AR-VR Content Annotation Using Augmented Image Features
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Solution Overview
Problem
Existing AR-VR applications struggle to dynamically generate annotations for images of devices with varying distortions, scales, and rotations, limiting the effectiveness of object detection models.
Innovation Solution
A method and system that dynamically generates annotated content for AR-VR applications by receiving images, generating augmented datasets, extracting features, and comparing them to pre-annotated datasets to train object detection models, using techniques like ORB, HOG, and SIFT to identify Regions of Interest (ROIs) and labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for training object detection models in AR-VR applications, then annotation accuracy can be maintained, but the time and effort required for annotating images increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing images to detect edges, contours, and key features before annotation is needed. This preliminary feature extraction creates a foundation that speeds up the subsequent annotation process while maintaining accuracy, as annotators can work with pre-processed feature maps rather than raw images.
Solution Approach 2:
The system creates copies of image features and characteristics to generate synthetic training data. By copying and transforming existing annotated images through various augmentations (rotations, scaling, distortions), the system generates additional training samples without requiring manual annotation of each new image, thus reducing annotation time while preserving accuracy through feature consistency.
2Productivity
If template-based automated annotation is used for substantially similar images, then annotation speed increases, but the system cannot dynamically generate annotations for images with varying distortion, scale, and rotations
Solution Approach 1:
The system transitions from static template-based annotation to dynamic annotation by implementing real-time feature detection and extraction. The annotation system continuously adapts to varying image conditions (distortion, scale, rotation) by detecting features dynamically rather than relying on fixed templates, enabling it to handle diverse image variations while maintaining annotation speed through automated processing.
Solution Approach 2:
The system changes parameters of image transformations (rotation angles, scale factors, distortion levels) to generate augmented training images. By systematically varying these parameters and applying consistent feature extraction methods, the system can dynamically annotate images with varying characteristics while maintaining annotation speed through parameterized processing rather than manual intervention.
3Reliability
If more images with varying distortions and scales are annotated manually to improve model training, then model accuracy improves, but the complexity and time required for data preparation increases
Solution Approach 1:
Instead of manually annotating numerous varied images, the system copies existing annotated images and applies automated transformations (rotations, scaling, distortions) to generate diverse training samples. This copying approach with automated transformations maintains model training quality by providing varied data while significantly reducing data preparation complexity and time compared to manual annotation of each transformed image.
Data Source
AI summary
A method and content annotating system for dynamically generating annotated content for training model for AR-VR applications. The content annotating system receives plurality of images for object associated with AR-VR applications. The content annotating system obtains pre-annotated datasets related to the plurality of images from user. The content annotating system generates plurality of augmented image datasets and extracts set of features from the pre-annotated datasets and the plurality of augmented image datasets. The content annotating systems compares the sets of features to identify ROIs on the plurality of augmented image datasets. Further, the content annotating system generates annotated content for the plurality of augmented image datasets based on comparison. The annotated content and the pre-annotated datasets are used to train model associated with AR-VR applications. Thus, the present disclosure dynamically generates annotated content to train model and detect objects associated with AR-VR applications.