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公开(公告)号：US20190026609A1
公开(公告)日：2019-01-24
申请号：US15658265
申请日：2017-07-24
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Zhe Lin , Radomir Mech , Jian Ren
Abstract: Techniques and systems are described to determine personalized digital image aesthetics in a digital medium environment. In one example, a personalized offset is generated to adapt a generic model for digital image aesthetics. A generic model, once trained, is used to generate training aesthetics scores from a personal training data set that corresponds to an entity, e.g., a particular user, group of users, and so on. The image aesthetics system then generates residual scores (e.g., offsets) as a difference between the training aesthetics score and the personal aesthetics score for the personal training digital images. The image aesthetics system then employs machine learning to train a personalized model to predict the residual scores as a personalized offset using the residual scores and personal training digital images.
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公开(公告)号：US20180300912A1
公开(公告)日：2018-10-18
申请号：US15485980
申请日：2017-04-12
Applicant: Adobe Systems Incorporated
Inventor： Vojtech Krs , Radomir Mech , Nathan Aaron Carr , Mehmet Ersin Yumer
IPC: G06T11/20
Abstract: Various embodiments enable curves to be drawn around 3-D objects by intelligently determining or inferring how the curve flows in the space around the outside of the 3-D object. The various embodiments enable such curves to be drawn without having to constantly rotate the 3-D object. In at least some embodiments, curve flow is inferred by employing a vertex position discovery process, a path discovery process, and a final curve construction process.
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公开(公告)号：US20170357877A1
公开(公告)日：2017-12-14
申请号：US15177197
申请日：2016-06-08
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Yufei Wang , Radomir Mech , Xiaohui Shen , Gavin Stuart Peter Miller
CPC classification number: G06K9/6218 , G06F17/30247 , G06F17/30265 , G06F17/3028 , G06K9/00228 , G06K9/00677 , G06K9/00718 , G06K9/00751 , G06K9/4628 , G06K9/6215 , G06K9/6254 , G06K9/6255 , G06K9/628 , G06N3/0454 , G06N3/084
Abstract: In embodiments of event image curation, a computing device includes memory that stores a collection of digital images associated with a type of event, such as a digital photo album of digital photos associated with the event, or a video of image frames and the video is associated with the event. A curation application implements a convolutional neural network, which receives the digital images and a designation of the type of event. The convolutional neural network can then determine an importance rating of each digital image within the collection of the digital images based on the type of the event. The importance rating of a digital image is representative of an importance of the digital image to a person in context of the type of the event. The convolutional neural network generates an output of representative digital images from the collection based on the importance rating of each digital image.
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公开(公告)号：US09626584B2
公开(公告)日：2017-04-18
申请号：US14511001
申请日：2014-10-09
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
CPC classification number: G06T3/40 , G06K9/4671 , G06T3/0012 , G06T11/60 , G06T2210/22
Abstract: Image cropping suggestion using multiple saliency maps is described. In one or more implementations, component scores, indicative of visual characteristics established for visually-pleasing croppings, are computed for candidate image croppings using multiple different saliency maps. The visual characteristics on which a candidate image cropping is scored may be indicative of its composition quality, an extent to which it preserves content appearing in the scene, and a simplicity of its boundary. Based on the component scores, the croppings may be ranked with regard to each of the visual characteristics. The rankings may be used to cluster the candidate croppings into groups of similar croppings, such that croppings in a group are different by less than a threshold amount and croppings in different groups are different by at least the threshold amount. Based on the clustering, croppings may then be chosen, e.g., to present them to a user for selection.
