AI-Assisted Object Boundary Annotation with Control Points
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for annotating object boundary information in images are inefficient and inaccurate, particularly when objects are partially obscured, requiring significant time and resources for manual annotation.
Innovation Solution
A method and device that utilize an artificial neural network-based algorithm to assist workers in efficiently and accurately annotating object boundary information through a bounding box input, predicted control point extraction, and interactive adjustment of control points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed to ensure accurate object boundary information, then annotation precision is improved, but annotation time increases significantly
Solution Approach 1:
The system performs preliminary annotation by automatically generating control points along object boundaries using an artificial neural network before the worker performs final verification. This preliminary action handles the time-consuming aspect of detailed boundary tracing while maintaining precision through subsequent human review.
Solution Approach 2:
The patent introduces control points as intermediary elements that represent object boundaries. Instead of requiring workers to manually trace entire boundaries, the system generates control points that serve as mediators, which workers then verify and adjust. This intermediary approach significantly reduces annotation time while maintaining accuracy.
2Ease of operation
If traditional scribble or click annotation methods are used, then annotation process is simplified, but ability to express obscured objects is insufficient
Solution Approach 1:
The patent segments the annotation process into two distinct parts: automatic control point generation by the artificial neural network and human verification/adjustment. This segmentation allows the system to handle obscured objects effectively through AI while maintaining operational simplicity through the structured two-step process.
Solution Approach 2:
The patent replaces the traditional mechanical annotation approach (manual scribbling or clicking) with an artificial intelligence-based system that generates control points automatically. This substitution maintains ease of operation by providing a structured interface while dramatically improving the ability to represent obscured objects through intelligent image analysis.
3Measurement precision
If complete manual annotation is performed for all object boundaries, then annotation accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent applies partial action by having the artificial neural network generate control points for only the most challenging portions of object boundaries, particularly obscured regions, while workers verify and complete the remaining parts. This partial automation approach reduces resource consumption by focusing AI computation where it provides the most value while maintaining high accuracy through human oversight.
Data Source
AI summary
The present invention relates to a method and a device of inputting annotation of object boundary information, and more particularly, to a method and a device of inputting annotation of object boundary information such that workers or the like efficiently and accurately input object boundary information in a service such as crowding sourcing, and preliminary work is assisted by an artificial neural network-based algorithm.