Alan Y Yuille

20070110322, May 17, 2007

Sep 5, 2006

11/516147

Alan Yuille - Los Angeles CA, US
Xiangrong Chen - Sunnyvale CA, US
Stellan Lagerstrom - Burlingame CA, US
Daniel Terry - New York NY, US
Mark Nitzberg - Oakland CA, US

G06K 9/68
G06K 9/34

382227000, 382176000

A method and apparatus for detecting text in real-world images comprises calculating a cascade of classifiers, the cascade comprising a plurality of stages, each stage including one or more weak classifiers, the plurality of stages organized to start out with classifiers that are most useful for ruling out non-text regions, and removing regions classified as non-text regions from the cascade prior to completion of the cascade, to further speed up processing.

20110091098, Apr 21, 2011

Oct 18, 2010

12/906997

Alan Yuille - Los Angeles CA, US
Xiangrong Chen - Sunnyvale CA, US
Stellan Lagerstrom - Burlingame CA, US
Daniel Terry - New York NY, US
Mark Nitzberg - Oakland CA, US

G06K 9/62
G06K 9/34

382159, 382176

A method and apparatus for detecting text in real-world images comprises calculating a cascade of classifiers, the cascade comprising a plurality of stages, each stage including one or more weak classifiers, the plurality of stages organized to start out with classifiers that are most useful for ruling out non-text regions, and removing regions classified as non-text regions from the cascade prior to completion of the cascade, to further speed up processing.

20220215646, Jul 7, 2022

Apr 23, 2020

17/605847

- Baltimore MD, US
Alan Yuille - Baltimore MD, US
Elliott Fishman - Baltimore MD, US

G06V 10/42
G16H 30/40
G16H 50/20
G06N 3/08

Systems, methods, and apparatus for segmenting internal structures depicted in an image. In one aspect, a method includes receiving data representing image data that depicts internal structures of a subject, providing an input data structure to a machine learning model, wherein the input data structure comprises fields structuring data that represents the received data representing the image data that depicts internal structures of the subject, wherein the machine learning model is a multi-stage deep convolutional network that has been trained to segment internal structures depicted by one or more images, receiving output data generated by the machine learning model based on the machine learning model's processing of the input data structure, and processing the output data to generate rendering data that, when rendered, a computer, causes the computer to output, for display, data that visually distinguishes between different internal structures depicted by the image data.