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公开(公告)号：US20160179844A1
公开(公告)日：2016-06-23
申请号：US14573963
申请日：2014-12-17
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Xin Lu , Zhe Lin , Radomir Mech
CPC classification number: G06F17/30256 , G06F17/30247 , G06F17/30271 , G06K9/00221 , G06K9/00261 , G06K9/036 , G06K9/4628 , G06K9/6215 , G06K9/6227 , G06K9/6272 , G06K9/66 , G06N3/04 , G06N3/08
Abstract: Neural network image curation techniques are described. In one or more implementations, curation is controlled of images that represent a repository of images. A plurality of images of the repository are curated by one or more computing devices to select representative images of the repository. The curation includes calculating a score based on image and face aesthetics, jointly, for each of the plurality of images through processing by a neural network, ranking the plurality of images based on respective said scores, and selecting one or more of the plurality of images as one of the representative images of the repository based on the ranking and a determination that the one or more said images are not visually similar to images that have already been selected as one of the representative images of the repository.
Abstract translation: 描述神经网络图像策划技术。 在一个或多个实现中，控制图像的图像的图像库。 存储库的多个图像由一个或多个计算设备进行策划，以选择存储库的代表图像。 该策展包括基于图像和面部美学计算一个分数，通过神经网络的处理来共同地为多个图像中的每个图像，基于相应的分数对多个图像进行排序，并且选择多个图像中的一个或多个 作为基于排名的存储库的代表性图像之一，并且确定一个或多个所述图像在视觉上与已经被选择为存储库的代表图像之一的图像相似。
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Patent Agency Ranking
公开(公告)号：US20160140408A1
公开(公告)日：2016-05-19
申请号：US14548170
申请日：2014-11-19
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Xin Lu , Zhe Lin , Radomir Mech
CPC classification number: G06K9/4676 , G06K9/4628
Abstract: Neural network patch aggregation and statistical techniques are described. In one or more implementations, patches are generated from an image, e.g., randomly, and used to train a neural network. An aggregation of outputs of patches processed by the neural network may be used to label an image using an image descriptor, such as to label aesthetics of the image, classify the image, and so on. In another example, the patches may be used by the neural network to calculate statistics describing the patches, such as to describe statistics such as minimum, maximum, median, and average of activations of image characteristics of the individual patches. These statistics may also be used to support a variety of functionality, such as to label the image as described above.
Abstract translation: 描述神经网络补丁聚合和统计技术。 在一个或多个实现中，从图像生成补片，例如随机地，并用于训练神经网络。 由神经网络处理的补丁的输出的聚合可以用于使用图像描述符来标记图像，例如标记图像的美学，对图像进行分类等等。 在另一示例中，神经网络可以使用补丁来计算描述补丁的统计量，例如描述诸如单个补丁的图像特征的激活的最小值，最大值，中值和平均值的统计信息。 这些统计信息也可以用于支持各种功能，例如如上所述标记图像。
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公开(公告)号：US20160117798A1
公开(公告)日：2016-04-28
申请号：US14524489
申请日：2014-10-27
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang
IPC: G06T3/40
CPC classification number: G06T3/40
Abstract: Image zooming is described. In one or more implementations, zoomed croppings of an image are scored. The scores calculated for the zoomed croppings are indicative of a zoomed cropping's inclusion of content that is captured in the image. For example, the scores are indicative of a degree to which a zoomed cropping includes salient content of the image, a degree to which the salient content included in the zoomed cropping is centered in the image, and a degree to which the zoomed cropping preserves specified regions-to-keep and excludes specified regions-to-remove. Based on the scores, at least one zoomed cropping may be chosen to effectuate a zooming of the image. Accordingly, the image may be zoomed according to the zoomed cropping such that an amount the image is zoomed corresponds to a scale of the zoomed cropping.
