Image Segmentation for Simplified Raster-to-Vector Reconstruction
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Solution Overview
Problem
Existing systems for raster-to-vector conversion are inefficient, inflexible, and inaccurate, often requiring user configuration and resulting in complex outputs with unnecessary details, which impact graphics processing and are difficult to modify.
Innovation Solution
An image vectorization system that utilizes a segmentation approach to distinguish between smooth-shaded and high-frequency regions, generating clean segments and accurate vector images without user interaction by identifying non-overlapping sets of pixels with smoothing functions and color-based clustering, and merging regions based on color similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing raster-to-vector conversion systems are used, then vectorization can be performed, but the process is inefficient and produces complex outputs with unnecessary details
Solution Approach 1:
The patent segments the raster image into distinct regions based on color similarity and spatial proximity, creating simplified regions that correspond to smooth-shaded areas. This segmentation approach reduces the complexity of the vector output by eliminating unnecessary details while maintaining the essential structure of the image, thereby improving vectorization efficiency.
Solution Approach 2:
The patent extracts and removes noise, artifacts, and anti-aliasing issues from the image data during the vectorization process. By taking out these unwanted elements, the system produces cleaner vector outputs with fewer paths, significantly improving both efficiency and the simplicity of the resulting vector graphics.
2Manufacturing precision
If existing vectorization systems are used, then conversion can be achieved, but accuracy is reduced due to handling of noise and artifacts
Solution Approach 1:
The patent converts harmful factors such as noise, artifacts, and anti-aliasing issues into beneficial information by using them to identify region boundaries and smooth-shaded areas. Instead of simply removing these elements, the system leverages their spatial distribution and color characteristics to accurately delineate regions, thereby improving vectorization accuracy while maintaining clean vector outputs.
3Extent of automation
If existing conversion systems are used, then raster images can be vectorized, but user configuration is required reducing flexibility
Solution Approach 1:
The patent implements a self-service vectorization system that automatically detects image characteristics, determines region boundaries, and generates vector outputs without requiring user configuration. The system autonomously handles various image types and conditions, eliminating the need for user intervention while maintaining high accuracy and flexibility in the vectorization process.
4Measurement precision
If detailed vectorization is performed, then image fidelity is maintained, but the number of paths increases impacting graphics processing
Solution Approach 1:
The patent merges adjacent and similar regions into single vector paths by using color similarity and spatial proximity as merging criteria. This merging approach reduces the total number of vector paths while maintaining image fidelity, as regions that are visually similar are combined into unified paths, thereby improving graphics processing efficiency without sacrificing visual quality.
Data Source
AI summary
This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilizes a segmentation approach that distinguishes between smooth-shaded regions from high-frequency regions in an image within a vectorization pipeline to generate a vector image. For instance, the disclosed systems utilize a smoothing function to identify non-overlapping sets of pixels that include locally smooth pixels and pixels with high frequency details for an image. Furthermore, in some instances, the disclosed systems generate separate sets of fill functions (representing color-based regions) using color-based pixel clustering for the non-overlapping sets of pixels. Moreover, in one or more instances, the disclosed systems merge neighboring color-based regions in the sets of fill functions (using color similarity) to generate a set of segmented regions for an image. In some implementations, the disclosed systems utilize the set of segmented regions, from the image, to generate a vector image from the image.