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    FAST DENSE PATCH SEARCH AND QUANTIZATION
    11.
    发明申请
    FAST DENSE PATCH SEARCH AND QUANTIZATION 有权
    快速密码搜索和量化

    公开(公告)号：US20150139557A1

    公开(公告)日：2015-05-21

    申请号：US14085488

    申请日：2013-11-20

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Jianchao Yang ,  Hailin Jin ,  Xin Lu

    IPC: G06K9/62

    CPC classification number: G06K9/4642 ,  G06K9/6273 ,  G06K9/6276

    Abstract: In techniques for fast dense patch search and quantization, partition center patches are determined for partitions of example image patches. Patch groups of an image each include similar image patches and a reference image patch that represents a respective patch group. A partition center patch of the partitions is determined as a nearest neighbor to the reference image patch of a patch group. The partition center patch can be determined based on a single-nearest neighbor (1-NN) distance determination, and the determined partition center patch is allocated as the nearest neighbor to the similar image patches in the patch group. Alternatively, a group of nearby partition center patches are determined as the nearest neighbors to the reference image patch based on a k-nearest neighbor (k-NN) distance determination, and the nearest neighbor to each of the similar image patches in the patch group is determined from the nearby partition center patches.

    Abstract translation: 在快速密集补丁搜索和量化的技术中，为示例图像补丁的分区确定分区中心补丁。 图像的补丁组各自包括相似的图像补丁和代表相应补丁组的参考图像补丁。 分区的分区中心补丁被确定为补丁组的参考图像补丁的最近邻。 可以基于单个最近邻居（1-NN）距离确定来确定分区中心补丁，并且将所确定的分区中心补丁分配为补丁组中的相似图像补丁的最近邻。 或者，基于k个最近邻（k-NN）距离确定，将一组附近的分区中心补丁确定为参考图像补丁的最近邻，并且补丁组中每个相似图像补丁的最近邻 是从附近的分区中心补丁确定的。

    Digital Image Completion Using Deep Learning
    12.
    发明申请
    Digital Image Completion Using Deep Learning 审中-公开

    公开(公告)号：US20190114748A1

    公开(公告)日：2019-04-18

    申请号：US15785359

    申请日：2017-10-16

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Xin Lu ,  Xiaohui Shen ,  Jimei Yang ,  Jiahui Yu

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

    Abstract: Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty. Responsive to this, the image completer outputs the filled digital image with hole-filling content in place of the holey digital image's holes.

    AUTOMATICALLY SEGMENTING IMAGES BASED ON NATURAL LANGUAGE PHRASES
    13.
    发明申请
    AUTOMATICALLY SEGMENTING IMAGES BASED ON NATURAL LANGUAGE PHRASES 审中-公开

    公开(公告)号：US20180268548A1

    公开(公告)日：2018-09-20

    申请号：US15458887

    申请日：2017-03-14

    Applicant: ADOBE SYSTEMS INCORPORATED

    Inventor： Zhe Lin ,  Xin Lu ,  Xiaohui Shen ,  Jimei Yang ,  Chenxi Liu

    IPC: G06T7/11 ,  G06F17/27 ,  G10L15/26

    Abstract: The invention is directed towards segmenting images based on natural language phrases. An image and an n-gram, including a sequence of tokens, are received. An encoding of image features and a sequence of token vectors are generated. A fully convolutional neural network identifies and encodes the image features. A word embedding model generates the token vectors. A recurrent neural network (RNN) iteratively updates a segmentation map based on combinations of the image feature encoding and the token vectors. The segmentation map identifies which pixels are included in an image region referenced by the n-gram. A segmented image is generated based on the segmentation map. The RNN may be a convolutional multimodal RNN. A separate RNN, such as a long short-term memory network, may iteratively update an encoding of semantic features based on the order of tokens. The first RNN may update the segmentation map based on the semantic feature encoding.

    Patch partitions and image processing
    14.
    发明授权
    Patch partitions and image processing 有权

    公开(公告)号：US09978129B2

    公开(公告)日：2018-05-22

    申请号：US15707418

    申请日：2017-09-18

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Jianchao Yang ,  Hailin Jin ,  Xin Lu

    IPC: G06K9/62 ,  G06T5/00 ,  G06K9/00

    CPC classification number: G06T5/005 ,  G06K9/00664 ,  G06K9/6218 ,  G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Patch partition and image processing techniques are described. In one or more implementations, a system includes one or more modules implemented at least partially in hardware. The one or more modules are configured to perform operations including grouping a plurality of patches taken from a plurality of training samples of images into respective ones of a plurality of partitions, calculating an image processing operator for each of the partitions, determining distances between the plurality of partitions that describe image similarity of patches of the plurality of partitions, one to another, and configuring a database to provide the determined distance and the image processing operator to process an image in response to identification of a respective partition that corresponds to a patch taken from the image.

    Patch Partitions and Image Processing
    20.
    发明申请
    Patch Partitions and Image Processing 有权
    补丁分区和图像处理

    公开(公告)号：US20150332438A1

    公开(公告)日：2015-11-19

    申请号：US14280421

    申请日：2014-05-16

    Applicant: Adobe Systems Incorporated

    Inventor： Zhe Lin ,  Jianchao Yang ,  Hailin Jin ,  Xin Lu

    IPC: G06T5/00 ,  G06T5/50

    CPC classification number: G06T5/005 ,  G06K9/00664 ,  G06K9/6218 ,  G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Patch partition and image processing techniques are described. In one or more implementations, a system includes one or more modules implemented at least partially in hardware. The one or more modules are configured to perform operations including grouping a plurality of patches taken from a plurality of training samples of images into respective ones of a plurality of partitions, calculating an image processing operator for each of the partitions, determining distances between the plurality of partitions that describe image similarity of patches of the plurality of partitions, one to another, and configuring a database to provide the determined distance and the image processing operator to process an image in response to identification of a respective partition that corresponds to a patch taken from the image.

    Abstract translation: 描述了补丁分区和图像处理技术。 在一个或多个实现中，系统包括至少部分地以硬件实现的一个或多个模块。 一个或多个模块被配置为执行操作，包括将从多个图像的训练样本获取的多个补丁分组到多个分区中的相应的分区中，为每个分区计算图像处理算子，确定多个分割之间的距离 描述多个分区的图像相似度的分区，并且配置数据库以提供所确定的距离，并且图像处理运算符响应于对应于所采取的补丁的相应分区的标识来处理图像 从图像。

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