US 12,488,566 B2
System and method for extracting object information from digital images to evaluate for realism
Sujeong Cha, Seattle, WA (US); Surya Raghavendra Vadlamani, Newtown, PA (US); Sukryool Kang, Pleasanton, CA (US); Anupam Anurag Tripathi, Chicago, IL (US); Mohamed Suhail, Santa Clara, CA (US); Peter Royer Smith, Jr., Sharon, CA (US); Bo Zhang, San Diego, CA (US); Daniel Garrison, Washington, MI (US); Jatinder Singh, New York, NY (US); and Neha Wadhwa Dang, New Delhi (IN)
Assigned to ACCENTURE GLOBAL SOLUTIONS LIMITED, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Sep. 29, 2023, as Appl. No. 18/478,265.
Claims priority of provisional application 63/511,339, filed on Jun. 30, 2023.
Prior Publication US 2025/0005901 A1, Jan. 2, 2025
Int. Cl. G06V 10/764 (2022.01); G06T 5/20 (2006.01); G06T 7/50 (2017.01); G06V 10/74 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/764 (2022.01) [G06T 5/20 (2013.01); G06T 7/50 (2017.01); G06V 10/761 (2022.01); G06V 20/70 (2022.01); G06V 2201/07 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for extracting object information from digital images, the method comprising:
receiving, at a realism assessment system, a user prompt involving real-world content;
submitting, from the realism assessment system and to a web-based search engine, the user prompt;
receiving, at the realism assessment system, a reference digital image retrieved by the search engine in response to the user prompt;
submitting, from the realism assessment system and to a first generative artificial intelligence (AI) model, the user prompt;
receiving, at the realism assessment system, a plurality of synthetic digital images including a first synthetic digital image and a second synthetic digital image, the plurality of synthetic digital images generated by the first generative AI model in response to the user prompt;
automatically classifying, via a deep learning-based instance segmentation model of the realism assessment system, a first set of pixels in the reference digital image as corresponding to a first object, a second set of pixels in the first synthetic digital image as corresponding to a second object, and a third set of pixels in the second synthetic digital image as corresponding to a third object;
extracting one or more Hu Moments for each of the first object, the second object, and the third object;
generating, at the realism assessment system, a first realism score based on a comparison of characteristics of the first object with the second object by comparing the one or more Hu Moments for the first object and the second object;
generating, at the realism assessment system, a second realism score based on a comparison of characteristics of the first object with the third object by comparing the one or more Hu Moments for the first object and the third object, the second realism score being greater than the first realism score;
determining, at the realism assessment system, the second synthetic digital image has a greater likelihood of accurately representing the real-world content than the first synthetic digital image based on the second realism score being greater than the first realism score; and
ranking each synthetic digital image of the plurality of synthetic digital images based on their computed realism score and presenting, at a computing device, the synthetic digital images in order of their ranking.