公开(公告)号：US09870637B2

公开(公告)日：2018-01-16

申请号：US14575742

申请日：2014-12-18

Applicant: Intel Corporation

Inventor： Glen J. Anderson ,  Kathy Yuen ,  Omesh Tickoo ,  Jamie Sherman ,  Jeffrey Ota ,  Ravishankar R. Iyer

IPC: G06T13/80 ,  G11B27/00

CPC classification number: G06T13/80 ,  G11B27/00

Abstract: Various systems and methods for frame removal and replacement for stop-action animation are described herein. A system for creating a stop-motion video includes an access module to access a series of frames of an input video, and a processing module to determine whether each frame of the series of frames includes a portion of a hand and composite frames from the series of frames that do not include the portion of the hand to render an output video. A system for creating a video includes an access module to access an input video, and a video processing module to identify a physical object in the input video, track movement of the physical object in the input video to identify a path, identify a three-dimensional model of the physical object, and create an output video with the three-dimensional model in place of the physical object, the three-dimensional model following the path.

公开(公告)号：US09639762B2

公开(公告)日：2017-05-02

申请号：US14477595

申请日：2014-09-04

Applicant: Intel Corporation

Inventor： Shayok Chakraborty ,  Omesh Tickoo ,  Ravishankar Iyer

IPC: G06K9/00 ,  G11B20/10 ,  G11B27/30 ,  G06K9/62

CPC classification number: G06K9/00751 ,  G06K9/6271 ,  G06K2209/27 ,  G11B20/10527 ,  G11B27/30 ,  G11B2020/10537 ,  H04N21/41407 ,  H04N21/4223 ,  H04N21/44008 ,  H04N21/8456 ,  H04N21/8549

Abstract: System, apparatus, method, and computer readable media for on-the-fly captured video summarization. A video stream is incrementally summarized in concurrence with generation of the stream by a camera module. Saliency of the video stream summary is maintained as the stream evolves by updating the summary to include only the most significant frames. In one exemplary embodiment, saliency is determined by optimizing an objective function including terms that are indicative of both the diversity of a selection, and how representative the selection is to the processed portion of the video data corpus. A device platform including a CM and comporting with the exemplary architecture may provide video camera functionality at ultra-low power, and/or with ultra-low storage resources, and/or with ultra-low communication channel bandwidth.

公开(公告)号：US20150161986A1

公开(公告)日：2015-06-11

申请号：US14365603

申请日：2013-12-09

Applicant: INTEL CORPORATION

Inventor： Omesh Tickoo ,  Rameshkumar G. Illikkal ,  Anthony L. Chun ,  Hector A. Cordourier Maruri

IPC: G10L15/06

CPC classification number: G10L15/07

Abstract: In embodiments, apparatuses, methods and storage media for personalized speech recognition are described. In various embodiments, a personalized speech recognition system (“PSRS”) may receive personal speech recognition training data (“PTD”) that is associated with a user to facilitate recognition of speech from the user. The PSRS may train a speech recognition module using the received PTD. The user may provide the PTD using a mobile device under control of the user. The PTD may be generated and stored on the mobile device through actions of the user, such as by using the mobile device to record a corpus of speech examples by the user. The user may subsequently facilitate provisioning of the PTD to the PSRS using the mobile device, such as through a wired or wireless network. Other embodiments may be described and claimed.

Abstract translation: 在实施例中，描述用于个性化语音识别的装置，方法和存储介质。 在各种实施例中，个性化语音识别系统（“PSRS”）可以接收与用户相关联的个人语音识别训练数据（“PTD”），以便于从用户识别语音。 PSRS可以使用接收到的PTD训练语音识别模块。 用户可以在用户的​​控制下使用移动设备来提供PTD。 PTD可以通过用户的动作来生成和存储在移动设备上，例如通过使用移动设备来记录用户语音语料库。 用户随后可以使用移动设备（例如通过有线或无线网络）方便地向PSRS提供PTD。 可以描述和要求保护其他实施例。

公开(公告)号：US11836240B2

公开(公告)日：2023-12-05

申请号：US18157154

申请日：2023-01-20

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: G06F21/44 ,  H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/022 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  G06F9/48 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06F18/24 ,  G06F18/21 ,  G06F18/22 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2413 ,  G06N3/045 ,  G06V30/19 ,  G06V10/82 ,  G06V10/94 ,  G06V10/75 ,  G06V10/20 ,  G06V10/40 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06T7/20 ,  G06F18/243 ,  G06V30/194 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

