Probabilistic Color Classification via Illumination Estimation
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately identifying colors of object components within images captured in various lighting environments, as color characteristics can vary significantly depending on the lighting source, making it difficult to determine object colors from RGB values alone.
Innovation Solution
A method and system that classify colors of object components within images by using a defined finite set of colors, employing zero-order and conditional probability distributions to determine the highest probability color classes for sub-regions, allowing for accurate color assignment even in unknown lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If color identification is performed using RGB values from images captured in various lighting environments, then the process is simple and direct, but the accuracy of color identification deteriorates due to lighting-induced color variations
Solution Approach 1:
The patent introduces an intermediary illumination estimation process that mediates between the raw RGB values and the final color identification. By estimating the illumination conditions and correcting the RGB values accordingly, the system bridges the gap between simple RGB reading and accurate color identification under varying lighting conditions
Solution Approach 2:
The patent transforms the color identification approach by changing parameters from direct RGB value usage to illumination-corrected color values. By estimating illumination characteristics and applying corrections to the RGB values, the system adapts the color parameters to account for lighting variations, thereby improving accuracy while maintaining operational simplicity
2Measurement precision
If illumination information is captured and stored with each image, then color identification accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system performs self-service by automatically estimating illumination conditions from the image data itself without requiring external illumination sensors or additional hardware. The illumination estimation is derived purely from analyzing the RGB values and color characteristics within the image, making the system self-sufficient and avoiding increased hardware complexity
Solution Approach 2:
The patent applies preliminary action by performing illumination estimation and color correction as preparatory steps before final color identification. By pre-processing the image data to estimate and compensate for illumination effects, the system prepares accurate color information in advance, improving downstream color identification without requiring complex real-time processing
3Measurement precision
If color values are corrected based on estimated illumination, then color accuracy in unknown lighting conditions improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing computational efforts on the most significant illumination correction factors rather than attempting to correct all possible color variations. By identifying and correcting only the dominant illumination effects that most impact color accuracy, the system achieves substantial improvement in color accuracy while limiting computational complexity to essential corrections only
Data Source
AI summary
Methods, systems, computer-readable media, and apparatuses for assigning a color class of a defined finite set of colors to at least one sub-region within a test image are presented. A plurality of sub-regions are identified within a test image. A first sub-region color value is determined for a selected first sub-region of the test image. Using the first sub-region color value and a plurality of zero-order probability distributions, a first color class of the defined finite set of colors is determined as a hypothesis color for the first sub-region. A second sub-region color value is determined for a selected second sub-region of the test image. Using the second sub-region color value and a conditional probability distribution conditioned on the hypothesis color for the first sub-region, a second color class of the defined set of colors is determined. The second color class is assigned to the second sub-region.