Machine Learning Image Processing Using Synthetic Training Data
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Solution Overview
Problem
Current image processing techniques, especially those using machine learning, are limited by the need for exhaustive and labeled training data sets, making them impractical for applications requiring high-level visual alterations and sophisticated ground truth data, such as altering the visual appearance of images or generating curated content.
Innovation Solution
A machine learning-based image processing architecture that generates comprehensive datasets with relevant ground truth data, using deep neural networks and convolutional neural networks, and automatically labels images to learn attributes associated with scenes and objects, enabling the detection and replication of high-level aesthetics and styles without manual artist intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning techniques are used for high-level visual appearance alteration, then image processing capability is improved, but the need for exhaustive labeled training data sets increases making the approach impractical
Solution Approach 1:
The patent applies preliminary action by pre-rendering three-dimensional object models to generate comprehensive training image datasets before machine learning processing. This pre-computation creates a ready-to-use training corpus that eliminates the need for exhaustive manual data collection and labeling, resolving the contradiction between advanced image processing capability and data quantity requirements
Solution Approach 2:
The patent uses copying by generating synthetic training images from three-dimensional object models through rendering. These synthesized copies serve as realistic training data without requiring physical objects or manual photography, thereby providing sufficient training data quantity while maintaining image processing adaptability
2Measurement precision
If machine learning techniques are used to learn complex ground truth data, then analysis accuracy is improved, but the difficulty of labeling images with sophisticated ground truth data increases
Solution Approach 1:
The patent applies self-service by enabling three-dimensional object models to automatically generate their own labeled training data through rendering. The models inherently contain geometric, material, and spatial information that are automatically encoded into the rendered images with corresponding ground truth labels, eliminating the need for manual labeling while achieving high measurement precision
Solution Approach 2:
The patent uses the three-dimensional object model as an intermediary between the physical object and the training data. This intermediary representation captures complex ground truth information in a structured format that can be automatically rendered into images with precise labels, resolving the contradiction between accuracy and labeling difficulty
3Reliability
If comprehensive training datasets are generated with relevant ground truth data, then machine learning model performance is improved, but the time and resources required for data generation increase
Solution Approach 1:
The patent uses copying to generate comprehensive training datasets by rendering multiple views and variations of three-dimensional object models. This synthetic data generation approach creates reliable training data with complete ground truth information while significantly reducing the time and resources compared to manual data collection and annotation processes
Data Source
AI summary
A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.