公开(公告)号：US20240127408A1

公开(公告)日：2024-04-18

申请号：US18514252

申请日：2023-11-20

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

IPC: G06T5/00 ,  G06N3/04

CPC classification number: G06T5/002 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.

公开(公告)号：US20210279896A1

公开(公告)日：2021-09-09

申请号：US17255837

申请日：2018-09-28

Applicant: INTEL CORPORATION

Inventor： Xiaofeng Tong ,  Chen Ling ,  Ming Lu ,  Qiang Li ,  Wenlong Li ,  Yikai Fang ,  Yumeng Wang

IPC: G06T7/292 ,  G06K9/00 ,  G06K9/62 ,  G06T7/215 ,  G06T7/70 ,  H04N7/18

Abstract: A multi-camera architecture for detecting and tracking a ball in real-time. The multi-camera architecture includes network interface circuitry to receive a plurality of real-time videos taken from a plurality of high-resolution cameras. Each of the high-resolution cameras simultaneously captures a sports event, wherein each of the plurality of high-resolution cameras includes a viewpoint that covers an entire playing field where the sports event is played. The multi-camera architecture further includes one or more processors coupled to the network interface circuitry and one or more memory devices coupled to the one or more processors. The one or more memory devices includes instructions to determine the location of the ball for each frame of the plurality of real-time videos, which when executed by the one or more processors, cause the multi-camera architecture to simultaneously perform one of a detection scheme or a tracking scheme on a frame from each of the plurality of real-time videos to detect the ball used in the sports event and perform a multi-camera build to determine a location of the ball in 3D for the frame from each of the plurality of real-time videos using one of detection or tracking results for each of the cameras.

公开(公告)号：US20210142448A1

公开(公告)日：2021-05-13

申请号：US17090170

申请日：2020-11-05

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

IPC: G06T5/00 ,  G06N3/04

Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.

公开(公告)号：US20190228556A1

公开(公告)日：2019-07-25

申请号：US16327779

申请日：2016-09-21

Applicant: Intel Corporation

Inventor： Shandong Wang ,  Ming Lu ,  Anbang Yao ,  Yurong Chen

IPC: G06T13/40 ,  G06T17/00 ,  G06F3/01 ,  G06T15/04 ,  G06T15/10

CPC classification number: G06T13/40 ,  G06F3/01 ,  G06T15/04 ,  G06T15/10 ,  G06T17/00

Abstract: Techniques related to estimating accurate face shape and texture from an image having a representation of a human face are discussed. Such techniques may include determining shape parameters that optimize a linear spatial cost model based on 2D landmarks, 3D landmarks, and camera and pose parameters, determining texture parameters that optimize a linear texture estimation cost model, and refining the shape parameters by optimizing a nonlinear pixel intensity cost function.

公开(公告)号：US11861907B2

公开(公告)日：2024-01-02

申请号：US17626994

申请日：2019-08-13

Applicant: Intel Corporation

Inventor： Yikai Fang ,  Qiang Li ,  Wenlong Li ,  Haihua Lin ,  Chen Ling ,  Ming Lu ,  Hongzhi Tao ,  Xiaofeng Tong ,  Yumeng Wang

IPC: G06V20/40 ,  H04N5/232 ,  G06V40/10 ,  G06V10/74 ,  G06V10/82 ,  H04N23/80

CPC classification number: G06V20/42 ,  G06V10/761 ,  G06V10/82 ,  G06V20/47 ,  G06V40/103 ,  H04N23/80 ,  G06V2201/07

Abstract: Methods, systems and apparatuses may provide for technology that selects a player from a plurality of players based on an automated analysis of two-dimensional (2D) video data associated with a plurality of cameras, wherein the selected player is nearest to a projectile depicted in the 2D video data. The technology may also track a location of the selected player over a subsequent plurality of frames in the 2D video data and estimate a location of the projectile based on the location of the selected player over the subsequent plurality of frames.

