Mobile Color Measurement via Variable Intensity Imaging
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Solution Overview
Problem
Current methods for color measurement using mobile phones are unreliable due to environmental lighting conditions and require additional equipment, making them inconvenient and costly, especially for irregularly shaped surfaces.
Innovation Solution
A color measurement device that captures images of a target surface under variable intensity, constant color light, using a trained model to infer surface color by processing a color feature tensor, allowing for accurate color measurement robust against environmental lighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile phone camera is used for color measurement, then accessibility and convenience are improved, but measurement reliability deteriorates due to environmental lighting influence
Solution Approach 1:
A trained machine learning model serves as an intermediary between the camera image data and the true surface color. The model learns to compensate for environmental lighting effects by processing images captured under various lighting conditions, thereby enabling reliable color measurement using the accessible mobile phone camera without requiring controlled lighting environments.
Solution Approach 2:
The system captures images under varying light intensities and uses a trained model to process these variable images. By capturing multiple images with different exposure settings and processing them through the trained model, the system can extract accurate color information that is invariant to environmental lighting conditions, thus maintaining reliability while using accessible mobile devices.
2Measurement precision
If additional equipment is used for mobile phone color measurement, then measurement accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The mobile phone itself performs the color measurement function through its existing camera and processing capabilities. The trained machine learning model enables the phone to automatically compensate for lighting conditions and extract accurate color information without requiring external equipment such as colorimeters or controlled lighting setups, thereby maintaining measurement precision while eliminating additional device complexity.
3Reliability
If additional equipment is used to control lighting, then color measurement reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The trained machine learning model acts as an intermediary that processes images captured under uncontrolled environmental lighting and extracts accurate color information. This eliminates the need for users to manually control lighting or set up controlled environments, maintaining measurement reliability while significantly improving ease of operation.
Solution Approach 2:
The machine learning model is pre-trained on diverse lighting conditions before deployment. This preliminary training enables the model to automatically handle various environmental lighting scenarios without requiring real-time lighting control or user intervention, thereby maintaining reliability while improving operational convenience.
4Measurement precision
If multiple images under variable light intensity are captured, then color measurement accuracy is improved, but measurement time increases
Solution Approach 1:
The system captures multiple images with different exposure settings to ensure accurate color measurement. By capturing a limited set of images with varying light intensities and processing them through the trained model, the system achieves accurate color extraction without requiring excessive imaging time, balancing precision and time efficiency.
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 and reliable color measurement using mobile devices, eliminating the need for additional equipment and overcoming the limitations of environmental lighting, thus allowing mobile phones to be used as effective color-measuring tools.
Implementation Method 1
capturing a plurality of images of the target surface as the target surface is illuminated with a variable intensity, constant color light source
Implementation Method 2
capturing a plurality of images of the target surface... determining, from image data included in the plurality of image
Data Source
AI summary
Methods and systems for determining a surface color of a target surface under an environment with an environmental light source. A plurality of images of the target surface are captured as the target surface is illuminated with a variable intensity, constant color light source and a constant intensity, constant color environmental light source, wherein the intensity of the light source on the target surface is varied by a known amount between the capturing of the images. A color feature tensor, independent of the environmental light source, is extracted from the image data, and used to infer a surface color of the target surface.