US 2026/0188036 A1Published application

Layout agnostic image segmentation

AssigneeOptum, Inc.

InventorsDinesh Bajaj, Mayank Chauhan, Anant Saxena

Life of this patent

Estimating the term…

Loading patent…

Abstract

Various embodiments of the present disclosure provide agnostic image segmentation techniques that improves the functionality of a computer in various aspects. The techniques comprise receiving image segmentation data that identifies a set of bounding boxes within an image; generating, using a clustering algorithm, and based on a y-axis distance between at least two bounding boxes within the set of bounding boxes, an initial bounding box cluster that comprises a first subset of bounding boxes; generating based on an x-axis distance between at least two bounding boxes within the initial bounding box cluster, a refined bounding box cluster from the initial bounding box cluster that comprises a second subset of bounding boxes; generating, a feature vector for the refined bounding box cluster based on a raw feature set; generating a segment classification for the refined bounding box cluster; and storing the raw feature set and the segment classification.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, by one or more processors, image segmentation data that identifies a set of bounding boxes within an image;
generating, by the one or more processors, using a clustering algorithm, and based on a y-axis distance between at least two bounding boxes within the set of bounding boxes, an initial bounding box cluster that comprises a first subset of bounding boxes from the set of bounding boxes;
generating, by the one or more processors, using the clustering algorithm, and based on an x-axis distance between at least two bounding boxes within the initial bounding box cluster, a refined bounding box cluster from the initial bounding box cluster that comprises a second subset of bounding boxes from the first subset of bounding boxes;
generating, by the one or more processors and using an embedding model, a feature vector for the refined bounding box cluster based on a raw feature set determined based at least in part on a portion of the image encapsulated by the second subset of bounding boxes;
generating, by the one or more processors and using a sequence labeler model, a segment classification for the refined bounding box cluster based on the feature vector;
and
storing, by the one or more processors, the raw feature set and the segment classification in association with the image.

4 more elements

A system comprising:

one or more processors;
and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, by one or more processors, image segmentation data that identifies a set of bounding boxes within an image;
generating, by the one or more processors using a clustering algorithm with a y-axis distance function, an initial bounding box cluster that comprises a first subset of bounding boxes from the set of bounding boxes;
generating, by the one or more processors and using the clustering algorithm with an x-axis distance function, a refined bounding box cluster from the initial bounding box cluster that comprises a second subset of bounding boxes from the first subset of bounding boxes;
generating, by the one or more processors and using an embedding model, a feature vector for the refined bounding box cluster based on a raw feature set determined based at least in part on a portion of the image encapsulated by the second subset of bounding boxes;
generating, by the one or more processors and using a sequence labeler model, a segment classification for the refined bounding box cluster based on the feature vector;
and
storing, by the one or more processors, the raw feature set and the segment classification in association with the image.

7 more elements

Dependencies are inferred from the claim text.