Synthetic Training Image Generation for Object Recognition
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Solution Overview
Problem
Existing object recognition systems face challenges in adapting to new environments due to the need for extensive manual labeling and recording of training images under varying imaging conditions, which is time-consuming and resource-intensive.
Innovation Solution
A method for generating training image data by modifying generic image data with respect to imaging-related parameters, such as fisheye distortion and noise, and determining similarity using Kernel Principal Component Analysis (KPCA) and Local Binary Patterns (LBP), allowing for the automatic creation of training images that mimic real-life conditions without manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recorded image data is collected under various imaging conditions and manually labelled, then the object recognition system can be trained to recognize objects under different conditions, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent uses a template image as a base and generates multiple modified copies by applying different imaging conditions (distortion, noise, brightness, contrast) to create synthetic training images. This copying approach eliminates the need to manually collect and label numerous real images under various conditions, significantly reducing time and resource requirements while maintaining training effectiveness
Solution Approach 2:
The system pre-defines a template image representing the object type and pre-establishes various imaging condition parameters (fisheye distortion, noise levels, brightness variations). These preliminary preparations allow for rapid generation of diverse training images without needing to physically capture images under each condition, addressing the time-consuming nature of traditional data collection
2Adaptability or versatility
If a larger number of training images are provided to improve machine learning, then the system can better handle varying imaging conditions, but the manual selection and labelling process becomes more resource-intensive
Solution Approach 1:
The system automatically generates training images by applying predefined imaging transformations to a template image without requiring manual selection or labelling. The automated process handles all variations of imaging conditions (distortion, noise, lighting) through algorithmic transformations, eliminating the need for human intervention in data preparation and significantly reducing operational complexity
Solution Approach 2:
The patent systematically varies imaging parameters (fisheye distortion strength, noise intensity, brightness levels, contrast) to generate diverse training images from a single template. This parameter-based approach allows for controlled generation of numerous training variations without manually processing each image, reducing both time and computational resources required
3Adaptability or versatility
If additional sets of training images are collected for new object types, then the system can recognize new object types, but the manual labelling process must be repeated for each new type
Solution Approach 1:
The system uses a universal template image approach where a single template can generate training images for multiple object types by applying different transformations and configurations. This universal method allows the same generation process to serve multiple object types, eliminating the need to repeat manual data collection and labelling for each new object type and significantly improving training efficiency
Data Source
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AI summary
A method, performed by a computing device, for generating training image data for a machine learning-based object recognition system is described. The method comprises receiving generic image data of an object type, receiving recorded image data related to the object type, and modifying the generic image data with respect to at least one imaging-related parameter. The method further comprises determining a degree of similarity between the modified generic image data and the recorded image data, and, when the determined degree of similarity fulfills a similarity condition, storing the modified generic image data as generated training image data of the object type. Further described are a computing device, a computer program product, a system and a motor vehicle.