公开(公告)号：US20230154092A1

公开(公告)日：2023-05-18

申请号：US17914314

申请日：2020-04-23

Applicant: Intel Corporation

Inventor： Shandong Wang ,  Yangyuxuan Kang ,  Anbang Yao ,  Ming Lu ,  Yurong Chen

IPC: G06T13/40 ,  G06T7/70

CPC classification number: G06T13/40 ,  G06T7/70 ,  G06T2207/30196 ,  G06T2207/10024 ,  G06T2207/20084

Abstract: Techniques are disclosed for providing improved pose tracking of a subject using a 2D camera and generating a 3D image that recreates the pose of the subject. A 3D skeleton map is estimated from a 2D skeleton map of the subject using, for example, a neural network. A template 3D skeleton map is accessed or generated having bone segments that have lengths set using, for instance, anthropometry statistics based on a given height of the template 3D skeleton map. An improved 3D skeleton map is then produced by at least retargeting one or more of the plurality of bone segments of the estimated 3D skeleton map to more closely match the corresponding template bone segments of the template 3D skeleton map. The improved 3D skeleton map can then be animated in various ways (e.g., using various skins or graphics) to track corresponding movements of the subject.

公开(公告)号：US11640526B2

公开(公告)日：2023-05-02

申请号：US16609735

申请日：2018-05-22

Applicant: INTEL CORPORATION

Inventor： Yiwen Guo ,  Anbang Yao ,  Hao Zhao ,  Ming Lu ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/082 ,  G06N3/084 ,  G06N5/00

Abstract: Methods and apparatus are disclosed for enhancing a neural network using binary tensor and scale factor pairs. For one example, a method of optimizing a trained convolutional neural network (CNN) includes initializing an approximation residue as a trained weight tensor for the trained CNN. A plurality of binary tensors and scale factor pairs are determined. The approximation residue is updated using the binary tensors and scale factor pairs.

公开(公告)号：US20220207678A1

公开(公告)日：2022-06-30

申请号：US17482998

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Shandong Wang ,  Yurong Chen ,  Sungye Kim ,  Attila Tamas Afra

IPC: G06T5/50 ,  G06K9/00 ,  G06T7/13 ,  G06N3/02

Abstract: The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

公开(公告)号：US20200167654A1

公开(公告)日：2020-05-28

申请号：US16615097

申请日：2018-05-23

Applicant: INTEL CORPORATION

Inventor： Yiwen Guo ,  Anbang Yao ,  Hao Zhao ,  Ming Lu ,  Yurong CHEN

IPC: G06N3/08 ,  G06F16/22 ,  G06N5/00 ,  G06N3/04

Abstract: Methods and apparatus are disclosed for enhancing a binary weight neural network using a dependency tree. A method of enhancing a convolutional neural network (CNN) having binary weights includes constructing a tree for obtained binary tensors, the tree having a plurality of nodes beginning with a root node in each layer of the CNN. A convolution is calculated of an input feature map with an input binary tensor at the root node of the tree. A next node is searched from the root node of the tree and a convolution is calculated at the next node using a previous convolution result calculated at the root node of the tree. The searching of a next node from root node is repeated for all nodes from the root node of the tree, and a convolution is calculated at each next node using a previous convolution result.

公开(公告)号：US12124533B2

公开(公告)日：2024-10-22

申请号：US17482875

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Scott Janus ,  Sungye Kim

IPC: G06F18/2136 ,  G06T11/00

CPC classification number: G06F18/2136 ,  G06T11/00 ,  G06T2207/20076 ,  G06T2207/20081

Abstract: Embodiments are generally directed to methods and apparatuses of spatially sparse convolution module for visual rendering and synthesis. An embodiment of a method for image processing, comprising: receiving an input image by a convolution layer of a neural network to generate a plurality of feature maps; performing spatially sparse convolution on the plurality of feature maps to generate spatially sparse feature maps; and upsampling the spatially sparse feature maps to generate an output image.