Color Recognition via Illumination-Invariant Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing systems face challenges in accurately recognizing colors, especially under varying illumination conditions, due to the impact of luminance variations on color differentiation and recognition accuracy.
Innovation Solution
The implementation of a color cluster learning engine that uses a weighted distance metric to adjust luminance components in color space calculations, reducing their influence and enhancing the distinction between colors, thereby improving color recognition accuracy across different illumination levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard color space calculations are used, then computational simplicity is maintained, but color recognition accuracy deteriorates under varying illumination conditions
Solution Approach 1:
The patent transforms color space parameters by converting RGB values to a new color space where the third dimension represents saturation rather than luminance. This parameter transformation allows the system to maintain computational efficiency while improving color recognition accuracy under varying illumination conditions, as the transformed saturation channel is less sensitive to lighting changes
Solution Approach 2:
The patent applies preliminary color space transformation and clustering analysis to training images before actual color recognition tasks. By pre-processing and pre-clusterizing color data from training images, the system establishes a reference framework that speeds up subsequent color recognition while maintaining high accuracy across different illumination conditions
2Measurement precision
If luminance components are included in color calculations, then complete color information is captured, but differentiation between colors deteriorates under varying illumination
Solution Approach 1:
The patent segments color information into separate channels, specifically isolating the saturation channel from luminance variations. By segmenting the color representation in this manner, the system can analyze color characteristics without the confounding influence of illumination changes, thereby improving color differentiation while retaining essential color information
Solution Approach 2:
The patent applies different processing weights to different color channels based on their sensitivity to illumination variations. The saturation channel is given higher weight for color differentiation tasks, while luminance channels are downweighted or excluded in specific computational contexts, creating a localized optimization for color recognition accuracy
3Measurement precision
If clustering is performed without illumination consideration, then processing speed is maintained, but color cluster accuracy deteriorates under varying light conditions
Solution Approach 1:
The patent performs preliminary clustering analysis on training images to establish illumination-invariant color clusters before actual color recognition tasks. This pre-clustering creates a reference framework that can be quickly queried during operation, maintaining processing speed while ensuring accurate color cluster identification under varying illumination conditions
Solution Approach 2:
The patent introduces an intermediary clustering process that acts as a bridge between raw color data and final color recognition. This intermediary step pre-processes color information into illumination-resistant clusters, which then serve as efficient reference points for rapid color identification, balancing accuracy and processing speed
Data Source
AI summary
In some examples, a system includes a color cluster learning engine and a color recognition engine. The color cluster learning engine may be configured to obtain a set of training images, process the training images to obtain clusters of pixel colors for the training images, identify learned color clusters from the clusters of pixel colors obtained from the training images, and label the learned color clusters with color indicators. The color recognition engine may be configured to receive an input image for color identification, process the input image to obtain a particular cluster of pixel colors that covers the highest number of pixels in the input image, match the particular cluster to a particular learned color cluster labeled with a particular color indicator, and identify a color of the input image as specified by the particular color indicator.