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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
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.
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公开(公告)号：US09792534B2
公开(公告)日：2017-10-17
申请号：US14995042
申请日：2016-01-13
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Quanzeng You , Hailin Jin , Chen Fang
CPC classification number: G06K9/6269 , G06F17/30253 , G06F17/3028 , G06K9/00664 , G06K9/4604 , G06K9/4628 , G06K9/6202 , G06K9/6274 , G06N3/08
Abstract: Techniques for image captioning with word vector representations are described. In implementations, instead of outputting results of caption analysis directly, the framework is adapted to output points in a semantic word vector space. These word vector representations reflect distance values in the context of the semantic word vector space. In this approach, words are mapped into a vector space and the results of caption analysis are expressed as points in the vector space that capture semantics between words. In the vector space, similar concepts with have small distance values. The word vectors are not tied to particular words or a single dictionary. A post-processing step is employed to map the points to words and convert the word vector representations to captions. Accordingly, conversion is delayed to a later stage in the process.
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公开(公告)号：US20170228659A1
公开(公告)日：2017-08-10
申请号：US15082877
申请日：2016-03-28
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jianchao Yang , Hailin Jin , Chen Fang
CPC classification number: G06N20/00 , G06F16/532 , G06F16/58 , G06N3/0454 , G06N3/08 , G06N5/04
Abstract: Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.
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公开(公告)号：US20170200065A1
公开(公告)日：2017-07-13
申请号：US14995032
申请日：2016-01-13
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Quanzeng You , Hailin Jin , Chen Fang
CPC classification number: G06K9/6269 , G06F17/30247 , G06F17/3028 , G06F17/30675 , G06K9/00664 , G06K9/4604 , G06K9/4628 , G06K9/6202 , G06K9/6274 , G06N3/0445 , G06N3/08 , G06N7/005
Abstract: Techniques for image captioning with weak supervision are described herein. In implementations, weak supervision data regarding a target image is obtained and utilized to provide detail information that supplements global image concepts derived for image captioning. Weak supervision data refers to noisy data that is not closely curated and may include errors. Given a target image, weak supervision data for visually similar images may be collected from sources of weakly annotated images, such as online social networks. Generally, images posted online include “weak” annotations in the form of tags, titles, labels, and short descriptions added by users. Weak supervision data for the target image is generated by extracting keywords for visually similar images discovered in the different sources. The keywords included in the weak supervision data are then employed to modulate weights applied for probabilistic classifications during image captioning analysis.
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公开(公告)号：US20170131877A1
公开(公告)日：2017-05-11
申请号：US14938660
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Radomir Mech , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
CPC classification number: H04L67/1074 , G06F21/10 , G06F21/6218 , G06Q20/1235 , G06T11/001 , G06T11/60 , G06T2200/24
Abstract: Content creation and sharing integration techniques and systems are described. In one or more implementations, techniques are described in which modifiable versions of content (e.g., images) are created and shared via a content sharing service such that image creation functionality used to create the images is preserved to permit continued creation using this functionality. In one or more additional implementations, image creation functionality employed by a creative professional to create content is leveraged to locate similar images from a content sharing service.
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Patent Agency Ranking
公开(公告)号：US20170098141A1
公开(公告)日：2017-04-06
申请号：US14876660
申请日：2015-10-06
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Hailin Jin
CPC classification number: G06K9/6828 , G06K9/6267 , G06K9/627 , G06K9/66 , G06K2209/01 , G06K2209/27 , G06N3/0454 , G06T7/60 , G06T2207/20081 , G06T2207/20084
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.
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7.发明授权公开(公告)号：US09501724B1
公开(公告)日：2016-11-22
申请号：US14734466
申请日：2015-06-09
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Jianchao Yang , Zhangyang Wang , Jonathan Brandt , Hailin Jin , Elya Shechtman , Aseem Omprakash Agarwala
CPC classification number: G06T3/40 , G06K9/6255 , G06K9/6828
Abstract: A convolutional neural network (CNN) is trained for font recognition and font similarity learning. In a training phase, text images with font labels are synthesized by introducing variances to minimize the gap between the training images and real-world text images. Training images are generated and input into the CNN. The output is fed into an N-way softmax function dependent on the number of fonts the CNN is being trained on, producing a distribution of classified text images over N class labels. In a testing phase, each test image is normalized in height and squeezed in aspect ratio resulting in a plurality of test patches. The CNN averages the probabilities of each test patch belonging to a set of fonts to obtain a classification. Feature representations may be extracted and utilized to define font similarity between fonts, which may be utilized in font suggestion, font browsing, or font recognition applications.
