-
公开(公告)号：US20180250826A1
公开(公告)日：2018-09-06
申请号：US15449541
申请日：2017-03-03
Applicant: Futurewei Technologies, Inc.
CPC classification number: B25J9/1697 , G06K9/00664 , G06K9/2027 , G06K9/3233 , G06K9/6248 , G06K9/6255 , G06K9/6256 , G06K9/6269 , G06K9/627 , H04N7/185
Abstract: A method for fine-grained object recognition in a robotic system is disclosed that includes obtaining an image of an object from an imaging device. Based on the image, a deep category-level detection neural network is used to detect pre-defined categories of objects. A feature map is generated for each pre-defined category of object detected by the deep category-level detection neural network. Embedded features are generated, based on the feature map, using a deep instance-level detection neural network corresponding to the pre-defined category of the object, wherein each pre-defined category of an object comprises a corresponding different instance-level detection neural network. An instance-level of the object is determined based on classification of the embedded features.
-
公开(公告)号：US20180174038A1
公开(公告)日：2018-06-21
申请号：US15383804
申请日：2016-12-19
Applicant: Futurewei Technologies, Inc.
CPC classification number: G06N3/08 , B25J9/163 , G06N3/008 , G06N3/0454 , G06N7/005 , Y10S901/47
Abstract: A robotic device is disclose as having deep reinforcement learning capability. The device includes non-transitory memory comprising instructions and one or more processors in communication with the memory. The instructions cause the one or more processors to receive a sensing frame, from a sensor, comprising an image. The processors then determine a movement transition based on the sensing frame and the deep reinforcement learning, wherein the deep reinforcement learning uses at least one of a map coverage reward, a map quality reward, or a traversability reward to determine the movement transition. The processors then update an area map based on the sensing frame and the deep reinforcement learning using a visual simultaneous localization and mapping (SLAM) process to determine the map updates.
-
公开(公告)号：US09854168B2
公开(公告)日：2017-12-26
申请号：US14642469
申请日：2015-03-09
Applicant: Futurewei Technologies, Inc.
Inventor： Zhenyu Wu , Wei Jiang , John Wus , Hong Heather Yu
CPC classification number: H04N5/23267 , G06T7/246 , G06T7/277 , G06T2207/10016 , G06T2207/20016 , H04N5/23251
Abstract: A device is disclosed comprising a memory configured for holding video and a processor coupled to the memory. The memory contains computer-executable instructions that, when executed by the processor, cause the device to perform operations to stabilize the video, the operations comprising buffering consecutive original video frames, determining transformation matrices from subsets of the original video frames, wherein the transformation matrices represent estimates of stable camera motion, using the transformation matrices to warp the original video frames and generate video that is stabilized relative to the original video frames, and adjusting a size of a subset of original video frames in response to detecting a condition.
-
24.发明申请公开(公告)号：US20170116741A1
公开(公告)日：2017-04-27
申请号：US14922518
申请日：2015-10-26
Applicant: Futurewei Technologies, Inc.
CPC classification number: G11B27/031 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/174 , G06T2207/10016 , G06T2207/10024
Abstract: Embodiments are provided for achieving multi-view video foreground-background segmentation with spatial-temporal graph cuts. A multi-view segmentation algorithm is used where a four-dimensional (4D) graph-cut is constructed by adding links across neighboring views over space and for consecutive frames over time. The segmentation uses both the color values of each input image and the image difference between the input image and the background image to obtain an initial graph-cut, before adding the temporal and spatial links. By using the background subtraction results as the initial segmentation seed, no user annotation is needed to perform multi-view segmentation.
-
-
-
Search Results
24
records
(took 0.018 seconds)
