公开(公告)号：US09830732B1

公开(公告)日：2017-11-28

申请号：US15596173

申请日：2017-05-16

Applicant: THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO

Inventor： Ali Punjani ,  Marcus Anthony Brubaker ,  David James Fleet

IPC: G06T15/04 ,  G06T19/20

CPC classification number: G06T15/04 ,  G06F19/16 ,  G06T7/37 ,  G06T7/75 ,  G06T19/20 ,  G06T2207/10061 ,  G06T2207/30024

Abstract: A system and a method for image alignment between at least two images to a three-dimensional model. The method including: determining a lower bound and an upper bound of an acceptable likelihood of mismatch between the at least two images; evaluating the likelihood of mismatch between the at least two images over a set of poses (r), shifts (t), or both poses (r) and shifts (t); and discarding those evaluations resulting beyond the lower bound and upper bound.

公开(公告)号：US10242483B2

公开(公告)日：2019-03-26

申请号：US15675893

申请日：2017-08-14

Applicant: THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO

Inventor： Ali Punjani ,  Marcus Anthony Brubaker ,  David James Fleet

IPC: G06T15/04 ,  G06T19/20 ,  G06F19/16 ,  G06T7/37 ,  G06T7/73

Abstract: A system and a method for image alignment between at least two images to a three-dimensional model. The method including: determining a lower bound and an upper bound of an acceptable likelihood of mismatch between the at least two images; evaluating the likelihood of mismatch between the at least two images over a set of poses (r), shifts (t), or both poses (r) and shifts (t); and discarding those evaluations resulting beyond the lower bound and upper bound.

公开(公告)号：US11680914B2

公开(公告)日：2023-06-20

申请号：US16753240

申请日：2018-10-05

Applicant: THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO

Inventor： Ali Punjani ,  David Fleet ,  Haowei Zhang

IPC: G06T11/00 ,  G01N23/2251 ,  G06T3/40 ,  G06T17/00 ,  G06V10/764 ,  G06V20/69 ,  G06V20/64

CPC classification number: G01N23/2251 ,  G06T3/40 ,  G06T11/006 ,  G06T17/00 ,  G06V10/764 ,  G06V20/64 ,  G06V20/693 ,  G06V20/695 ,  G06V20/698 ,  G01N2223/3103 ,  G01N2223/401

Abstract: There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.

公开(公告)号：US11515002B2

公开(公告)日：2022-11-29

申请号：US16288429

申请日：2019-02-28

Applicant: THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO

Inventor： Marcus Anthony Brubaker ,  Ali Punjani ,  David James Fleet

IPC: G16B15/00

Abstract: Disclosed herein are systems and methods for efficient 3D structure estimation from images of a transmissive object, including cryo-EM images. The method generally comprises, receiving a set of 2D images of a target specimen from an electron microscope, carrying out a reconstruction technique to determine a likely molecular structure, and outputting the estimated 3D structure of the specimen. The described reconstruction technique comprises: establishing a probabilistic model of the target structure; optimizing using stochastic optimization to determine which structure is most likely; and, optionally utilizing importance sampling to minimize computational burden.

公开(公告)号：US10282513B2

公开(公告)日：2019-05-07

申请号：US15292520

申请日：2016-10-13

Applicant: THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO

Inventor： Marcus Anthony Brubaker ,  Ali Punjani ,  David James Fleet

IPC: G06F19/16 ,  G06F19/12

Abstract: Disclosed herein are systems and methods for efficient 3D structure estimation from images of a transmissive object, including cryo-EM images. The method generally comprises, receiving a set of 2D images of a target specimen from an electron microscope, carrying out a reconstruction technique to determine a likely molecular structure, and outputting the estimated 3D structure of the specimen. The described reconstruction technique comprises: establishing a probabilistic model of the target structure; optimizing using stochastic optimization to determine which structure is most likely; and, optionally utilizing importance sampling to minimize computational burden.