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公开(公告)号：US20250117959A1
公开(公告)日：2025-04-10
申请号：US18480665
申请日：2023-10-04
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng , Zhongpai Gao , Abhishek Sharma
IPC: G06T7/70
Abstract: Multiple predictions about the position of an object during a time period may each indicate the position of the object at a respective time during the time period. Respective validity indications corresponding to the multiple predictions may each indicate an accuracy of the corresponding prediction. Whether a change has occurred in a distribution of the predictions from a first subset of predictions to a second subset of predictions during the time period may be determined. If the change has occurred, a prediction from the first subset of predictions or the second subset of predictions may be selected, based on the validity of the predictions and/or the detection of a motion, as a best indication of the position of the object.
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公开(公告)号：US20240331446A1
公开(公告)日：2024-10-03
申请号：US18126853
申请日：2023-03-27
Inventor： Zhongpai Gao , Abhishek Sharma , Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
CPC classification number: G06V40/20 , G06V10/26 , G06V10/40 , G06V10/82 , G06V40/11 , G06V10/774 , G06V2201/03
Abstract: Automatic hand gesture determination may be a challenging task considering the complex anatomy and high dimensionality of the human hand. Disclosed herein are systems, methods, and instrumentalities associated with recognizing a hand gesture in spite of the challenges. An apparatus in accordance with embodiments of the present disclosure may use machine learning based techniques to identify the area of an image that may contain a hand and to determine an orientation of the hand relative to a pre-defined direction. The apparatus may then adjust the area of the image containing the hand to align the orientation of the hand with the pre-defined direction and/or to scale the image area to a pre-defined size. Based on the adjusted image area, the apparatus may detect a plurality of hand landmarks and predict a gesture indicated by the hand based on the plurality of detected landmarks.
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公开(公告)号：US20240135737A1
公开(公告)日：2024-04-25
申请号：US18128290
申请日：2023-03-29
Inventor： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC classification number: G06V20/70 , G06V10/235
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.
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公开(公告)号：US20240070905A1
公开(公告)日：2024-02-29
申请号：US17897465
申请日：2022-08-29
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng , Abhishek Sharma
IPC: G06T7/73
CPC classification number: G06T7/74 , G06T2207/30196
Abstract: The 3D pose of a person may be estimated by triangulating 2D representations of body keypoints (e.g., joint locations) of the person. The triangulation may leverage various metrics such as confidence scores associated with the 2D representations of a keypoint and/or temporal consistency between multiple 3D representations of the keypoint. The 2D representations may be arranged into groups, a candidate 3D representation may be determined for each group, taking into account of the confidence score of each 2D representation in the group, and the candidate 3D representation that has the smallest error may be used to represent the keypoint. Other 3D representation(s) of the keypoint determined from images taken at different times may be used to refine the 3D representation of the keypoint.
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公开(公告)号：US12014815B2
公开(公告)日：2024-06-18
申请号：US17869852
申请日：2022-07-21
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng
IPC: G16H30/40 , G06T19/00 , H04N13/189
CPC classification number: G16H30/40 , G06T19/00 , H04N13/189
Abstract: Described herein are systems, methods, and instrumentalities associated with generating a multi-dimensional representation of a medical environment based on images of the medical environments. Various pre-processing and/or post-processing operations may be performed to supplement and/or improve the multi-dimensional representation. These operations may include determining semantic information associated with the medical environment based on the images and adding the semantic information to the multi-dimensional representation in addition to space and time information. The operations may also include anonymizing a person presented in the multi-dimensional representation, adding synthetic views to the multi-dimensional representation, improving the quality of the multi-dimensional representation, etc. The multi-dimensional representation of the medical environment generated using these techniques may allow a user to experience and explore the medical environment, for example, via a virtual reality device.