Abstract translation: 描述图像缩放。 在一个或多个实现中，对图像进行缩放裁剪。 针对放大的裁剪计算的分数表示缩放的裁剪包含在图像中捕获的内容。 例如，分数表示缩放裁剪包括图像的显着内容的程度，包括在缩放裁剪中的显着内容在图像中的程度以及缩放裁剪保留指定的程度 区域 - 要保留并排除指定的要移除的区域。 基于分数，可以选择至少一个缩放的裁剪来实现图像的缩放。 因此，可以根据缩放的裁剪来缩放图像，使得图像被缩放的量对应于缩放裁剪的比例。
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公开(公告)号：US09299004B2
公开(公告)日：2016-03-29
申请号：US14062680
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
CPC classification number: G06K9/4671 , G06T7/11 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/20004 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081
Abstract: In techniques for image foreground detection, a foreground detection module is implemented to generate varying levels of saliency thresholds from a saliency map of an image that includes foreground regions. The saliency thresholds can be generated based on an adaptive thresholding technique applied to the saliency map of the image and/or based on multi-level segmentation of the saliency map. The foreground detection module applies one or more constraints that distinguish the foreground regions in the image, and detects the foreground regions of the image based on the saliency thresholds and the constraints. Additionally, different ones of the constraints can be applied to detect different ones of the foreground regions, as well as to detect multi-level foreground regions based on the saliency thresholds and the constraints.
Abstract translation: 在用于图像前景检测的技术中，实施前景检测模块以从包括前景区域的图像的显着图生成不同级别的显着阈值。 可以基于应用于图像的显着图的自适应阈值技术和/或基于显着图的多级分割来生成显着阈值。 前景检测模块应用区分图像中的前景区域的一个或多个约束，并且基于显着性阈值和约束来检测图像的前景区域。 此外，可以应用不同的约束来检测不同的前景区域，以及基于显着性阈值和约束来检测多级前景区域。
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公开(公告)号：US20150117783A1
公开(公告)日：2015-04-30
申请号：US14062559
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
IPC: G06K9/46
CPC classification number: G06K9/4671 , G06T7/11 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/20004 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081
Abstract: In techniques for iterative saliency map estimation, a salient regions module applies a saliency estimation technique to compute a saliency map of an image that includes image regions. A salient image region of the image is determined from the saliency map, and an image region that corresponds to the salient image region is removed from the image. The salient regions module then iteratively determines subsequent salient image regions of the image utilizing the saliency estimation technique to recompute the saliency map of the image with the image region removed, and removes the image regions that correspond to the subsequent salient image regions from the image. The salient image regions of the image are iteratively determined until no salient image regions are detected in the image, and a salient features map is generated that includes each of the salient image regions determined iteratively and combined to generate the final saliency map.
Abstract translation: 在迭代显着性图估计技术中，显着区域模块应用显着性估计技术来计算包括图像区域的图像的显着性图。 从显着性图确定图像的显着图像区域，并且从图像中去除对应于显着图像区域的图像区域。 显着区域模块然后使用显着性估计技术迭代地确定图像的随后的显着图像区域，以重新计算去除图像区域的图像的显着图，并且从图像中去除与后续显着图像区域相对应的图像区域。 迭代地确定图像的显着图像区域，直到在图像中没有检测到显着的图像区域，并且生成包括迭代地确定并组合的每个显着图像区域以产生最终显着图的显着特征图。
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公开(公告)号：US20150091200A1
公开(公告)日：2015-04-02
申请号：US14041045
申请日：2013-09-30
Applicant: Adobe Systems Incorporated
Inventor： Radomir Mech
IPC: B29C67/00
CPC classification number: B29C64/141 , B29C47/0011 , B29C47/92 , B29C64/106 , B29C64/386 , B33Y10/00 , B33Y50/02
Abstract: This document describes techniques and apparatuses for smooth 3D printing using multi-stage filaments. These techniques are capable of creating smoother surfaces than many current techniques. In some cases, the techniques determine a portion of a surface of a 3D object that includes, or will include, a printing artifact or is otherwise not smooth, and then applies multi-stage filaments to provide a smoothing surface over that portion.
Abstract translation: 本文档描述了使用多级细丝进行平滑3D打印的技术和装置。 这些技术能够产生比许多当前技术更平滑的表面。 在某些情况下，这些技术确定3D对象的表面的一部分，其包括或将包括打印伪影，或者否则不平滑，然后施加多级细丝以在该部分上提供平滑表面。
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