Accelerated learning in machine vision using artificially implanted defects
OwnerDonglok Kim, Shih-Jong J. Lee +1inventors
InventorsDonglok Kim, Shih-Jong J. Lee, Seho Oh
Life of this patent
Estimating the term…
Abstract
A learning acceleration method is disclosed that can be applied to multiple types and stages of learning to enhance the learning efficiency and outcome. Artificially created training samples can improve representation of all classes in the training set, decrease the difficulty of obtaining sufficient training samples, and decrease the difficulty of unequal sample prevalence. Two specific embodiments of learning acceleration are disclosed: learning accelerated algorithm training and learning accelerated start-up learning. Three objects of interest implantation methods are disclosed: texture mapping of defects, parametric synthesis of negative samples, and manual image editing.
Claims24
A computerized accelerated learning method for image-based decision system applications such as machine vision, non-contact gauging, inspection, robot guidance, medical imaging to accelerate learning maturity and enhance learning outcome comprises the following steps:
- (a) input learning sample images;
- (b) perform object of interest implantation on images using the learning sample images to generate simulated learning samples containing simulated objects of interest in the images;
- (c) perform computerized algorithm learning using the input learning sample images and the simulated learning sample images.
An accelerated computerized algorithm training method for image-based decision system applications such as machine vision, non-contact gauging. inspection, robot guidance, medical imaging to accelerate learning maturity and enhance learning outcome comprises the following steps:
- (a) input learning sample images;
- (b) perform object of interest implantation on images using the learning samples to generate simulated learning samples containing simulated objects of interest in the images;
- (c) perform computerized algorithm training using the learning sample images and the simulated learning samples images.
A computerized accelerated start-up learning method for image-based decision system applications such as machine vision, non-contact gauging, inspection, robot guidance, medical imaging to accelerate learning maturity and enhance learning outcome comprising:
- (a) input start-up learning sample images;
- (b) perform object of interest implantation on images using the start-up learning sample images to generate simulated learning sample containing simulated objects of interest in the images;
- (c) perform computerized start-up learning on a general computerized algorithm using the input start-up learning sample images and the simulated learning sample images.
Dependencies are inferred from the claim text.