Semantic Segmentation Correction via Vertex Manipulation
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Solution Overview
Problem
Manual semantic segmentation is time-consuming and labor-intensive, and AI-driven methods often produce imprecise image masks that are difficult for users to correct accurately.
Innovation Solution
A system that includes a processor and display for receiving images, outputting automatic segmentations, and allowing users to input corrections, which adjusts vertices in real-time using algorithms like draw-and-replace and slide-and-delete to refine the segmentation masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-driven semantic segmentation methods are used, then productivity is improved by automating the segmentation process, but manufacturing precision deteriorates because the generated image masks are imprecise and difficult to correct
Solution Approach 1:
The patent introduces an intermediary correction tool between the AI-generated mask and the final annotation. This tool includes vertex manipulation capabilities that allow users to precisely adjust segmentation boundaries by dragging, adding, or removing vertices. The intermediary layer enables users to correct AI errors efficiently without starting from scratch, thus maintaining both productivity and precision
Solution Approach 2:
The patent segments the correction process into discrete vertex operations rather than requiring manual redrawing of entire boundaries. Users can independently adjust individual vertices along the segmentation line, allowing precise local corrections while maintaining the overall AI-generated structure. This segmented approach improves both correction speed and accuracy
2Manufacturing precision
If manual semantic segmentation is performed by human labelers, then manufacturing precision is improved through careful manual annotation, but productivity deteriorates due to the time-consuming and labor-intensive process
Solution Approach 1:
The system performs preliminary AI-driven segmentation to generate an initial mask before manual refinement. This preliminary action provides a close approximation of the final result, reducing the amount of manual work needed. Users only need to perform corrective actions on erroneous segments rather than annotating from scratch, thus improving productivity while maintaining precision
Solution Approach 2:
The patent implements dynamic vertex manipulation where segmentation boundaries can be easily adjusted by dragging vertices with the mouse or touch input. The system dynamically updates the mask in real-time as users move vertices, allowing fluid and efficient correction processes. This dynamic interaction significantly speeds up the manual refinement process compared to traditional static annotation methods
3Ease of operation
If AI-generated image masks are produced, then ease of operation is improved by automating the segmentation, but ease of repair deteriorates because the masks are unsuitable for user correction
Solution Approach 1:
The patent transforms static AI-generated masks into dynamic, editable structures with manipulatable vertices. Users can interactively adjust boundaries by dragging vertices, and the system dynamically recalculates and redraws the mask in real-time. This dynamic editability makes AI-generated masks suitable for correction while preserving the benefits of automation
Solution Approach 2:
The system provides immediate visual feedback when users drag or modify vertices, showing real-time updates to the segmentation mask. This feedback mechanism helps users understand the effect of their corrections and makes the repair process intuitive and easy to perform, bridging the gap between automated generation and manual correction
Data Source
AI summary
A computer-implement method includes receiving one or more images from one or more sensors, outputting the one or more images at a display, outputting an automatic segmentation line of the one or more portions of the image in response to an object identified in the one or more images, and in response to one or more inputs received at the system associated with a correction, outputting a correction line on the display associated with the object, wherein the correction line automatically adjust one or more vertices associated with an incorrect portion of the automatic segmentation, wherein the one or more vertices are adjusted in response to the one or more inputs.