Stereo Depth Camera Array Real-Time Registration via Coefficient Segmentation
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Solution Overview
Problem
Real-time image registration in camera arrays is computationally intensive and typically requires specialized calibration targets, making it impractical for field use beyond initial factory settings.
Innovation Solution
Adjusting a selected subset of independent registration coefficients determined to have the largest contribution to registration errors, allowing for real-time registration maintenance without the need for specialized calibration targets by using images captured during normal usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full image registration is performed using all registration coefficients, then registration precision is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the full set of registration coefficients into two groups: a first subset of coefficients that are adjusted frequently (real-time or near-real-time), and a second subset that is adjusted less frequently (during calibration). This segmentation allows the system to maintain high registration precision by updating critical parameters frequently while avoiding the computational burden of adjusting all coefficients simultaneously, thus resolving the contradiction between precision and complexity.
Solution Approach 2:
Instead of performing complete registration adjustment using all coefficients in every frame, the patent applies partial action by adjusting only the first subset of coefficients (those with largest contribution to errors) in real-time. This partial adjustment is sufficient to maintain acceptable registration precision while dramatically reducing computational complexity compared to adjusting all coefficients.
2Measurement precision
If full image registration is performed computationally intensive processing is required, but processing time increases making it unsuitable for real-time field use
Solution Approach 1:
The patent divides the registration coefficients into two subsets with different update frequencies. The first subset (most impactful coefficients) is updated frequently using real-time image data, while the second subset is updated less frequently during calibration periods. This segmentation enables the system to maintain registration accuracy over time without requiring computationally intensive full-registration processing at every frame, thus reducing processing time loss.
Solution Approach 2:
The patent performs preliminary calibration to determine the full set of registration coefficients and identify which coefficients contribute most to registration errors. This preliminary analysis allows the system to pre-determine the optimal subset of coefficients for real-time adjustment, avoiding the need to process all coefficients during time-critical real-time operation.
3Measurement precision
If traditional registration methods are used, then comprehensive registration correction is achieved, but specialized calibration targets are required making field use impractical
Solution Approach 1:
The patent extracts and isolates the most critical registration coefficients (the first subset) that have the largest contribution to registration errors. By focusing adjustment efforts on this extracted subset rather than requiring complete recalibration with specialized targets, the system achieves effective registration correction in the field using only standard imaging capabilities, thus improving ease of operation and field deployability.
Solution Approach 2:
The system performs self-adjustment of registration coefficients using images captured during normal operation. By automatically identifying and adjusting the first subset of coefficients based on real-time image data without requiring external calibration targets or manual intervention, the camera array maintains registration accuracy through self-service, making field use practical and easy to operate.
Data Source
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AI summary
Real-time registration of a camera array in an image capture device may be implemented in the field by adjusting a selected subset of independent parameters in a mapping function, termed registration coefficients, which have been determined to have the largest contribution to registration errors so that the array can be maintained in its initial factory-optimized calibrated state. By having to adjust only a relatively small subset of registration coefficients from within a larger set of coefficients (which are typically determined using a specialized calibration target in a factory setting), far fewer matching patterns need to be identified in respective images captured by cameras in the array in order to correct for registration errors. Such simplified pattern matching may be performed using images that are captured during normal camera array usage so that registration may be performed in real-time in the field without the need for specialized calibration targets.