Huawei Technologies Co., Limited

Teradici Corporation

Zhan Xu

Qiang Tang

Zili Yi

Tao Yang

David Victor Hobbs

Bo Tong Liu

Kevin Cannons

Vishnu Sanjay Ramiya Srinivasan

Shekoofeh Azizi

Baogang Xie

Daesik Jang

Tahmid Z Chowdhury

Mohammad Asiful Hossain

Methods and systems for high-resolution image manipulation are disclosed. An original high-resolution image to be manipulated is obtained, as well as a driving signal indicating a manipulation result. The original high-resolution image is down-sampled to obtain a low-resolution image to be manipulated. Using a trained manipulation generator, a low-resolution manipulated image and a motion field are generated from the low-resolution image. The motion field represent pixel displacements of the low-resolution image to obtain the manipulation indicated by the driving signal. A high-frequency residual image is computed from the original high-resolution image. A high-frequency manipulated residual image is generated using the motion field. A high-resolution manipulated image is outputted by combining the high-frequency manipulated residual image and a low-frequency manipulated image generated from the low-resolution manipulated image by up-sampling.

Methods and systems for high definition image manipulation with neural networks

Methods and systems for fully-automatic image processing to detect and remove unwanted people from a digital image of a photograph. The system includes the following modules: 1) Deep neural network (DNN)-based module for object segmentation and head pose estimation; 2) classification (or grouping) of wanted versus unwanted people based on information collected in the first module; 3) image inpainting of the unwanted people in the digital image. The classification module can be rules-based in an example. In an example, the DNN-based module generates, from the digital image: 1. A list of object category labels, 2. A list of object scores, 3. A list of binary masks, 4. A list of object bounding boxes, 5. A list of crowd instances, 6. A list of human head bounding boxes, and 7. A list of head poses (e.g., yaws, pitches, and rolls).

Machine-learning model, methods and systems for removal of unwanted people from photographs

Methods and systems for high-resolution image inpainting are disclosed. An original high-resolution image to be inpainted is obtained, as well as an inpainting mask indicating an inside-mask area to be inpainted. The original high-resolution image is down-sampled to obtain a low-resolution image to be inpainted. Using a trained inpainting generator, a low-resolution inpainted image and a set of attention scores are generated from the low-resolution image. The attention scores represent the similarity between inside-mask regions and outside-mask regions. A high-frequency residual image is computed from the original high-resolution image. An aggregated high-frequency residual image is generated using the attention scores, including high-frequency residual information for the inside-mask area. A high-resolution inpainted image is outputted by combining the aggregated high-frequency residual image and a low-frequency inpainted image generated from the low-resolution inpainted image.

Method and system for high-resolution image inpainting

A crowd motion summarization method that provides a rich, real-time description of the crowd's characteristics from a video, such as, speed, orientation, count, spatial locations, and time. A feature tracking module receives each video frame and detects features (feature points) from the video frame. A crowd occupancy detection module receives the video frame and generates a binary crowd occupancy map having human pixel positions which indicate the human location versus non-human location, and generates a total human count of humans detected in the video frame. The feature tracking module generates feature tracking information for only those features contained in the human pixel positions which indicate the human location. In an example, the detected features are Kanade-Lucas-Tomasi (KLT) features. A feature-crowd matching module generates, using the feature tracking information and the total human count: crowd motion data. The method outputs the crowd motion data.

Methods and systems for crowd motion summarization via tracklet based human localization

A cell radio network temporary identifier (C-RNTI) allocation method and device are disclosed. The method includes: when UE accesses a serving cell, randomly determining, by an entity at which the serving cell is located, a start position for selecting a C-RNTI, selecting a C-RNTI for the UE within a C-RNTI interval range starting from the start position, and allocating the selected C-RNTI to the UE. So the entity at which the serving cell is located randomizes the start position of allocation performed by the entity and uses the start position as a start position of an allocation interval, so that allocation of the C-RNTI is randomized.

Cell radio network temporary identifier C-RNTI allocation method and device

An uplink power control method and apparatus are provided. The method is performed by a base station and includes: determining, according to a first quantity of accesses and a second quantity of accesses, whether to adjust a cell-level uplink power parameter, where the first quantity of accesses is a count of times that UEs access a cell of the base station by sending a random access preamble for one time, and the second quantity of accesses is a count of times that the UEs access the cell by sending a random access preamble for at least two times; and adjusting, when determining to adjust the cell-level uplink power parameter, the cell-level uplink power parameter, and sending an adjusted parameter to UE within the coverage area of the cell, so that the UE sends an uplink signal according to the adjusted cell-level uplink power parameter, thereby saving a measurement resource.

Uplink power control method and apparatus

A method and apparatus for encoding a frame from a mixed content image sequence. In one embodiment, the method, executed under the control of a processor configured with computer executable instructions, comprises (i) generating, by an encoding processor, an image type mask that divides the frame into an unchanged portion, an object portion and a picture portion; (ii) producing lossless encoded content, by the encoding processor, from the object portion and the image type mask; (iii) generating, by the encoding processor, a filtered facsimile from the frame, the filtered facsimile generated by retaining the picture portion and filling the unchanged portion and the object portion with neutral image data; and (iv) producing, by the encoding processor, lossy encoded content from the filtered facsimile.

Method and apparatus for encoding mixed content image sequences

A method and system for communicating a computer rendered image sequence from a host computer to a remote computer. The method comprises determining, at the host computer, while performing a progressive encoding of an image portion of the computer rendered image sequence, motion of the image portion, wherein the progressive encoding comprises generating a lossy encoding of a frequency transform of the image portion and a first refinement encoding of the frequency transform; generating, at the host computer, a motion vector representing the motion; and communicating, from the host computer to the remote computer, the lossy encoding, the first refinement encoding, and the motion vector.

Growing community of inventors

Richmond, CA, United States of America

Zhan Xu