and speed improve, but system weight, size, and power consumption increase
Solution Approach 1:
The patent implements self-service by using the image data itself to guide the processing - shape intervals are derived from the image content, and matching is performed only on relevant shape-defining pixels. This self-organizing approach allows the algorithm to adapt to different scenes without requiring additional computational resources or external assistance, achieving real-time processing on resource-constrained devices.
Solution Approach 2:
The patent applies partial action by performing matching only on boundary pixels and shape intervals rather than all pixels in the image. This selective processing provides sufficient depth information for most applications while dramatically reducing computational requirements, allowing deployment on low-power devices without sacrificing essential functionality.
Data Source
AI summary
A method for stereo vision may include filtering a row or column in a stereo image to obtain intensity profiles, identifying peaks in the intensity profiles, pairing peaks within a maximum disparity distance, determining a shape interval for peak pairs, selecting a peak pair with a maximum shape interval, determining a disparity offset for the peak pairs, extending shape intervals to include all pixels in the intensity profiles, computing depths or distances from disparity offsets, and smoothing the stereo image disparity map along a perpendicular dimension. Another method for stereo vision includes filtering stereo images to intensity profiles, identifying peaks in the intensity profiles, pairing peaks within a maximum disparity distance, determining shape intervals for peak pairs, and selecting peak pairs with the maximum shape interval. Apparatus corresponding to the above methods are also disclosed herein.