Light Source Estimation via Spectral Pixel Grouping
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Solution Overview
Problem
Existing imaging technologies require manual specification of pixels with equal material components to estimate the light source, making the process cumbersome and impractical for real-time image capture.
Innovation Solution
A method that assumes a given spectral energy distribution of a light source and determines the likelihood of its correctness by grouping neighboring pixel data in a predetermined space, using a set generated by the specular reflection component, without requiring manual specification of material components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual specification of pixels with equal material components is performed to estimate light source, then measurement precision of light source estimation is improved, but device complexity and operation time increase significantly
Solution Approach 1:
The system automatically identifies and groups pixels with similar material components by analyzing spectral reflectance characteristics, eliminating the need for manual specification. The algorithm self-determines which pixels belong to the same material group based on their spectral signatures, enabling autonomous light source estimation without user intervention in pixel selection.
Solution Approach 2:
The invention transforms the manual pixel specification process into an automated parameter-based grouping process. By changing from manual selection to automated spectral analysis, the system uses mathematical parameters (spectral reflectance values) to automatically group pixels, reducing operational complexity while maintaining estimation accuracy.
2Measurement precision
If manual specification of pixels with equal material components is required, then light source estimation accuracy is improved, but productivity decreases due to cumbersome processing
Solution Approach 1:
The system performs automated pixel grouping based on spectral characteristics, eliminating manual intervention. The algorithm automatically identifies pixels with similar material components and groups them, enabling rapid light source estimation without the time-consuming manual process required by prior methods.
Solution Approach 2:
The system pre-processes image data by automatically identifying and grouping pixels with similar spectral reflectance characteristics before performing light source estimation. This preliminary automated grouping action eliminates the need for manual pixel specification during the main processing task, significantly improving productivity.
3Measurement precision
If the specular method is used to estimate light source, then measurement precision is improved, but ease of operation deteriorates due to requirement for manual pixel specification
Solution Approach 1:
The system automatically performs the pixel grouping function that was previously manual in the specular method. By using automated spectral analysis to identify and group pixels with similar material components, the system maintains the accuracy benefits of the specular method while completely eliminating the operational complexity and inconvenience of manual pixel specification.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate estimation of the light source used during image capture without manual specification of pixels with equal material components, improving efficiency and practicality in image processing.
Implementation Method 1
the reflectance of an object is divided into two components, a diffuse reflection component (material component) for reflecting each color by the inherent spectral reflectance of the object and a specular reflection component (specular component)
Implementation Method 2
distribution holding means for holding a spectral energy distribution of a specific light source as a specular reflection component
Data Source
AI summary
A light source estimating device includes: a distribution holding section holding a spectral energy distribution of a specific light source as a specular reflection component; a set generating section generating a set determined by the specular reflection component and pixel data of the input image in a predetermined space, with respect to each pixel data of the input image; a detecting section detecting, with respect to each pixel data of the input image, another pixel data neighboring the pixel data and included in the set; and a determining section determining whether or not the specific light source corresponds to the light source used during capture, by setting a likelihood that the specific light source corresponds to the light source used during capture high if the other pixel data exists, and setting a likelihood that the specific light source corresponds to the light source used during capture low if the other pixel data does not exist.