Raster Image Vectorization With Localized Control And Semantic Segmentation
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Solution Overview
Problem
Conventional vectorization methods fail to accurately convert raster images to vector images due to noise interference, inaccurate representation of visually apparent features, and improper filling or shading, resulting in unsatisfactory vector images.
Innovation Solution
An enhanced vectorization system that includes an image vectorization module capable of localized user control, using edge detection, semantic element recognition, and hierarchical segmentation to convert raster images into vector images with improved noise reduction and feature accuracy, allowing for localized adjustments such as denoising, segment splitting, and merging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional vectorization methods are used to convert raster images to vector images, then the conversion process is simple and fast, but the resulting vector images contain noise interference, inaccurate feature representation, and improper filling or shading
Solution Approach 1:
The patent segments the vectorization process into distinct modules: edge detection module, semantic element recognition module, hierarchical segmentation module, and localized control module. Each module handles specific aspects of the conversion, allowing complex processing to be broken down into manageable, targeted operations that improve accuracy without overwhelming system complexity
Solution Approach 2:
The patent performs preliminary actions before final vectorization by first detecting edges, recognizing semantic elements, and creating hierarchical segmentations. These preparatory steps organize the raster image data into structured representations that guide the subsequent vectorization process, ensuring accurate feature representation while maintaining systematic control
2Manufacturing precision
If noise reduction is applied during vectorization, then the visual quality of the vector image improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by implementing localized control that allows different regions of the image to receive different levels of noise reduction processing. The system identifies areas with significant noise interference and applies enhanced denoising only to those specific regions, while areas with less noise receive minimal or no processing, thereby improving overall quality while reducing total processing time
Solution Approach 2:
The patent introduces an intermediary hierarchical segmentation structure that mediates between the raw raster image and the final vector output. This intermediate representation organizes semantic elements and edges into structured segments, allowing noise reduction to be applied selectively at appropriate levels of the hierarchy, balancing quality improvement with processing efficiency
3Manufacturing precision
If edge detection and semantic element recognition are performed to improve feature accuracy, then the vector image better represents the original picture, but the device complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex tasks of edge detection and semantic element recognition into separate, specialized modules. The edge detection module focuses solely on identifying boundaries, while the semantic element recognition module identifies meaningful objects and features. This segmentation allows each module to be optimized for its specific function, improving feature accuracy while managing system complexity through modular design
Solution Approach 2:
The patent adds a hierarchical dimension to the vectorization process by organizing detected edges and semantic elements into hierarchical segmentations with multiple levels of organization. This dimensional addition allows the system to manage complex feature relationships systematically, improving feature representation accuracy while providing structured control over the complexity of processing
4Manufacturing precision
If localized user control is implemented for denoising, segment splitting, and merging, then the user can achieve better visual quality, but the ease of operation decreases due to increased control complexity
Solution Approach 1:
The patent segments the control interface into distinct, localized controls for different operations (denoising, segment splitting, segment merging) and different regions of the image. Users can access and adjust specific controls for the operations they need, rather than being presented with a single complex global control system. This segmentation makes the interface more manageable and easier to navigate while maintaining precise control capabilities
Solution Approach 2:
The patent implements local quality in the control interface by allowing users to apply different control settings to different localized regions of the image. Users can select specific areas and apply denoising, splitting, or merging operations with parameters optimized for that region, providing precise visual quality control while keeping the interaction simple through context-sensitive controls
Data Source
AI summary
Enhanced vectorization of raster images is described. An image vectorization module converts a raster image with bitmapped data to a vector image with vector elements based on mathematical formulas. In some embodiments, spatially-localized control of a vectorization operation is provided to a user. First, the user can adjust an intensity of a denoising operation differently at different areas of the raster image. Second, the user can adjust an automated segmentation by causing one segment to be split into two segments along a zone marked with an indicator tool, such as a brush. Third, the user can adjust an automated segmentation by causing two segments to be merged into a combined segment. The computation of the vector elements is based on the adjusted segmentation. In other embodiments, semantic information gleaned from the raster image is incorporated into the vector image to facilitate manipulation, such as joint selection of multiple vector elements.