公开(公告)号：US11854208B2

公开(公告)日：2023-12-26

申请号：US17149432

申请日：2021-01-14

Applicant: The Regents of the University of California

Inventor： Demetri Terzopoulos ,  Ali Hatamizadeh

IPC: G06T7/149 ,  G06T7/11 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06T7/149 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20116

Abstract: Systems and methods for image segmentation using neural networks and active contour methods in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating image segmentations from an input image. The method includes steps for receiving an input image, identifying a set of one or more parameter maps from the input image, identifying an initialization map from the input image, and generating an image segmentation based on the set of parameter maps and the initialization map.

公开(公告)号：US20210217178A1

公开(公告)日：2021-07-15

申请号：US17149432

申请日：2021-01-14

Applicant: The Regents of the University of California

Inventor： Demetri Terzopoulos ,  Ali Hatamizadeh

IPC: G06T7/149 ,  G06T7/11 ,  G06N3/04 ,  G06N3/08

Abstract: Systems and methods for image segmentation using neural networks and active contour methods in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating image segmentations from an input image. The method includes steps for receiving an input image, identifying a set of one or more parameter maps from the input image, identifying an initialization map from the input image, and generating an image segmentation based on the set of parameter maps and the initialization map.

公开(公告)号：US12159226B2

公开(公告)日：2024-12-03

申请号：US17139800

申请日：2020-12-31

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Jonathan Hopkins ,  Ali Hatamizadeh ,  Yuanping Song

IPC: G06N3/08 ,  G06N3/04

Abstract: A boundary learning optimization tool for training neural networks with accurate models of parameterized flexure using a minimal number of numerically generated performance solutions generated from different design instantiations of those topologies. Performance boundaries are output by the neural network in optimization steps, with geometric parameters varied from smallest allowable feature sizes to largest geometrically compatible feature sizes for given constituent materials. The plotted performance boundaries define the design spaces of flexure systems toward allowing designers to visually identify which geometric versions of their synthesized topologies best achieve desired combinations of performance capabilities.

公开(公告)号：US20210241100A1

公开(公告)日：2021-08-05

申请号：US17139800

申请日：2020-12-31

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Jonathan Hopkins ,  Ali Hatamizadeh ,  Yuanping Song

IPC: G06N3/08 ,  G06N3/04

Abstract: A boundary learning optimization tool for training neural networks with accurate models of parameterized flexure using a minimal number of numerically generated performance solutions generated from different design instantiations of those topologies. Performance boundaries are output by the neural network in optimization steps, with geometric parameters varied from smallest allowable feature sizes to largest geometrically compatible feature sizes for given constituent materials. The plotted performance boundaries define the design spaces of flexure systems toward allowing designers to visually identify which geometric versions of their synthesized topologies best achieve desired combinations of performance capabilities.