Semantic Vectorization for Raster Object 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 utilize machine learning-based semantic classification models to identify and generate semantically relevant vector objects from raster images by parsing pixels into clusters and generating paths around these clusters, reducing manual interaction and improving accuracy.
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 is poor and requires significant manual user interaction
Solution Approach 1:
The patent replaces manual mechanical operations with an automated machine learning-based semantic vectorization system. The system uses trained neural networks to automatically classify pixels into semantic objects and generate vector paths, eliminating the need for manual tracing and editing operations while achieving high conversion accuracy.
Solution Approach 2:
The system performs self-service by automatically generating vector objects from raster images through semantic classification. The machine learning model autonomously identifies objects, segments pixels, and creates vector paths without requiring user intervention, thereby improving both accuracy and ease of operation simultaneously.
2Productivity
If conventional vectorization techniques are used, then vector objects can be generated, but computational resources are inefficiently utilized
Solution Approach 1:
The patent changes the fundamental parameters of the vectorization process by using machine learning models that process image data in an optimized manner. The system transforms raster images through semantic classification and direct vector path generation, reducing computational complexity and processing time compared to conventional pixel-by-pixel tracing methods.
Solution Approach 2:
The system performs preliminary semantic classification of the entire image to identify objects and their boundaries before generating vector paths. This preliminary action organizes the data structure in advance, enabling faster and more efficient vector object generation without requiring time-consuming manual corrections or iterative processing.
3Loss of time
If manual creation of vector objects is performed, then accuracy can be controlled, but the time required for creation is excessive
Solution Approach 1:
The patent substitutes manual mechanical creation processes with automated machine learning-based generation. The system uses trained neural networks to automatically create vector objects from raster images, eliminating the need for users to manually trace, edit, and refine vector paths, thereby dramatically reducing creation time while maintaining ease of operation through simple image input.
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.