Microsoft Technology Licensing, LLC

Boe Technology Group Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Other

Microsoft Corporation

Mingjing Li

Hong-Jiang Zhang

Lei Zhang

Wei-Ying Ma

Zhiwei Li

Bin Wang

Jianfeng Gao

Yuxiao Hu

Zheng Chen

Lie Lu

Xian-Sheng Hua

Yan-Feng Sun

Xiaoguang Rui

Jue Wang

Hao Fang Wu

Yanfeng Sun

Xingfeng Ren

Mingjian Yu

Lei Wu

Liu Wenyin

Dongfang Li

Yuanhao Chen

Feng Zhang

Jing Liu

Ji-Rong Wen

Rui Yang

Zhen Chen

Yi Zhou

Rong Xiao

David R Parlin

Wen-Yin Liu

Kai-Fu Lee

Feng Jing

Yu-Fei Ma

Jian-Yun Nie

Lee-Feng Chien

Long Zhu

Hai-Feng Wang

Lie Gu

Dong-Feng Cai

Longbin Chen

Jing Lui

Chang-Hu Wang

An array substrate and a display panel are provided. The array substrate includes a base substrate and a first conductive layer, a first insulating layer, a second conductive layer and a third conductive layer which are sequentially stacked on the base substrate, the first insulating layer insulates the first conductive layer from the second conductive layer, the first conductive layer includes a first signal line, the second conductive layer includes a second signal line and a first connection part spaced apart from each other, the third conductive layer includes a second connection part, the first connection part is electrically connected with the first signal line through a first via hole in the first insulating layer; the second connection part is electrically connected with the first connection part and the second signal line to constitute a connection structure electrically connecting the first signal line with the second signal line.

Array substrate and display panel

An array substrate, a manufacturing method thereof and a display panel are provided, the array substrate includes a base substrate and a first conductive layer, a first insulating layer, a second conductive layer and a third conductive layer which are sequentially stacked on the base substrate, the first insulating layer insulates the first conductive layer from the second conductive layer, the first conductive layer includes a first signal line, the second conductive layer includes a second signal line and a first connection part spaced apart from each other, the third conductive layer includes a second connection part, the first connection part is electrically connected with the first signal line through a first via hole in the first insulating layer; the second connection part is electrically connected with the first connection part and the second signal line to constitute a connection structure electrically connecting the first signal line with the second signal line.

Array substrate and manufacturing method thereof, display panel

Statistical approaches to large-scale image annotation are described. Generally, the annotation technique includes compiling visual features and textual information from a number of images, hashing the images visual features, and clustering the images based on their hash values. An example system builds statistical language models from the clustered images and annotates the image by applying one of the statistical language models.

Statistical approach to large-scale image annotation

A dual cross-media relevance model (DCMRM) is used for automatic image annotation. In contrast to the traditional relevance models which calculate the joint probability of words and images over a training image database, the DCMRM model estimates the joint probability by calculating the expectation over words in a predefined lexicon. The DCMRM model may be advantageous because a predefined lexicon potentially has better behavior than a training image database. The DCMRM model also takes advantage of content-based techniques and image search techniques to define the word-to-image and word-to-word relations involved in image annotation. Both relations can be estimated by using image search techniques on the web data as well as available training data.

Dual cross-media relevance model for image annotation

Word correlations are estimated using a content-based method, which uses visual features of image representations of the words. The image representations of the subject words may be generated by retrieving images from data sources (such as the Internet) using image search with the subject words as query words. One aspect of the techniques is based on calculating the visual distance or visual similarity between the sets of retrieved images corresponding to each query word. The other is based on calculating the visual consistence among the set of the retrieved images corresponding to a conjunctive query word. The combination of the content-based method and a text-based method may produce even better result.

Estimating word correlations from images

Improvements are provided to effectively assess a user's face and head pose such that a computer or like device can track the user's attention towards a display device(s). Then the region of the display or graphical user interface that the user is turned towards can be automatically selected without requiring the user to provide further inputs. A frontal face detector is applied to detect the user's frontal face and then key facial points such as left/right eye center, left/right mouth corner, nose tip, etc., are detected by component detectors. The system then tracks the user's head by an image tracker and determines yaw, tilt and roll angle and other pose information of the user's head through a coarse to fine process according to key facial points and/or confidence outputs by pose estimator.

Head pose assessment methods and systems

Techniques for identifying copied images based on an original image are described. The identifying copied image is based on creating unique and identifiable features that in turn are used to generate multiple histograms. The histograms are generated by patches of the image, where the patches are created by equally dividing the image. The combined patch histograms are representative of the image.

Patch-based texture histogram coding for fast image similarity search

Systems and methods for bipartite graph reinforcement modeling to annotate web images are described. In one aspect the systems and methods implement bipartite graph reinforcement modeling operations to identify a set of annotations that are relevant to a Web image. The systems and methods annotate the Web image with the identified annotations. The systems and methods then index the annotated Web image. Responsive to receiving an image search query from a user, wherein the image search query comprises information relevant to at least a subset of the identified annotations, the image search engine service presents the annotated Web image to the user.

Bipartite graph reinforcement modeling to annotate web images

Systems and methods for visual language modeling for image classification are described. In one aspect the systems and methods model training images corresponding to multiple image categories as matrices of visual words. Visual language models are generated from the matrices. In view of a given image, for example, provided by a user or from the Web, the systems and methods determine an image category corresponding to the given image. This image categorization is accomplished by maximizing the posterior probability of visual words associated with the given image over the visual language models. The image category, or a result corresponding to the image category, is presented to the user.

Visual language modeling for image classification

An advertisement image classification system trains a binary classifier to classify images as advertisement images or non-advertisement images and then uses the binary classifier to classify images of web pages as advertisement images or non-advertisement images. During a training phase, the classification system generates training data of feature vectors representing the images and labels indicating whether an image is an advertisement image or a non-advertisement image. The classification system trains a binary classifier to classify images using training data. During a classification phase, the classification system inputs a web page with an image and generates a feature vector for the image. The classification system then applies the trained binary classifier to the feature vector to generate a score indicating whether the image is an advertisement image or a non-advertisement image.

Classification of images as advertisement images or non-advertisement images

A method and system for propagating the relevance of labeled documents to a query to unlabeled documents is provided. The propagation system provides training data that includes queries, documents labeled with their relevance to the queries, and unlabeled documents. The propagation system then calculates the similarity between pairs of documents in the training data. The propagation system then propagates the relevance of the labeled documents to similar, but unlabeled, documents. The propagation system may iteratively propagate labels of the documents until the labels converge on a solution. The training data with the propagated relevances can then be used to train a ranking function.

Propagating relevance from labeled documents to unlabeled documents

Training a ranking function using propagated document relevance

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Beijing, China

Mingjing Li