Back to results
US 11,899,749 B2Granted
Automatic labeling and segmentation using machine learning models
AssigneeNVIDIA Corporation
InventorsSubhashree Radhakrishnan, Partha Sriram, Farzin Aghdasi, Seunghwan Cha, Zhiding Yu
Life of this patent
Estimating the term…
Abstract
In various examples, training methods as described to generate a trained neural network that is robust to various environmental features. In an embodiment, training includes modifying images of a dataset and generating boundary boxes and/or other segmentation information for the modified images which is used to train a neural network.
Claims33
A method comprising:
- obtaining an image including an object depicted in the image, wherein the object is associated with a first set of boundaries;
- generating, based at least in part on the first set of boundaries, one or more labels corresponding to one or more areas of the image to distinguish areas depicting respective parts of the object from areas depicting respective parts of the image different from the object;
- performing a set of transformations on the image to generate a set of transformed images;
- and
- generating, based at least in part on the one or more generated labels, a second set of boundaries of the object for the set of transformed images, wherein the second set of boundaries enclose a smaller portion of the image than the first set of boundaries.
2 more elements
A system comprising:
- one or more processors;
- and
- memory storing instructions that, as a result of being executed by the one or more processors, cause the system to:
- obtain an image depicting an object that is associated with first boundaries;
- use a first model to obtain a segmentation of the image to distinguish pixels of the object from other pixels based at least in part on the first boundaries;
- use the segmentation to determine second boundaries of the object in a set of modified versions of the image, wherein the second boundaries enclose a region that encompasses the object and is smaller than at least one region enclosed by the first boundaries;
- and
- associate the object with the second boundaries of the object in a dataset to update a second model.
5 more elements
A system comprising:
- one or more processors to perform one or more operations, the one or more operations comprising at least:
- instantiating a pre-labeling tool based at least in part on labels associated with objects within a first set of images of a first dataset and a first set of mask annotations;
- modifying a second set of images of a second dataset to generate a set of modified images;
- and
- updating a neural network based at least part on the set of modified images and a second set of mask annotations, generated by the pre-labeling tool, corresponding to objects within the set of modified images, wherein the second set of mask annotations enclose a smaller area that encompasses the objects compared to the first set of mask annotations.
2 more elements
A method comprising:
- obtaining a segmentation of an image that is associated with a first boundary;
- performing a transformation of the image to obtain two or more transformed images;
- and
- generating a second boundary of an object depicted in the two or more transformed images based at least in part on the segmentation, wherein the second boundary encloses the object within a smaller region compared to a region enclosed by the first boundary.
1 more element
Dependencies are inferred from the claim text.