Abstract translation: 对卷积神经网络（CNN）进行字体识别和字体相似学习。 在训练阶段，通过引入差异来合成具有字体标签的文本图像，以最小化训练图像与真实世界文本图像之间的差距。 生成训练图像并将其输入到CNN中。 根据CNN正在训练的字体数量，输出被输入到N-way softmax函数中，产生N类标签上分类文本图像的分布。 在测试阶段，每个测试图像的高度被标准化，并以纵横比挤压，从而产生多个测试贴片。 CNN对属于一组字体的每个测试补丁的概率进行平均，以获得分类。 可以提取和利用特征表示来定义可以在字体建议，字体浏览或字体识别应用中使用的字体之间的字体相似性。
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公开(公告)号：US20160171755A1
公开(公告)日：2016-06-16
申请号：US15053156
申请日：2016-02-25
Applicant: Adobe Systems Incorporated
Inventor： Kevin Karsch , Kalyan Sunkavalli , Sunil Hadap , Nathan Carr , Hailin Jin
CPC classification number: G06T15/506 , G06T7/536 , G06T11/60 , G06T15/50 , G06T19/20 , G06T2207/10024 , G06T2207/10028 , G06T2219/2021
Abstract: Image editing techniques are disclosed that support a number of physically-based image editing tasks, including object insertion and relighting. The techniques can be implemented, for example in an image editing application that is executable on a computing system. In one such embodiment, the editing application is configured to compute a scene from a single image, by automatically estimating dense depth and diffuse reflectance, which respectively form the geometry and surface materials of the scene. Sources of illumination are then inferred, conditioned on the estimated scene geometry and surface materials and without any user input, to form a complete 3D physical scene model corresponding to the image. The scene model may include estimates of the geometry, illumination, and material properties represented in the scene, and various camera parameters. Using this scene model, objects can be readily inserted and composited into the input image with realistic lighting, shadowing, and perspective.
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公开(公告)号：US09311690B2
公开(公告)日：2016-04-12
申请号：US14205027
申请日：2014-03-11
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Zhuoyuan Chen , Scott D. Cohen , Jianchao Yang , Zhe Lin
CPC classification number: H04N5/21 , G06T3/4053 , G06T3/4076 , G06T5/002 , G06T5/003 , G06T5/20 , G06T5/50 , G06T7/269 , G06T2207/10016 , G06T2207/20021 , G06T2207/20032 , G06T2207/20182 , G06T2207/20201 , H04N5/144 , H04N5/145 , H04N5/147
Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.
Abstract translation: 在使用光流的视频去噪的技术中，视频内容的图像帧包括破坏视频内容的噪声。 选择参考帧，并且从参考帧内确定参考帧中的图像块的匹配补丁。 针对相对于参考帧的先前和后续图像帧计算噪声估计。 基于光流计算图像帧的噪声估计，并且可用于确定类似运动补丁对参考帧中的图像补丁进行去噪的贡献。 基于光流计算确定与参考帧中的图像块相对应的来自先前和后续图像帧的类似运动补丁。 基于来自参考帧的匹配补丁的平均值和从先前和后续图像帧确定的类似运动补丁，去除图像补丁。
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10.发明申请公开(公告)号：US20150317802A1
公开(公告)日：2015-11-05
申请号：US14801432
申请日：2015-07-16
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin
CPC classification number: G06T7/2033 , G06K9/3233 , G06K9/3241 , G06K9/3275 , G06K9/4652 , G06T5/001 , G06T7/246 , G06T7/579 , G06T7/70 , G06T2200/04 , G06T2207/10012 , G06T2207/10016 , G06T2207/20164 , G06T2207/30241 , G06T2207/30244 , H04N5/232 , H04N13/00 , H04N13/10 , H04N17/002
Abstract: A non-keyframe reconstruction technique is described for selecting and reconstructing keyframes that have not yet been included in a reconstruction of an input image sequence to provide a better reconstruction in a structure from motion (SFM) technique. The technique may, for example, be used in an adaptive reconstruction algorithm implemented by a general SFM technique. This technique may add and reconstruct non-keyframes to a set of keyframes already generated by an initialization technique and reconstructed by adaptive and optimization techniques for iteratively selecting and reconstructing additional keyframes. Camera motion and intrinsic parameters may be computed for non-keyframes by optimizing a cost function. Output of the non-keyframe reconstruction technique may include at least camera intrinsic parameters and Euclidean motion parameters for the images in the input image sequence.
Abstract translation: 描述了非关键帧重建技术，用于选择和重建尚未包括在输入图像序列的重建中的关键帧，以在运动（SFM）技术的结构中提供更好的重建。 该技术可以例如用于通过一般SFM技术实现的自适应重建算法中。 该技术可以将非关键帧添加到已经由初始化技术生成的一组关键帧中，并通过用于迭代地选择和重建附加关键帧的自适应和优化技术来重建非关键帧。 可以通过优化成本函数来计算非关键帧的相机运动和内在参数。 非关键帧重构技术的输出可以至少包括用于输入图像序列中的图像的相机本征参数和欧氏距离运动参数。
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