Microsoft Technology Licensing, LLC

Boe Technology Group Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Microsoft Corporation

Qiong Yang

Xiaoou Tang

Fang Wen

David Patrick Vronay

Leizhong Zhang

Ta Bao

Chunhui Zhang

Yang Xia

Liang Peng

Song Wu

Rong Xiao

Tong Wang

Sai Zhang

Quan Wen Zhang

Yi Deng

Shijun Li

Zengli Liu

Deyong Wang

Chao Wang

Randy Yuan Kong

Xueyin Lin

Dian Gong

Xintao Bao

Zhongfu Ye

Beifan Ma

Deguo He

Mianen Chen

The present disclosure provides a target mounting fixture, including: a target fixing frame for fixing a target, the target fixing frame including a hoisting portion; a target carrier detachably connected to the target fixing frame; and a moving unit connected to the target carrier for moving the target carrier to move the target.

Target mounting fixture

Embodiments of the present invention provide a display panel and a display device. The display panel comprises a first substrate and a second substrate which are disposed by cell-aligning, and light for displaying exits from the second substrate side. The display panel further comprises an optics element, the optics element being disposed at the light-emergent side of the second substrate to enable the light exiting from a light-emergent side of the second substrate to pass through the optics element and then cover a bezel area of the display panel. The display panel enables light exiting from a light-emergent side of a second substrate to pass through the optics element and then cover the bezel area of the display panel, by disposing the optics element, thereby enabling an image displayed by the display panel to cover the bezel area of the display panel and then achieving a non-bezel displaying.

Display panel and display device

Systems and methods of segmenting images are disclosed. The similarity of images in a set of images is compared. A group of images is selected from the set of images. The images in the group of images are selected based on compared similarities among the images. An informative image is selected from the group of images. User-defined semantic information of the informative image is received. The group of images is modeled as a graph. Each image in the group of images denotes a node in the graph. Edges of the graph denote a foreground or background relationship between images. One or more images in the group of images may be automatically segmented by propagating semantic information of the informative image to images in the group having a graph node corresponding to the informative image. Segmentation results can be refined according to user provided image semantics.

Active segmentation for groups of images

New devices, systems and methods are hereby provided that enable a user to open a communication channel by selecting an image on a monitor. A device that includes a monitor and a graphical position user input mechanism is configured to display one or more images on the monitor, to receive a graphical position user input selecting one of the images, and to open a communication channel directed to a contact information element associated with the selected image. For example, this may include placing a phone call to a selected recipient, or opening an email, instant messaging, or text message composition panel addressed to a selected recipient, by touching, clicking on, or otherwise selecting an image of the recipient, or an image that is associated with the recipient.

Graphical communication user interface with graphical position user input mechanism for selecting a display image

Systems and methods are described for learning visual object cutout from a single example. In one implementation, an exemplary system determines the color context near each block in a model image to create an appearance model. The system also learns color sequences that occur across visual edges in the model image to create an edge profile model. The exemplary system then infers segmentation boundaries in unknown images based on the appearance model and edge profile model. In one implementation, the exemplary system minimizes the energy in a graph-cut model where the appearance model is used for data energy and the edge profile is used to modulate edges. The system is not limited to images with nearly identical foregrounds or backgrounds. Some variations in scale, rotation, and viewpoint are allowed.

Learning object cutout from a single example

The handling of occlusions in stereo imaging is disclosed. In one implementation, an association between a discontinuity in one stereo image and an occlusion in a second stereo image is utilized. In such an implementation, the first and second stereo images are segmented. A mapping of a discontinuity within the second stereo image is used to form at least part of a boundary of an occlusion in the first stereo image. The mapped discontinuity is found at a boundary between two segments in the second stereo image, and once mapped, divides a segment in the first stereo image into two patches. An energy calculation is made in an iterative manner, alternating with changes to a solution with the disparities and occlusions of the patches. Upon minimization, disparities and occlusions at the patch and pixel level are available.

Occlusion handling in stereo imaging

An interactive photo annotation method uses clustering based on facial similarities to improve annotation experience. The method uses a face recognition algorithm to extract facial features of a photo album and cluster the photos into multiple face groups based on facial similarity. The method annotates a face group collectively using annotations, such as name identifiers, in one operation. The method further allows merging and splitting of face groups. Special graphical user interfaces, such as displays in a group view area and a thumbnail area and drag-and-drop features, are used to further improve the annotation experience.

Interactive photo annotation based on face clustering

Systems and methods of segmenting images are disclosed herein. The similarity of images in a set of images is compared. A group of images is selected from the set of images. The images in the group of images are selected based on compared similarities among the images. An informative image is selected from the group of images. User-defined semantic information of the informative image is received. The group of images as a graph is modeled as a graph. Each image in the group of images denotes a node in the graph. Edges of the graph denote a foreground relationship between images or a background relationship between images. One or more images in the group of images are automatically segmented by propagating the semantic information of the informative image to images in the group of images having a corresponding graph node that is related to a graph node corresponding to the informative image. Segmentation results can be refined according to user provided image semantics.

Exemplary systems and methods use micro-structure modeling of an image for extracting image features. The micro-structure in an image is modeled as a Markov Random Field, and the model parameters are learned from training images. Micro-patterns adaptively designed from the modeled micro-structure capture spatial contexts of the image. In one implementation, a series of micro-patterns based on the modeled micro-structure can be automatically designed for each block of the image, providing improved feature extraction and recognition because of adaptability to various images, various pixel attributes, and various sites within an image.

Modeling micro-structure for feature extraction

A search includes comparing a query image provided by a user to a plurality of stored images of faces stored in a stored image database, and determining a similarity of the query image to the plurality of stored images. One or more resultant images of faces, selected from among the stored images, are displayed to the user based on the determined similarity of the stored images to the query image provided by the user. The resultant images are displayed based at least in part on one or more facial features.

Image-based face search

A Bayesian competitive model integrated with a generative classifier for unspecific person verification is described. In one aspect, a competitive measure for verification of an unspecific person is calculated using a discriminative classifier. The discriminative classifier is based on a Bayesian competitive model that is adaptable to unknown new classes. The Bayesian competitive model is integrated with a generative verification in view of a set of confidence criteria to make a decision regarding verification of the unspecific person.

Bayesian competitive model integrated with a generative classifier for unspecific person verification

Salience-preserving image fusion is described. In one aspect, multi-channel images are fused into a single image. The fusing operations are based on importance-weighted gradients. The importance weighted gradients are measured using respective salience maps for each channel in the multi-channel images.

Growing community of inventors

Beijing, China

Qiong Yang