Color Assignment Using Relative Intensity Values
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing methods fail to effectively segment clusters of colors with similar visual appearance and assign appropriate colors to images, leading to unsatisfactory clarity and aesthetics due to dullness or poor color choices, and current methods for modifying colors are time-consuming and difficult.
Innovation Solution
A computer-implemented method and system that adjusts the minimum perceptible color difference (MPCD) and color acuity (CA) to segment and modify clusters of colors, allowing user confirmation and control over color selection and spectrum adjustment, using functions like linear, polynomial, or Gaussian functions to refine color assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional color modification methods are used, then color changes can be applied to images, but the process is time-consuming and difficult
Solution Approach 1:
The system automatically segments colors and generates color palettes without requiring manual user intervention. The computer implements the color segmentation and assignment processes autonomously, allowing the image processing system to serve itself rather than requiring continuous user control.
Solution Approach 2:
The system changes color parameters automatically by segmenting the image into color clusters and assigning new colors from generated palettes. This transforms the manual parameter adjustment process into an automated parameter transformation process, improving efficiency and ease of operation.
2Manufacturing precision
If color segmentation is performed without proper clustering methods, then color assignment can be done, but the visual appearance and clarity are unsatisfactory
Solution Approach 1:
The image is segmented into distinct color clusters using algorithms that group pixels with similar colors. This segmentation preserves visual information by ensuring that pixels within each cluster maintain their relative color relationships, while enabling precise color assignment at the cluster level rather than individual pixel level.
Solution Approach 2:
The system transforms color space parameters to identify and segment color clusters, then applies parameter changes by assigning new colors from generated palettes. This maintains the structural integrity of color relationships while improving visual appearance and clarity.
3Measurement precision
If manual color selection is used, then color accuracy can be controlled, but the process is difficult and time-consuming
Solution Approach 1:
The computer automatically generates color palettes and assigns colors to clusters without requiring manual user selection. The system performs color analysis, cluster identification, and palette generation autonomously, eliminating the time-consuming manual color selection process while maintaining precision through algorithmic color matching.
Solution Approach 2:
The manual mechanical process of color selection is replaced with an automated computational system that uses algorithms to analyze colors, segment clusters, and generate palettes. This substitution of manual operation with automated processing maintains color selection accuracy while dramatically reducing the time required.
4Productivity
If color clusters are not properly segmented, then color assignment can be performed quickly, but the visual appearance becomes dull and unattractive
Solution Approach 1:
The system implements automated color cluster segmentation that groups pixels by color similarity before assignment. This segmentation occurs rapidly through computational algorithms, maintaining both speed and quality by identifying natural color groupings in the image that preserve visual appearance while enabling efficient batch color assignment.
Solution Approach 2:
The system performs parameter changes by transforming color space representations to identify clusters, then applying color assignments based on cluster characteristics. This automated parameter transformation maintains segmentation quality while improving productivity through efficient computational processing.
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
A method and system for assigning colours to an image or part thereof, the method comprising: selecting a sequence of colours for assignment to the image or part thereof; determining a minimum intensity I MIN within the image or part thereof; determining a maximum intensity I MAX within the image or part thereof; and determining relative intensity values RlV(i) for each pixel or voxel i according to; (I) where I(i) is an intensity of pixel or voxel /, and f is a preselected function (such as to re-arrange the normalized values); and assigning colours to at least some pixels in the image or part thereof based on the relative intensity values and an order of each of the colours in the sequence.