Adaptive Exposure Bracketing for Chromaticity Maximization
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Solution Overview
Problem
Existing imaging systems fail to maintain color consistency across rapidly changing lighting conditions, as they are limited by biological color constancy mechanisms and cannot adapt quickly enough for high-end machine vision applications, and they lack the ability to synthetically predict images at high frame rates with varying exposures.
Innovation Solution
The implementation of a camera response function to maximize chromaticity and ensure hue consistency through synthetic image formation, auto-exposure bracketing, and illuminant estimation, allowing for robust color consistency across different lighting conditions by computing and adjusting exposure settings in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If biological color constancy mechanisms are used, then color perception is maintained under varying lighting, but adaptation speed is too slow for high-end machine vision applications
Solution Approach 1:
The patent replaces biological color constancy mechanisms with a computational camera response function-based system. Instead of relying on slow biological adaptation, the system uses mathematical models to predict chromaticity and compute exposure settings instantly, achieving both color consistency and high speed adaptation required for machine vision applications.
Solution Approach 2:
The system dynamically adjusts exposure parameters based on computed chromaticity values from the camera response function. By changing exposure settings in real-time based on mathematical predictions rather than biological adaptation, the system achieves rapid parameter optimization while maintaining color accuracy under varying lighting conditions.
2Productivity
If traditional imaging systems are used, then simple capture is possible, but color consistency across rapidly changing lighting conditions cannot be maintained
Solution Approach 1:
The patent computes the camera response function and predicts chromaticity values in advance for different exposure settings. This preliminary computational action allows the system to determine optimal exposure parameters before capturing each frame, enabling high frame rates while ensuring color consistency is maintained across rapidly changing lighting conditions.
3Measurement precision
If exposure settings are adjusted manually, then color accuracy can be optimized, but real-time adaptation to lighting changes is not possible
Solution Approach 1:
The system uses the camera response function to continuously monitor lighting conditions and automatically adjusts exposure settings based on computed chromaticity predictions. This closed-loop feedback mechanism maintains color accuracy in real-time without manual intervention, eliminating the trade-off between precision and response time.
Solution Approach 2:
The imaging system autonomously computes optimal exposure parameters using the camera response function and chromaticity predictions. The system serves itself by automatically adapting to lighting changes without requiring manual adjustment, achieving both high color accuracy and rapid response time simultaneously.
Data Source
AI summary
A method, system and computer program are provided that present a real-time approach to Chromaticity maximization to be used in image segmentation. The ambient illuminant in a scene may be first approximated. The input image may then be preprocessed to remove the impact of the illuminant, and approximate an ambient white light source instead. The resultant image is then choma-maximized. The result is an adaptive Chromaticity maximization algorithm capable of adapting to a wide dynamic range of illuminations. A segmentation algorithm is put in place as well that takes advantage of such an approach. This approach also has applications in HDR photography and real-time HDR video.