公开(公告)号：US12106531B2

公开(公告)日：2024-10-01

申请号：US17383362

申请日：2021-07-22

Applicant: Microsoft Technology Licensing, LLC

Inventor： Lijuan Wang ,  Zicheng Liu ,  Ying Jin ,  Hongli Deng ,  Kun Luo ,  Pei Yu ,  Yinpeng Chen

IPC: G06V40/10 ,  G06T7/70 ,  G06V10/22

CPC classification number: G06V10/22 ,  G06T7/70 ,  G06V40/10 ,  G06T2207/30196

Abstract: To improve the accuracy and efficiency of object detection through computer digital image analysis, the detection of some objects can inform the sub-portion of the digital image to which subsequent computer digital image analysis is directed to detect other objects. In such a manner object detection can be made more efficient by limiting the image area of a digital image that is analyzed. Such efficiencies can represent both computational efficiencies and communicational efficiencies arising due to the smaller quantity of digital image data that is analyzed. Additionally, the detection of some objects can render the detection of other objects more accurate by adjusting confidence thresholds based on the detection of those related objects. Relationships between objects can be utilized to inform both the image area on which subsequent object detection is performed and the confidence level of such subsequent object detection.

公开(公告)号：US11429842B2

公开(公告)日：2022-08-30

申请号：US16396513

申请日：2019-04-26

Applicant: Microsoft Technology Licensing, LLC

Inventor： Lijuan Wang ,  Kevin Lin ,  Zicheng Liu ,  Kun Luo

IPC: G06T7/10 ,  G06N3/04 ,  G06N3/08 ,  G06F17/15 ,  G06V40/10

Abstract: A computing system is provided. The computing system includes a processor configured to execute a convolutional neural network that has been trained, the convolutional neural network including a backbone network that is a concatenated pyramid network, a plurality of first head neural networks, and a plurality of second head neural networks. At the backbone network, the processor is configured to receive an input image as input and output feature maps extracted from the input image. The processor is configured to: process the feature maps using each of the first head neural networks to output corresponding keypoint heatmaps; process the feature maps using each of the second head neural networks to output corresponding part affinity field heatmaps; link the keypoints into one or more instances of virtual skeletons using the part affinity fields; and output the instances of the virtual skeletons.

公开(公告)号：US20200272812A1

公开(公告)日：2020-08-27

申请号：US16281876

申请日：2019-02-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Lijuan Wang ,  Zicheng Liu ,  Kevin Lin ,  Kun Luo

IPC: G06K9/00 ,  G06K9/62

Abstract: A machine accesses a training data set comprising multiple real images and multiple synthetic images. The machine trains a joint prediction module to predict joint locations in visual data using the multiple real images. The machine trains a part affinity field prediction module to identify adjacent joints in visual data using the multiple real images. The machine trains the joint prediction module to predict joint locations in visual data using the multiple synthetic images. The machine trains the part affinity field prediction module to identify adjacent joints in visual data using the multiple synthetic images. The machine trains a body part prediction module to identify body parts in visual data using the multiple synthetic images. The machine provides a trained human body part segmentation module comprising the trained joint prediction module, the trained part affinity field prediction module, and the trained body part prediction module.

公开(公告)号：US11645506B2

公开(公告)日：2023-05-09

申请号：US17822080

申请日：2022-08-24

Applicant: Microsoft Technology Licensing, LLC

Inventor： Lijuan Wang ,  Kevin Lin ,  Zicheng Liu ,  Kun Luo

IPC: G06V40/10 ,  G06N3/04 ,  G06N3/08 ,  G06T7/10 ,  G06F17/15

CPC classification number: G06N3/0454 ,  G06F17/15 ,  G06N3/08 ,  G06T7/10 ,  G06V40/103 ,  G06T2207/10004 ,  G06T2207/10028 ,  G06T2207/10048 ,  G06T2207/20081

Abstract: A computing system is provided. The computing system includes a processor configured to execute a convolutional neural network that has been trained, the convolutional neural network including a backbone network that is a concatenated pyramid network, a plurality of first head neural networks, and a plurality of second head neural networks. At the backbone network, the processor is configured to receive an input image as input and output feature maps extracted from the input image. The processor is configured to: process the feature maps using each of the first head neural networks to output corresponding keypoint heatmaps; process the feature maps using each of the second head neural networks to output corresponding part affinity field heatmaps; link the keypoints into one or more instances of virtual skeletons using the part affinity fields; and output the instances of the virtual skeletons.