Semantic Vectorization of Raster Objects for Accurate Path Generation
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Solution Overview
Problem
Conventional techniques for converting raster images to vector objects are inefficient, inaccurate, and require significant manual user interaction, leading to errors and inefficient use of computational resources.
Innovation Solution
Semantic vectorization techniques use machine learning-based semantic classification models to identify semantic objects in raster images, generating semantically relevant vector objects by parsing pixels into clusters and creating paths around these clusters, reducing manual interaction and improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to convert raster images to vector objects, then the conversion process can be performed, but the accuracy and relevance of generated vector objects deteriorate
Solution Approach 1:
The system performs automatic semantic vectorization of raster images to generate vector objects without requiring manual user interaction. The semantic segmentation model automatically identifies and converts objects, eliminating the need for users to manually trace or edit vector paths, thus achieving both high accuracy and automation.
Solution Approach 2:
The patent replaces manual mechanical tracing operations with an automated machine learning-based semantic segmentation system. Instead of users manually creating vector paths, the system uses neural networks to automatically segment and convert raster images into semantically accurate vector objects.
2Reliability
If manual editing of vector objects is performed, then the quality of vector objects can be improved, but the time and computational resources required increase
Solution Approach 1:
The system generates high-quality vector objects automatically through semantic segmentation, eliminating the need for manual editing. The neural network model inherently produces accurate vector representations directly from raster images, achieving both high reliability and time efficiency.
Solution Approach 2:
The semantic segmentation model performs preliminary analysis of the raster image to automatically identify objects, their boundaries, and semantic meanings before vector generation. This preliminary action ensures high-quality vector output is achieved directly without requiring subsequent manual editing.
3Productivity
If conventional vector generation techniques are used, then vector objects can be created, but computational resources are wasted due to inefficient processing
Solution Approach 1:
The patent replaces inefficient conventional vector generation algorithms with a machine learning-based semantic segmentation system. The neural network model processes raster images more efficiently by directly generating semantically meaningful vector objects, reducing computational waste associated with manual editing and rework.
Data Source
AI summary
Semantic vectorization techniques are described that support generating and editing of vector objects from raster objects. A raster object, for instance, is received as an input by a semantic vectorization system. The raster object is utilized by the semantic vectorization system to generate a semantic classification for the raster object. The semantic classification identifies semantic objects in the raster image. The semantic vectorization system leverages the semantic classification to generate vector objects. As a result, the vector objects resemble the semantic objects in the raster object.