Raster Image Region Merging for Vector Tracing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for tracing raster images using computer tools often result in inefficiencies such as 'gapping' and 'beading' due to manual handling of polygons and auto-tracing algorithms, which require extensive manual correction and are time-consuming and costly for professionals in the digital arts.
Innovation Solution
A method and system that rank regions by color similarity, automatically merge adjacent regions, and remove artifacts based on pixel thresholds, using a planar map to generate a curve-based representation of the raster image, thereby reducing manual intervention and improving tracing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tracing using Bézier curve tool is used, then each polygon can be precisely traced, but the process is inefficient and results in gapping and spacing between polygons
Solution Approach 1:
The system performs automatic tracing by identifying polygons and applying Bézier curves algorithmically without requiring manual intervention for each polygon. The computer system serves itself by automatically processing the raster image and generating vector output, eliminating the need for manual tracing while maintaining precision through automated algorithms.
Solution Approach 2:
The manual mechanical process of dragging control handles and adjusting curves by hand is replaced with an automated algorithmic system that identifies polygons and generates Bézier curves through computational methods. This substitution of mechanical manual operations with automated computing processes resolves the contradiction between precision and efficiency.
2Productivity
If auto-tracing algorithm is used, then tracing efficiency is improved, but gapping between adjacent polygons still remains
Solution Approach 1:
The system performs post-processing operations that analyze the traced output and automatically close gaps between adjacent polygons. By implementing feedback mechanisms that detect and correct gapping issues in the generated vector data, the system maintains both high automation efficiency and polygon continuity precision.
Solution Approach 2:
The system performs preliminary actions during the tracing process to prevent gapping by ensuring proper connectivity between adjacent polygons before final output is generated. By proactively addressing potential gapping issues during the automated tracing process rather than correcting them afterward, the system maintains both efficiency and continuity.
3Extent of automation
If auto-tracing treats diagonally oriented regions as separate polygons, then processing is automated, but beading and visual distortion occur
Solution Approach 1:
The system merges diagonally oriented regions that should form continuous lines into single polygons rather than treating them as separate entities. By combining adjacent regions with similar visual characteristics into unified polygon structures, the system eliminates beading artifacts and maintains visual accuracy while preserving the benefits of automated processing.
Solution Approach 2:
The system adjusts tracing parameters and algorithms to specifically handle diagonally oriented regions, changing how these areas are identified and processed. By modifying the automated tracing parameters to recognize diagonal continuity patterns, the system prevents beading while maintaining high automation levels.
4Manufacturing precision
If manual correction of gaps and beaded polygons is performed, then tracing accuracy is improved, but labor expense and time consumption increase
Solution Approach 1:
The system performs self-correction by automatically detecting and fixing gaps and beading issues in the traced output without requiring manual intervention. The computer system serves itself by implementing automated post-processing operations that correct accuracy issues, eliminating the time-consuming manual correction process while maintaining high tracing accuracy.
Solution Approach 2:
The manual mechanical process of zooming in, identifying, and correcting gaps and beading by hand is replaced with automated computational algorithms that detect and repair these issues programmatically. This substitution eliminates the significant time loss associated with manual correction while preserving tracing accuracy through systematic automated processing.
Data Source
AI summary
Methods and apparatuses to merge and/or remove (e.g., edges, regions, vertices, etc.) in a planar map of a raster image are disclosed. In one embodiment, a method includes ranking a plurality of regions of a raster image in a scoring matrix based on color similarities between each of the plurality of regions; and automatically merging certain ones of the plurality of regions connected through at least one of an edge and a vertex based on the scoring matrix. In addition, the method may include merging other regions of the plurality of regions without considering the scoring matrix when the other regions are smaller than a threshold number of pixels and automatically removing artifacts and noise from the raster image based on the threshold number of pixels.