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    Constraining Memory Use for Overlapping Virtual Memory Operations
    1.
    发明申请
    Constraining Memory Use for Overlapping Virtual Memory Operations 审中-公开

    公开(公告)号：US20190095326A1

    公开(公告)日：2019-03-28

    申请号：US15716050

    申请日：2017-09-26

    Applicant: Adobe Systems Incorporated

    Inventor： Chih-Yao Hsieh ,  Zhaowen Wang

    IPC: G06F12/0804 ,  G06F12/0846 ,  G06F9/50

    Abstract: Constraining memory use for overlapping virtual memory operations is described. The memory use is constrained to prevent memory from exceeding an operational threshold, e.g., in relation to operations for modifying content. These operations are implemented according to algorithms having a plurality of instructions. Before the instructions are performed in relation to the content, virtual memory is allocated to the content data, which is then loaded into the virtual memory and is also partitioned into data portions. In the context of the described techniques, at least one of the instructions affects multiple portions of the content data loaded in virtual memory. When this occurs, the instruction is carried out, in part, by transferring the multiple portions of content data between the virtual memory and a memory such that a number of portions of the content data in the memory is constrained to the memory that is reserved for the operation.

    Font attributes for font recognition and similarity
    2.
    发明授权
    Font attributes for font recognition and similarity 有权

    公开(公告)号：US09875429B2

    公开(公告)日：2018-01-23

    申请号：US14876667

    申请日：2015-10-06

    Applicant: Adobe Systems Incorporated

    Inventor： Zhaowen Wang ,  Luoqi Liu ,  Hailin Jin

    IPC: G06K9/62 ,  G06K9/66

    CPC classification number: G06K9/6257 ,  G06K9/4628 ,  G06K9/6272 ,  G06K9/6277 ,  G06K9/66 ,  G06K9/6828 ,  G06K2209/01 ,  G06N3/0454

    Abstract: Font recognition and similarity determination techniques and systems are described. In a first example, localization techniques are described to train a model using machine learning (e.g., a convolutional neural network) using training images. The model is then used to localize text in a subsequently received image, and may do so automatically and without user intervention, e.g., without specifying any of the edges of a bounding box. In a second example, a deep neural network is directly learned as an embedding function of a model that is usable to determine font similarity. In a third example, techniques are described that leverage attributes described in metadata associated with fonts as part of font recognition and similarity determinations.

    RECOGNIZING UNSEEN FONTS BASED ON VISUAL SIMILARITY
    7.
    发明申请
    RECOGNIZING UNSEEN FONTS BASED ON VISUAL SIMILARITY 审中-公开

    公开(公告)号：US20180089151A1

    公开(公告)日：2018-03-29

    申请号：US15280505

    申请日：2016-09-29

    Applicant: Adobe Systems Incorporated

    Inventor： Zhaowen Wang ,  Hailin Jin

    IPC: G06F17/21

    CPC classification number: G06F17/214 ,  G06K9/00852 ,  G06K9/6828

    Abstract: Font recognition and similarity determination techniques and systems are described. For example, a computing device receives an image including a font and extracts font features corresponding to the font. The computing device computes font feature distances between the font and fonts from a set of training fonts. The computing device calculates, based on the font feature distances, similarity scores for the font and the training fonts used for calculating features distances. The computing device determines, based on the similarity scores, final similarity scores for the font relative to the training fonts.

    Smoothing Images Using Machine Learning
    8.
    发明申请
    Smoothing Images Using Machine Learning 有权

    公开(公告)号：US20170161876A1

    公开(公告)日：2017-06-08

    申请号：US14957539

    申请日：2015-12-02

    Applicant: Adobe Systems Incorporated

    Inventor： Nathan A. Carr ,  Zhaowen Wang ,  Duygu Ceylan ,  I-Chao Shen

    IPC: G06T5/00 ,  G06K9/62 ,  G06T19/20 ,  G06T7/00 ,  G06T3/40

    CPC classification number: G06T5/002 ,  G06K9/44 ,  G06K9/6256 ,  G06T7/13 ,  G06T2207/20081

    Abstract: Smoothing images using machine learning is described. In one or more embodiments, a machine learning system is trained using multiple training items. Each training item includes a boundary shape representation and a positional indicator. To generate the training item, a smooth image is downscaled to produce a corresponding blocky image that includes multiple blocks. For a given block, the boundary shape representation encodes a blocky boundary in a neighborhood around the given block. The positional indicator reflects a distance between the given block and a smooth boundary of the smooth image. In one or more embodiments to smooth a blocky image, a boundary shape representation around a selected block is determined. The representation is encoded as a feature vector and applied to the machine learning system to obtain a positional indicator. The positional indicator is used to compute a location of a smooth boundary of a smooth image.

    Distributed similarity learning for high-dimensional image features
    9.
    发明授权
    Distributed similarity learning for high-dimensional image features 有权
    分布式相似度学习用于高维图像特征

    公开(公告)号：US09436893B2

    公开(公告)日：2016-09-06

    申请号：US14091972

    申请日：2013-11-27

    Applicant: Adobe Systems Incorporated

    Inventor： Jianchao Yang ,  Zhaowen Wang ,  Zhe Lin ,  Jonathan Brandt

    IPC: G06K9/62

    CPC classification number: G06K9/6269 ,  G06K9/6235

    Abstract: A system and method for distributed similarity learning for high-dimensional image features are described. A set of data features is accessed. Subspaces from a space formed by the set of data features are determined using a set of projection matrices. Each subspace has a dimension lower than a dimension of the set of data features. Similarity functions are computed for the subspaces. Each similarity function is based on the dimension of the corresponding subspace. A linear combination of the similarity functions is performed to determine a similarity function for the set of data features.

    Abstract translation: 描述了用于高维图像特征的分布式相似性学习的系统和方法。 访问一组数据功能。 使用一组投影矩阵来确定由该组数据特征形成的空间的子空间。 每个子空间的尺寸小于数据特征集合的维度。 为子空间计算相似度函数。 每个相似度函数基于相应子空间的维度。 执行相似度函数的线性组合以确定该组数据特征的相似度函数。

    Font Replacement Based on Visual Similarity
    10.
    发明申请
    Font Replacement Based on Visual Similarity 审中-公开

    公开(公告)号：US20180300592A1

    公开(公告)日：2018-10-18

    申请号：US16013791

    申请日：2018-06-20

    Applicant: Adobe Systems Incorporated

    Inventor： Hailin Jin ,  Zhaowen Wang ,  Gavin Stuart Peter Miller

    IPC: G06K9/68 ,  G06F17/21

    CPC classification number: G06K9/6828 ,  G06F17/214 ,  G06N3/0454 ,  G06N3/08 ,  G06N5/025 ,  G06N7/005

    Abstract: Font replacement based on visual similarity is described. In one or more embodiments, a font descriptor includes multiple font features derived from a visual appearance of a font by a font visual similarity model. The font visual similarity model can be trained using a machine learning system that recognizes similarity between visual appearances of two different fonts. A source computing device embeds a font descriptor in a document, which is transmitted to a destination computing device. The destination compares the embedded font descriptor to font descriptors corresponding to local fonts. Based on distances between the embedded and the local font descriptors, at least one matching font descriptor is determined. The local font corresponding to the matching font descriptor is deemed similar to the original font. The destination computing device controls presentations of the document using the similar local font. Computation of font descriptors can be outsourced to a remote location.

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