Polygon Tracing via Morphological Dilation and ML Filtering
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Solution Overview
Problem
Existing automated methods for tracing polygons in design files are limited by human error, observer bias, and computational intensity, particularly in handling 'noisy' files with incomplete polygon tracing and small inaccuracies, and lack integration of user input with machine learning for error checking and optimization.
Innovation Solution
A system and method that extracts vector imagery, filters lines to create simplified line art, uses morphological dilation to generate polygonal approximations, identifies salient polygons through geometric parameters, and employs machine learning models to detect candidate regions, suggest modifications, and validate improvements, thereby reducing human error and enhancing polygon tracing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated methods like doubly connected edge list are used to trace polygons from lines, then the process is faster than manual tracing, but small gaps in polygons remain unfilled leading to incomplete polygons
Solution Approach 1:
The system performs preliminary morphological dilation on the line segments before polygon tracing to proactively fill small gaps and connect broken boundaries. This preliminary action ensures that the DCEL algorithm receives pre-processed, more complete line data, eliminating the gap-filling limitation without sacrificing tracing speed.
Solution Approach 2:
Morphological dilation acts as an intermediary processing step between the raw line segments and the DCEL polygon tracing algorithm. This intermediary operation transforms the input data to be more suitable for the tracing algorithm, bridging the gap between incomplete line data and complete polygon output.
2Manufacturing precision
If morphological image processing (erosion) is applied to fill gaps in polygons, then polygon completeness improves, but computational intensity increases significantly
Solution Approach 1:
The system applies morphological dilation with controlled parameters (structuring element size, number of iterations) to achieve sufficient gap filling without excessive computational effort. By using partial action (limited dilation strength) rather than aggressive erosion, the system achieves acceptable polygon completeness while maintaining computational efficiency.
3Measurement precision
If parallel lines that are narrowly separated are processed by DCEL, then detailed diagram components are captured, but superfluous sliver polygons are created not intended by the original creator
Solution Approach 1:
The system applies local quality filtering where different regions of the traced polygons are evaluated differently. Salient polygons that represent meaningful diagram components are preserved, while sliver polygons in regions with parallel lines are identified and removed based on local geometric properties and area thresholds.
Solution Approach 2:
The system discards superfluous sliver polygons generated by DCEL through geometric parameter filtering, while recovering and preserving salient polygons that represent intended diagram components. This selective discarding and recovering process eliminates harmful artifacts while maintaining useful information.
4Manufacturing precision
If manual polygon tracing is performed to ensure accuracy, then human error and observer bias are minimized, but the process becomes time consuming
Solution Approach 1:
The system implements feedback through geometric parameter filtering and salient polygon identification that automatically corrects common tracing errors. The feedback mechanism evaluates traced polygons against geometric criteria and refines the output, achieving accuracy comparable to manual review without the time cost.
Solution Approach 2:
The automated tracing system performs self-correction through geometric parameter validation and filtering, eliminating the need for manual review. The system serves itself by automatically identifying and correcting errors in polygon tracing, achieving both speed and accuracy.
5Measurement precision
If small polygons are generated due to small inaccuracies in drawn line segments, then complete geometric information is captured, but the output contains noise not intended by the original diagram
Solution Approach 1:
The system changes geometric parameters (area thresholds, aspect ratio criteria, perimeter-to-area ratios) to distinguish between meaningful small polygons and noise polygons. By adjusting these parameters, the system filters out noise while preserving legitimate small geometric features in the diagram.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively minimizes human error and observer bias by automatically tracing and improving polygons, providing accurate polygon boundaries with reduced computational intensity and integrating user input for validation, leading to improved polygon fidelity and efficiency.
Implementation Method 1
morphologically dilating the simplified line art to generate a polygonal approximation
Implementation Method 2
calculating geometric difference between the source file canvas bounds and the polygonal approximation to identify contracted polygons
Implementation Method 3
detecting, by a first machine learning model, candidate regions for salient polygon improvement, generating, by a second machine learning model, suggested modifications to salient polygon vertices
Data Source
AI summary
A system and method for tracing polygons in a drawing source file. The method includes extracting vector imagery from the source file, creating a planar representation of the vector imagery as a plurality of lines, filtering the plurality of lines to create simplified line art, morphologically dilating the simplified line art to generate a polygonal approximation, calculating geometric difference between the source file canvas bounds and the polygonal approximation to identify contracted polygons, morphologically dilating the contracted polygons to create visual polygons, and filtering the visual polygons according to one or more geometric parameters to identify salient polygons.