公开(公告)号：US11769336B2

公开(公告)日：2023-09-26

申请号：US17377422

申请日：2021-07-16

Applicant: Intel Corporation

Inventor： Daniel Pohl ,  Maik Fox

IPC: G06K9/00 ,  G08G1/095 ,  G06N5/04 ,  G08G1/07 ,  G08G1/01 ,  G06V20/58

CPC classification number: G06V20/584 ,  G06N5/04 ,  G08G1/0145 ,  G08G1/07 ,  G08G1/095

Abstract: An infrastructure state prediction device includes a processor configured to receive sensor information from a sensor at an infrastructure element, wherein the sensor information includes an observation of a traffic object at the infrastructure element; and determine, based on the sensor information and an infrastructure state model comprising information indicative of a state timing for the infrastructure element, an updated state timing for the infrastructure element; and a transmitter configured to transmit the updated state timing to the infrastructure element.

公开(公告)号：US11620729B2

公开(公告)日：2023-04-04

申请号：US17573392

申请日：2022-01-11

Applicant: Intel Corporation

Inventor： Daniel Pohl

IPC: G06T3/40 ,  G06T15/06 ,  G06T7/20 ,  G06T5/50 ,  G06T5/00

Abstract: Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.

公开(公告)号：US11526964B2

公开(公告)日：2022-12-13

申请号：US16898116

申请日：2020-06-10

Applicant: Intel Corporation

Inventor： Daniel Pohl ,  Carl Marshall ,  Selvakumar Panneer

IPC: G06T5/00 ,  G06N3/08 ,  G06N3/04 ,  G06T15/00 ,  G06T1/20

Abstract: An apparatus to facilitate deep learning based selection of samples for adaptive supersampling is disclosed. The apparatus includes one or more processing elements to: receive training data comprising input tiles and corresponding supersampling values for the input tiles, wherein each input tile comprises a plurality of pixels, and train, based on the training data, a machine learning model to identify a level of supersampling for a rendered tile of pixels.

公开(公告)号：US11289078B2

公开(公告)日：2022-03-29

申请号：US16456523

申请日：2019-06-28

Applicant: Intel Corporation

Inventor： Daniel Pohl

IPC: G10L15/00 ,  G10L15/26 ,  G10L15/22 ,  G06F3/16 ,  H04N5/232 ,  G06N3/08 ,  G06F40/56

Abstract: An apparatus, method and computer readable medium for a voice-controlled camera with artificial intelligence (AI) for precise focusing. The method includes receiving, by the camera, natural language instructions from a user for focusing the camera to achieve a desired photograph. The natural language instructions are processed using natural language processing techniques to enable the camera to understand the instructions. A preview image of a user desired scene is captured by the camera. Artificial Intelligence (AI) is applied to the preview image to obtain context and to detect objects within the preview image. A depth map of the preview image is generated to obtain distances from the detected objects in the preview image to the camera. It is determined whether the detected objects in the image match the natural language instructions from the user.

公开(公告)号：US11237572B2

公开(公告)日：2022-02-01

申请号：US16233236

申请日：2018-12-27

Applicant: Intel Corporation

Inventor： Andre Ryll ,  Daniel Pohl ,  Markus Achtelik ,  Bastian Jaeger ,  Jan Willem Vervoorst

IPC: G05D1/10 ,  G06T7/50 ,  B64C39/02 ,  B64D47/08 ,  G05D1/00

Abstract: According to various aspects, a collision avoidance method may include: receiving depth information of one or more depth imaging sensors of an unmanned aerial vehicle; determining from the depth information a first obstacle located within a first distance range and movement information associated with the first obstacle; determining from the depth information a second obstacle located within a second distance range and movement information associated with the second obstacle, the second distance range is distinct from the first distance range, determining a virtual force vector based on the determined movement information, and controlling flight of the unmanned aerial vehicle based on the virtual force vector to avoid a collision of the unmanned aerial vehicle with the first obstacle and the second obstacle.

