Raster-to-Vector Conversion Using Color Scale Curve Fitting
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Solution Overview
Problem
Conventional systems struggle to efficiently convert raster images with color scales to vectors due to non-linear color gradients, requiring tedious and inaccurate visual inspections, especially for users with visual impairments.
Innovation Solution
A method and system that utilize a trained model to segment color channel values, apply polynomial curve fitting, and generate vectors with geometric shapes and values based on pixel colors, enabling efficient conversion of raster images to vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection is used to determine pixel color values, then conversion accuracy may be maintained, but the process becomes difficult and inefficient
Solution Approach 1:
The patent replaces the manual visual inspection process with an automated computer-based image processing system. The system uses algorithms to automatically analyze pixel colors, compare them against the color scale, and extract corresponding values, thereby maintaining accuracy while dramatically improving conversion efficiency and eliminating the need for tedious manual inspection
Solution Approach 2:
The patent introduces a color scale as an intermediary reference between the raster image and the target values. This color scale serves as a standardized mapping tool that enables the automated system to accurately translate pixel colors into meaningful quantitative values, resolving the difficulty of direct visual interpretation
2Measurement precision
If color scale with non-linear gradient is used, then more accurate representation is achieved, but conversion complexity increases
Solution Approach 1:
The patent replaces complex manual analysis of non-linear color gradients with automated image processing algorithms. The computer system efficiently handles the mathematical transformations required for non-linear color spaces, converting pixel values to standardized color models and extracting quantitative data without requiring users to understand the underlying complexity
Solution Approach 2:
The patent transforms the complex non-linear color gradient into a standardized parameter system by comparing pixel colors against a defined color scale with known value mappings. This parameter transformation approach converts the complex visual information into simple, extractable numerical values while preserving the non-linear relationship accuracy
3Loss of information
If raster image is used, then detailed visual information is preserved, but resolution independence is lost
Solution Approach 1:
The patent performs preliminary extraction of quantitative information from the raster image before any potential resizing operations. By converting the raster image to a vector representation with extracted numerical values, the system preserves both the visual information (through the original raster) and enables resolution independence (through the extracted vector data that can be applied to images at any scale)
Solution Approach 2:
The patent creates a vector-based copy of the quantitative information from the raster image. Instead of working directly with the resolution-dependent raster data, the system extracts and represents the essential information in a vector format that is resolution-independent, allowing the data to be used across different scales and formats
Data Source
AI summary
Systems and methods for converting a raster image with a corresponding color scale into a plurality of vectors are provided. An example method includes receiving the raster image and the color scale. In some embodiments, the color scale includes a plurality of colors and a plurality of unit values. In certain embodiments, each color of the plurality of colors corresponds to a unit value of the plurality of unit values. In some embodiments, the raster image includes a plurality of pixels each corresponding to a pixel color. In certain embodiments, each color of the plurality of colors is segmented into a plurality of color channel values. In some embodiments, a model is trained to convert a color to a vector value based on the plurality of segmented color channel values for each color of the plurality of colors and the plurality of unit values. In certain embodiments, the plurality of vectors are generated and each include a vector location, a geometric shape, and a vector value.