AR Image Training for Precise Object Segmentation and Contour Modeling
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Solution Overview
Problem
Existing methods for detecting and modeling objects and characteristic areas in images suffer from precision issues due to the use of characteristic points, leading to inaccurate detection and modeling, and manual annotation lacks consistency and accuracy.
Innovation Solution
A machine learning system is trained using augmented reality images containing virtual elements to accurately segment and model objects and characteristic areas, employing a joint learning process that combines segmentation models with contour point sets, addressing occlusion problems and enhancing precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning systems use separate processing stages for detection and recognition, then the system structure is simple and easy to implement, but the processing time increases and productivity decreases
Solution Approach 1:
The patent combines multiple machine learning models (detection model and recognition model) into a single unified neural network architecture. This merging eliminates the need for separate processing stages, allowing the system to perform both detection and recognition simultaneously in one pass through the network, thereby reducing processing time while maintaining a manageable system structure through integrated design
2Measurement precision
If the machine learning system processes entire high-resolution images, then the detection precision is high, but the computational resources and processing time increase significantly
Solution Approach 1:
The patent implements a two-stage processing approach where the image is first divided into multiple regions of interest based on initial detection, and then only these segmented regions are processed by the recognition model. This segmentation strategy maintains high detection precision by focusing computational resources on relevant areas while significantly reducing overall computational resource consumption compared to processing entire high-resolution images
3Reliability
If multiple machine learning models are trained separately on different datasets, then each model can be optimized for its specific task, but the training time and computational resources increase
Solution Approach 1:
The patent merges multiple machine learning models into a single unified neural network that can be trained as one integrated system. This allows simultaneous optimization of detection and recognition capabilities during a single training process, reducing total training time while maintaining high model performance through joint learning and shared feature extraction layers
Data Source
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AI summary
The invention relates to a learning method of a machine learning system. According to such a method, the machine learning system carries out the steps of: obtaining augmented reality images comprising a real image and at least one virtual element representative of the object and/or the characteristic region; obtaining, for each augmented reality image, learning information comprising, for at least one given virtual element of the augmented reality image: a model for segmenting the given virtual element, and a set of contour points corresponding to a parameterisation of the given virtual element; and learning on the basis of the plurality of augmented reality images and the learning information, delivering a set of parameters enabling the machine learning system to detect and model the object and/or the characteristic region in a given image.