Jason J Corso

20130002842, Jan 3, 2013

Apr 26, 2012

13/457305

Dipankar Das - Los Angeles CA, US
Marcus O. Filipovich - Venice CA, US
Jason J. Corso - Buffalo NY, US
Gregory D. Hager - Baltimore MD, US

Ikona Medical Corporation - Venice CA

H04N 7/18

348 65

Systems and methods for extracting and measuring motion from images capture during capsule endoscopy in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, an endoscope system configured to generate a spatial index of images captured along a passageway includes a processor and a camera configured to capture a plurality of images as the camera moves along a passageway, wherein the processor is configured to compare sets of at least two images from the plurality of images, determine motion of the camera along the passageway using the sets of at least two images, determine the distance the camera traveled along the passageway at the point at which an image in each of the sets of at least two images was captured, and generate a spatial index for the plurality of images by associating distances traveled along the passageway with the images.

20130083177, Apr 4, 2013

Jun 7, 2012

13/491027

Jason J. CORSO - Buffalo NY, US
Michael SMITH - Santa Monica CA, US
Marcus O. FILIPOVICH - Venice CA, US

Ikona Medical Corporation - Venice CA

A61B 1/05

348 65

Systems and methods for capturing and mosaicking images of one or more surfaces of a collapsed cavity are described. One embodiment includes capturing images using an endoscope, where the optics of the endoscope are radially symmetrical, locating the optical center and dewarping each of the captured images by mapping the image from polar coordinates centered on the optical center of the image to rectangular coordinates, discarding portions of each dewarped image to create high clarity dewarped images, estimating the motion of the endoscope with respect to the interior surface of the cavity that occurred between successive high clarity dewarped images, registering the high clarity dewarped images with respect to each other using the estimates of motion, and combining the registered high clarity dewarped images to create at least one mosaic.

20100165087, Jul 1, 2010

Dec 31, 2008

12/347855

Jason J. Corso - Buffalo NY, US
Michael Smith - Santa Monica CA, US
Marcus O. Filipovich - Venice CA, US

H04N 5/38
G06K 9/68

348 65, 382219, 348E05093

Systems and methods for capturing and mosaicking images of one or more surfaces of a collapsed cavity are described. A number of embodiments are capable of performing mosaicking in real time. Many embodiments are configured to perform multiple passes during motion estimation and/or registration to improve the quality of the resulting mosaic(s). One embodiment includes capturing images using an endoscope, where the optics of the endoscope are radially symmetrical, locating the optical center of each of the captured images, dewarping each of the captured images by mapping the image from polar coordinates centered on the optical center of the image to rectangular coordinates, discarding portions of each dewarped image to create high clarity dewarped images, estimating the motion of the endoscope with respect to the interior surface of the cavity that occurred between successive high clarity dewarped images, registering the high clarity dewarped images with respect to each other using the estimates of motion, and combining the registered high clarity dewarped images to create at least one mosaic.

20200258616, Aug 13, 2020

Dec 6, 2019

16/705371

- Ann Arbor MI, US
- Boston MA, US
Francis D. Pagani - South Lyon MI, US
Michael R. Mathias - Ann Arbor MI, US
Jason J. Corso - Chelsea MI, US
Roger Daglius Dias - Cambridge MA, US
Emily Mower Provost - Ann Arbor MI, US

G16H 40/20
G16H 10/60
H04N 21/44
H04N 21/439
G06N 3/08
G06N 20/20
G06N 20/10

Embodiments described herein relate, inter alia, to receiving one or more segments of a digital recording, wherein the one or segments include video and/or audio data of a surgical procedure; analyzing, via a video/audio understanding model, the one or more segments to (i) characterize a plurality of independent features associated with a technical skill and/or a non-technical practice that are evident in the one or more segments and (ii) determine a higher-order pattern based upon analyzing a group of at least two of the plurality of independent features; comparing the higher-order pattern to ratings data associated to outcomes following one or more surgical procedures; and automatically generating a quality score based upon the comparing, wherein the quality score is predictive of an assessment of the technical skill and/or non-technical practice.

20200202615, Jun 25, 2020

Dec 5, 2019

16/704529

- Ann Arbor MI, US
Stephan Lemmer - Belleville MI, US
Jason Corso - Chelsea MI, US
Walter S. Lasecki - Ann Arbor MI, US

G06T 17/00
G06T 11/20

A method is disclosed for reconstructing three-dimensional video from two-dimensional video data using particle filtering and thereby generating training data for autonomous vehicles. In one version, the method comprises: receiving a set of annotations associated with a video frame comprising a view of at least a portion of a vehicle, each annotation comprising at least one two-dimensional line; removing at least one outlier from the set of annotations; determining an estimated vehicle model based on the set of annotations; and providing the estimated vehicle model to a driving simulator.

20190077398, Mar 14, 2019

Dec 29, 2017

15/858592

- Erlanger KY, US
- Ann Arbor MI, US
Brent Griffin - Ann Arbor MI, US
Jason J. Corso - Ann Arbor MI, US

B60W 30/095
B60W 30/18
B60W 50/00
G05D 1/00
G05D 1/02
G08G 1/16
G06K 9/00

System, methods, and other embodiments described herein relate to predicting lane changes for nearby vehicles of a host vehicle. In one embodiment, a method includes, in response to detecting that one or more of the nearby vehicles are present proximate to the host vehicle, collecting pose information about the nearby vehicles. The nearby vehicles are traveling proximate to the host vehicle and in a direction of the host vehicle. The method includes analyzing the pose information of the nearby vehicles using separate recurrent units of a structural recurrent neural network (S-RNN) to generate factors according to the lanes. The method includes generating prediction indicators for the nearby vehicles as a function of the factors for the lanes using the S-RNN. The method includes providing electronic outputs identifying the prediction indicators that specify a likelihood of the nearby vehicles changing between the lanes.