公开(公告)号：US11086340B2

公开(公告)日：2021-08-10

申请号：US16664965

申请日：2019-10-28

Applicant: Intel Corporation

Inventor： Daniel Pohl ,  Daniel Gurdan ,  Roman Schick ,  Tim Ranft

IPC: G05D1/12 ,  B64C39/02 ,  B64D47/08 ,  B64D47/02 ,  G09F21/06 ,  G05D1/10 ,  G08G5/00

Abstract: A system for unmanned aerial vehicle alignment including: an image sensor, configured to obtain an image of unmanned aerial vehicles and provide to a processor image data corresponding to the obtained image, the processor, configured to determine from the image data image positions of the unmanned aerial vehicles, determine an average position of the unmanned aerial vehicles relative to a first axis based on the image positions, determine an average line that extends along a second axis through the average position, wherein the first and second axes are perpendicular to each other, determine a target position of one of the unmanned aerial vehicles based on a relationship between its respective image position and a target alignment, and determine an adjustment instruction to direct said one of the unmanned aerial vehicles toward the target position, and provide the adjustment instruction to said one of the unmanned aerial vehicles.

公开(公告)号：US10970538B2

公开(公告)日：2021-04-06

申请号：US16181921

申请日：2018-11-06

Applicant: Intel Corporation

Inventor： Radhakrishnan Venkataraman ,  James M. Holland ,  Sayan Lahiri ,  Pattabhiraman K ,  Kamal Sinha ,  Chandrasekaran Sakthivel ,  Daniel Pohl ,  Vivek Tiwari ,  Philip R. Laws ,  Subramaniam Maiyuran ,  Abhishek R. Appu ,  Eimoustapha Ould-Ahmed-Vall ,  Peter L. Doyle ,  Devan Burke

IPC: G09G5/391 ,  G06K9/00 ,  G09G5/10 ,  G06F3/01 ,  G06F3/147 ,  G09G5/00 ,  G09G5/393 ,  G09G5/36

Abstract: Systems, apparatuses, and methods may provide for technology to dynamically control a display in response to ocular characteristic measurements of at least one eye of a user.

公开(公告)号：US10789675B2

公开(公告)日：2020-09-29

申请号：US16236023

申请日：2018-12-28

Applicant: Intel Corporation

Inventor： Daniel Pohl

IPC: G06T3/40 ,  G06T15/06 ,  G06T7/20 ,  G06T5/50 ,  G06T5/00

Abstract: Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.

公开(公告)号：US20180061117A1

公开(公告)日：2018-03-01

申请号：US15251180

申请日：2016-08-30

Applicant: Intel Corporation

Inventor： Daniel Pohl

IPC: G06T15/20 ,  G06F3/01 ,  G06T15/00 ,  G06T3/40

CPC classification number: G06T3/0012 ,  G06F3/011 ,  G06F3/013 ,  G06T15/005 ,  G06T19/006 ,  G06T2210/36 ,  H04N13/117 ,  H04N13/279 ,  H04N13/344

Abstract: Embodiments described herein provide for blind spot rendering optimizations for eye tracking head mounted displays. One embodiment provides an apparatus comprising first logic to receive eye-tracking data from an eye tracking system, second logic to determine a blind spot region for a scene based on the eye tracking data, and third logic to provide identifying data for the blind spot region to a renderer. The renderer is configured to render pixels of the scene that fall within the blind spot region at a lower rendering quality relative to the remainder of the scene.

公开(公告)号：US12185210B2

公开(公告)日：2024-12-31

申请号：US17253998

申请日：2018-12-24

Applicant: Intel Corporation

Inventor： Daniel Pohl ,  Tomer Rider ,  Wenlong Yang

IPC: H04W4/80 ,  G06F3/01 ,  G06F13/22 ,  G06T7/70 ,  H04B7/26 ,  H04B17/318 ,  H04W12/63

Abstract: The present disclosure is directed at pairing a host electronic device with a peripheral electronic device using visual recognition and deep learning techniques. In particular, the host device may receive an indication of a peripheral device via a camera or as a result of searching for the peripheral device (e.g., due startup of a related application or periodic scanning). The host device may also receive an image of the peripheral device (e.g., captured via the camera), and determine a visual distance to the peripheral device based on the image. The host device may also determine a signal strength of the peripheral device, and determine a signal distance to the peripheral device based on the signal strength. The host device may pair with the peripheral device if the visual distance and the signal distance are approximately equal.