International business machines corporation (20240104830). AUGMENTING DATA USED TO TRAIN COMPUTER VISION MODEL WITH IMAGES OF DIFFERENT PERSPECTIVES simplified abstract
Contents
- 1 AUGMENTING DATA USED TO TRAIN COMPUTER VISION MODEL WITH IMAGES OF DIFFERENT PERSPECTIVES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUGMENTING DATA USED TO TRAIN COMPUTER VISION MODEL WITH IMAGES OF DIFFERENT PERSPECTIVES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
AUGMENTING DATA USED TO TRAIN COMPUTER VISION MODEL WITH IMAGES OF DIFFERENT PERSPECTIVES
Organization Name
international business machines corporation
Inventor(s)
AUGMENTING DATA USED TO TRAIN COMPUTER VISION MODEL WITH IMAGES OF DIFFERENT PERSPECTIVES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104830 titled 'AUGMENTING DATA USED TO TRAIN COMPUTER VISION MODEL WITH IMAGES OF DIFFERENT PERSPECTIVES
Simplified Explanation
The abstract describes a method, system, and computer program product for improving the accuracy of a vision model by generating a three-dimensional model of an object using images from a dataset, obtaining images of the object from different perspectives, and augmenting the dataset used to train the vision model with these new images.
- Three-dimensional model generation: Images of an object from a dataset are used to create a three-dimensional model of the object.
- Obtaining images from different perspectives: Images of the object from a second set of perspectives are obtained, which may include perspectives not present in the original dataset.
- Dataset augmentation: The dataset used to train the vision model is augmented with the new images of the object from different perspectives.
Potential Applications
This technology could be applied in various fields such as computer vision, object recognition, and augmented reality.
Problems Solved
This technology addresses the issue of limited perspectives in training datasets, which can lead to reduced accuracy in vision models.
Benefits
The benefits of this technology include improved accuracy of vision models, better object recognition, and enhanced performance in applications such as autonomous vehicles and robotics.
Potential Commercial Applications
Potential commercial applications of this technology include image recognition software, surveillance systems, and medical imaging technology.
Possible Prior Art
One possible prior art could be the use of data augmentation techniques in machine learning to improve model performance.
Unanswered Questions
1. How does the system handle variations in lighting conditions when obtaining images from different perspectives? 2. What is the computational cost associated with generating three-dimensional models of objects from images in the dataset?
Original Abstract Submitted
a computer-implemented method, system and computer program product for improving accuracy of a vision model. images of an object with a first set of perspectives are received from a dataset used to train the vision model. a three-dimensional model of the object is then generated using the images of the object from the dataset. using the three-dimensional model of the object, images of the object with a second set of perspectives are obtained. for example, the second set of perspectives may include different perspectives than the perspectives of the object from the images contained in the dataset. the dataset used to train the vision model may then be augmented with such images of the object with a second set of perspectives. in this manner, the dataset used to train the vision model includes a greater number of perspectives of the object thereby improving the accuracy of the vision model.