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Patent Agency Ranking
公开(公告)号：US20240164758A1
公开(公告)日：2024-05-23
申请号：US17989251
申请日：2022-11-17
Inventor： Ziyan Wu , Shanhui Sun , Arun Innanje , Benjamin Planche , Abhishek Sharma , Meng Zheng
CPC classification number: A61B8/5261 , A61B6/5247 , A61B8/4254 , A61B8/466 , A61B8/5223
Abstract: Sensing device(s) may be installed in a medical environment to captures images of the medical environment, which may include an ultrasound probe and a patient. The images may be processed to determine, automatically, the position of the ultrasound probe relative to the patient's body. Based on the determined position, ultrasound image(s) taken by the ultrasound probe may be aligned with a 3D patient model and displayed with the 3D patient model, for example, to track the movements of the ultrasound probe and/or provide a visual representation of the anatomical structure(s) captured in the ultrasound image(s) against the 3D patient model. The ultrasound images may also be used to reconstruct a 3D ultrasound model of the anatomical structure(s).
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公开(公告)号：US20240029867A1
公开(公告)日：2024-01-25
申请号：US17869852
申请日：2022-07-21
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng
IPC: G16H30/40 , H04N13/189 , G06T19/00
CPC classification number: G16H30/40 , H04N13/189 , G06T19/00
Abstract: Described herein are systems, methods, and instrumentalities associated with generating a multi-dimensional representation of a medical environment based on images of the medical environments. Various pre-processing and/or post-processing operations may be performed to supplement and/or improve the multi-dimensional representation. These operations may include determining semantic information associated with the medical environment based on the images and adding the semantic information to the multi-dimensional representation in addition to space and time information. The operations may also include anonymizing a person presented in the multi-dimensional representation, adding synthetic views to the multi-dimensional representation, improving the quality of the multi-dimensional representation, etc. The multi-dimensional representation of the medical environment generated using these techniques may allow a user to experience and explore the medical environment, for example, via a virtual reality device.
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公开(公告)号：US20230419507A1
公开(公告)日：2023-12-28
申请号：US17851494
申请日：2022-06-28
Inventor： Benjamin Planche , Liangchen Song , Ziyan Wu , Meng Zheng
CPC classification number: G06T7/246 , G06T7/90 , G06T15/20 , G06T2207/20081 , G06T2207/20084
Abstract: Described herein are systems, methods, and instrumentalities associated with estimating the motions of multiple 3D points in a scene and predicting a view of scene based on the estimated motions. The tasks may be accomplished using one or more machine-learning (ML) models. A first ML model may be used to predict motion-embedding features for a temporal state of a scene, based on motion-embedding features for previous states. A second ML model may be used to predict a motion field representing displacement or deformation of the multiple 3D points from a source time to a target time. Then, a third ML model may be used to predict respective image properties of the 3D points based on their updated locations at the target time and/or a viewing direction. An image of the scene at the target time may then be generated based on the predicted image properties of the 3D points.
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公开(公告)号：US20240378731A1
公开(公告)日：2024-11-14
申请号：US18195009
申请日：2023-05-09
Inventor： Zhongpai Gao , Abhishek Sharma , Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.
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公开(公告)号：US20240346684A1
公开(公告)日：2024-10-17
申请号：US18133185
申请日：2023-04-11
Inventor： Meng Zheng , Jun Wang , Benjamin Planche , Zhongpai Gao , Ziyan Wu
IPC: G06T7/73
CPC classification number: G06T7/73 , G06T2207/20081 , G06T2207/30196
Abstract: Disclosed herein are systems, methods and instrumentalities associated with multi-person joint location and pose estimation based on an image that depicts multiple people in a scene, where at least some of the joint locations of a person may be blocked or obstructed by other people or objects in the scene. The estimation may be performed by detecting and grouping joint locations in the image using a bottom-up approach, and refining each group of detected joint locations by recovering obstructed joint location(s) that may be missing from the group. The detection, grouping, and/or refinement may be accomplished based on one or more machine learning (ML) models that may be implemented using artificial neural networks such as convolutional neural networks.
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