18607604. IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM simplified abstract (CANON KABUSHIKI KAISHA)
Contents
IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM
Organization Name
Inventor(s)
Naoto Matsubara of Kanagawa (JP)
IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18607604 titled 'IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM
Simplified Explanation: The patent application aims to train a highly accurate model quickly by selecting an imaging device based on virtual viewpoint information and using machine learning with a learning dataset.
- **Key Features and Innovation:**
- Obtaining a plurality of captured images from different imaging devices. - Selecting an imaging device based on virtual viewpoint information for machine learning. - Performing machine learning using a learning dataset associating captured images with virtual viewpoint images.
- **Potential Applications:**
This technology can be used in various fields such as computer vision, augmented reality, virtual reality, and image processing.
- **Problems Solved:**
The technology addresses the challenge of obtaining accurate models with limited learning time by optimizing the selection of imaging devices for training.
- **Benefits:**
- Faster training of highly accurate models. - Improved efficiency in machine learning tasks. - Enhanced performance in computer vision applications.
- **Commercial Applications:**
"Virtual Viewpoint Selection for Machine Learning" technology can be applied in industries such as autonomous vehicles, surveillance systems, medical imaging, and gaming for improved image processing and analysis.
- **Prior Art:**
Researchers can explore prior art related to virtual viewpoint selection, machine learning in imaging devices, and optimization techniques for training accurate models.
- **Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms, virtual viewpoint selection methods, and applications of computer vision technology.
Questions about Virtual Viewpoint Selection for Machine Learning:
1. How does virtual viewpoint selection improve the efficiency of machine learning models in image processing tasks?
- Virtual viewpoint selection optimizes the training process by choosing the most relevant imaging device based on predefined virtual viewpoint information, leading to faster and more accurate model training.
2. What are the potential challenges in implementing virtual viewpoint selection for machine learning in real-world applications?
- Some challenges may include the complexity of integrating virtual viewpoint information with machine learning algorithms, ensuring compatibility with different imaging devices, and optimizing the selection process for diverse datasets and applications.
Original Abstract Submitted
To make it possible to obtain a highly accurate trained model with a small amount of learning time. A plurality of captured images corresponding to each of a plurality of imaging devices and virtual viewpoint information predefining a virtual viewpoint for generating a virtual viewpoint image are obtained and based on the virtual viewpoint information, an imaging device that is referred to in machine learning is selected from among the plurality of imaging devices. Then, the machine learning is performed by using learning dataset associating a captured image of a selected imaging device as reference data with a learning virtual viewpoint image generated by taking a viewpoint of the imaging device as a virtual viewpoint.