CPC classification number: G06F21/44 ,  G06F9/4881 ,  G06F9/5044 ,  G06F9/5066 ,  G06F9/5072 ,  G06F16/535 ,  G06F16/538 ,  G06F16/54 ,  G06F16/951 ,  G06F18/21 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2163 ,  G06F18/22 ,  G06F18/24 ,  G06F18/24143 ,  G06F21/45 ,  G06F21/53 ,  G06F21/6254 ,  G06F21/64 ,  G06K15/1886 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N5/022 ,  G06T7/11 ,  G06T7/70 ,  G06V10/20 ,  G06V10/40 ,  G06V10/454 ,  G06V10/75 ,  G06V10/82 ,  G06V10/95 ,  G06V10/96 ,  G06V20/00 ,  G06V30/19173 ,  G06V30/274 ,  G06V40/161 ,  G06V40/20 ,  H04L9/0643 ,  H04L9/3239 ,  H04L67/12 ,  H04L67/51 ,  H04N19/46 ,  H04N19/80 ,  H04W4/70 ,  G06F18/24323 ,  G06F2209/503 ,  G06F2209/506 ,  G06F2221/2117 ,  G06T7/20 ,  G06T7/223 ,  G06T2207/10016 ,  G06T2207/20021 ,  G06T2207/20024 ,  G06T2207/20052 ,  G06T2207/20056 ,  G06T2207/20064 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2207/30242 ,  G06V30/194 ,  G06V2201/10 ,  H04L9/50 ,  H04L67/10 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/42 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  H04N19/625 ,  H04N19/63 ,  H04W12/02

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US11586854B2

公开(公告)日：2023-02-21

申请号：US16830341

申请日：2020-03-26

Applicant: Intel Corporation

Inventor： Nilesh Ahuja ,  Ibrahima Ndiour ,  Javier Felip Leon ,  David Gomez Gutierrez ,  Ranganath Krishnan ,  Mahesh Subedar ,  Omesh Tickoo

IPC: G06K9/00 ,  G06K9/62 ,  G05B13/04 ,  G06N3/084 ,  G05D1/00 ,  G05D1/02 ,  B60W60/00 ,  G05B13/02 ,  G06V20/20 ,  G06V20/58 ,  G06V40/10

Abstract: Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

公开(公告)号：US11531850B2

公开(公告)日：2022-12-20

申请号：US16947590

申请日：2020-08-07

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: G06K9/62 ,  G06F9/48 ,  G06F9/50 ,  G06F16/535 ,  G06F16/538 ,  G06F16/951 ,  G06F21/44 ,  G06F21/45 ,  G06F21/53 ,  G06F21/62 ,  G06F21/64 ,  H04L9/06 ,  G06N5/02 ,  G06N3/04 ,  H04L9/32 ,  H04W4/70 ,  G06F16/54 ,  G06N3/063 ,  G06V10/20 ,  G06V10/40 ,  G06V10/75 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06V30/194 ,  G06T7/20 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

公开(公告)号：US20220191537A1

公开(公告)日：2022-06-16

申请号：US17509246

申请日：2021-10-25

Applicant: Intel Corporation

Inventor： Yiting Liao ,  Yen-Kuang Chen ,  Shao-Wen Yang ,  Vallabhajosyula S. Somayazulu ,  Srenivas Varadarajan ,  Omesh Tickoo ,  Ibrahima J. Ndiour

IPC: H04N19/52 ,  H04N19/523 ,  G06N3/04 ,  G06K9/62 ,  H04N19/172 ,  G06V10/20

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

公开(公告)号：US20210243012A1

公开(公告)日：2021-08-05

申请号：US16948304

申请日：2020-09-11

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06K9/00 ,  G06N3/04 ,  H04L29/08 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06K9/46 ,  G06K9/62 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08 ,  H04N19/80 ,  G06F16/951 ,  G06K9/36 ,  H04N19/46 ,  G06T7/70 ,  G06K9/64 ,  G06K9/72

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US10949674B2

公开(公告)日：2021-03-16

申请号：US16298549

申请日：2019-03-11

Applicant: Intel Corporation

Inventor： Myung Hwangbo ,  Krishna Kumar Singh ,  Teahyung Lee ,  Omesh Tickoo

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

公开(公告)号：US10778412B2

公开(公告)日：2020-09-15

申请号：US16024397

申请日：2018-06-29

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06K9/00 ,  G06N3/04 ,  H04L29/08 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06K9/46 ,  G06K9/62 ,  G06N3/08 ,  H04N19/80 ,  G06F16/951 ,  G06K9/36 ,  H04N19/46 ,  G06T7/70 ,  G06K9/64 ,  G06K9/72 ,  H04W12/02 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.