Synthetic Surrogate Image Generation for Landmark Annotation
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Solution Overview
Problem
Current image processing technologies face challenges in efficiently annotating landmarks on real two-dimensional images, particularly in generating accurate training datasets for machine learning models used in applications like pose estimation and image classification.
Innovation Solution
The method involves generating a training set of images by receiving real images of an object, estimating the pose of the object in a subset of these images, creating surrogate images using a three-dimensional model of the object, and perturbing characteristics of the object to mimic real-world variations. Landmarks are then labeled on these surrogate images based on the three-dimensional model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real images are manually annotated with landmarks, then training dataset accuracy is improved, but time consumption and labor cost increase significantly
Solution Approach 1:
The patent creates synthetic surrogate images by rendering 3D models of objects from multiple camera positions and perspectives. These synthetic images with automatically generated landmark annotations serve as copies that replace the need for manual annotation of real images, significantly reducing time and labor while maintaining training quality
Solution Approach 2:
The system pre-computes surrogate images and their corresponding landmark annotations before actual training begins. By preparing this synthetic training data in advance through automated 3D rendering and pose estimation, the time-consuming annotation task is performed once on synthetic data rather than repeatedly on real images
2Adaptability or versatility
If more diverse training images are generated, then model generalization is improved, but data processing complexity increases
Solution Approach 1:
The system varies multiple parameters in the 3D rendering process including camera position, lighting conditions, object poses, and background environments to generate diverse synthetic training images. This automated parameter variation achieves model generalization without manual intervention, managing complexity through systematic control
Solution Approach 2:
The 3D model rendering system serves multiple functions simultaneously: generating training images, creating augmented views, establishing pose-label correspondences, and simulating various real-world conditions. This multi-functional approach achieves diversity efficiently through a single unified processing framework
3Measurement precision
If pose estimation accuracy is improved, then surrogate image quality is improved, but computational requirements increase
Solution Approach 1:
The system uses the trained machine learning model itself to generate the training data it needs. The model estimates poses on real images, which are then used to create synthetic surrogate images with automatic annotations, creating a self-reinforcing cycle that improves accuracy without proportional increases in computational cost
Solution Approach 2:
Pose estimation is performed on a subset of real images to generate the initial set of surrogate images. This preliminary pose estimation on limited data reduces immediate computational requirements while still enabling the creation of sufficient training data to improve overall model performance
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image processing that involves annotating landmarks on real two-dimensional images. In one aspect, the methods include generating a training set of images of an object for landmark detection. This includes receiving a collection of real images of an object, estimating a pose of the object in each real image in a proper subset of the collection of real images, creating a collection of surrogate images of the object for the training set using the estimated poses and a three-dimensional model of the object.