18415596. ALIGNING A DISTORTED IMAGE simplified abstract (ASML NETHERLANDS B.V.)
Contents
ALIGNING A DISTORTED IMAGE
Organization Name
Inventor(s)
Maxim Pisarenco of Son en Breugel (NL)
Scott Anderson Middlebrooks of Duizel (NL)
Markus Gerardus Martinus Maria Van Kraaij of Eindhoven (NL)
Coen Adrianus Verschuren of Eindhoven (NL)
ALIGNING A DISTORTED IMAGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18415596 titled 'ALIGNING A DISTORTED IMAGE
Simplified Explanation: The patent application describes a method for generating synthetic distorted images using a computer program stored on a non-transitory computer-readable medium.
- **Key Features and Innovation:**
* Obtaining a set of distorted images as input. * Determining distortion modes of the input images using a model. * Generating different combinations of distortion modes. * Creating synthetic distorted images based on these combinations. * Compiling all synthetic distorted images into an output set.
- **Potential Applications:**
* Image processing and editing software. * Augmented reality and virtual reality applications. * Data augmentation for machine learning and computer vision tasks.
- **Problems Solved:**
* Efficient generation of diverse synthetic distorted images. * Enhancing image datasets for training machine learning models. * Facilitating research in image processing and computer vision.
- **Benefits:**
* Improved accuracy and robustness of machine learning models. * Enhanced visual effects in multimedia applications. * Streamlined image data augmentation processes.
- **Commercial Applications:**
* "Advanced Image Data Augmentation Techniques for Machine Learning Models in Computer Vision Applications"
- **Prior Art:**
* Researchers and developers can explore existing patents and publications related to image data augmentation, computer vision, and synthetic image generation.
- **Frequently Updated Research:**
* Stay updated on advancements in image processing, machine learning, and computer vision research for potential improvements in synthetic image generation techniques.
Questions about Synthetic Distorted Images: 1. What are the potential challenges in accurately determining distortion modes of input images?
- Accurately determining distortion modes may be challenging due to variations in image quality, lighting conditions, and object characteristics.
2. How can synthetic distorted images generated using this method benefit training machine learning models?
- Synthetic distorted images can enhance the diversity and robustness of training datasets, leading to improved model performance and generalization.
Original Abstract Submitted
Disclosed herein is a non-transitory computer readable medium that has stored therein a computer program, wherein the computer program comprises code that, when executed by a computer system, instructs the computer system to perform a method for generating synthetic distorted images, the method comprising: obtaining an input set that comprises a plurality of distorted images; determining, using a model, distortion modes of the distorted images in the input set; generating a plurality of different combinations of the distortion modes; generating, for each one of the plurality of combinations of the distortion modes, a synthetic distorted image in dependence on the combination; and including each of the synthetic distorted images in an output set.
