Other

Ge Precision Healthcare LLC

International Business Machines Corporation

Yu Huang

Yanran Xu

Fanbo Meng

Peng Hui Jiang

Sheng Yan Sun

Shan Jiang

Fang C Chen

Jie Ling

Yan Ma

Raghu Prasad

Adam G Pautsch

A system, method, and computer program product are configured to: provide a tablespace comprising an original partition having a lower boundary L, an upper boundary U, a split percentage P, and a split ratio R; and in response to the original partition reaching a fullness of P, automatically split the original partition to a first progeny partition and a second progeny partition, wherein the first progeny partition has a lower boundary Lthat is the same as the lower boundary L of the original partition and an upper boundary Uthat is determined based on whether the insert is a sequential or random insert, and wherein the second progeny partition has an upper boundaries Uthat is the same as the upper boundary U of the original partition and a lower boundary Lthat is determined based on whether the insert is a sequential or random insert.

Self-maintained tablespace

A method for generating an object 3D point cloud in a medical imaging system comprising a machine table and a scanning device comprises: extracting a valid 3D point cloud in a valid region where the machine table is located from a global 3D point cloud based on a current height of the machine table and boundary information of the machine table, wherein the global 3D point cloud comprises 3D point clouds of an object and a surrounding environment thereof, and the object 3D point cloud is comprised in the valid 3D point cloud; and removing an environment 3D point cloud of the surrounding environment from the valid 3D point cloud to obtain the object 3D point cloud, wherein the surrounding environment comprises at least part of the machine table. In some embodiments, a moving path of the machine table in the medical imaging system is planned using the object 3D point cloud. In some embodiments, collision prediction of the object is performed in the medical imaging system using the object 3D point cloud.

Method and system for generating object 3D point cloud in medical imaging system

The present invention relates to a system and a method for calibration between coordinate systems of a 3D camera and a medical imaging apparatus. The calibration system includes a calibration tool having markers and a reference point that is aligned with a center of the medical imaging apparatus to serve as an origin of the coordinate system. Positions of the markers in the coordinate system of the medical imaging apparatus are calculated according to relative positions of the markers with respect to the reference point. A 3D camera captures images to determine positions of the markers in the coordinate system of the 3D camera. A calculation device calibrates the coordinate system of the 3D camera and the coordinate system of the medical imaging apparatus using the positions of the markers in the coordinate system of the 3D camera and the p in the coordinate system of the medical imaging apparatus.

System and method for calibration between coordinate systems of 3D camera and medical imaging apparatus and application thereof

Various embodiments of a deep learning (DL)-based face perception engine for constructing, providing, and applying a highly-personalized face perception model for an individual through a deep learning process are disclosed. In some embodiments, a disclosed face perception engine includes a deep neural network configured for training a personalized face perception model for a unique individual based on a standard set of training images and a corresponding set of decisions on the set of training images provided by the unique individual. When sufficiently trained using the standard set of training images and the corresponding set of decisions, the personalized face perception model for the unique individual perceives a new face photo/image as if through the eyes of that unique individual. Hence, the trained face perception model can be used an 'agent' or “representative” of the associated person in making very personal decisions, such as to decide if a given face photo/image includes a desirable face in the eyes of that person.

Deep-learning-based dating profiles filtering and recommendation system

Growing community of inventors

Beijing, China

Yu Huang