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公开(公告)号：US10692221B2
公开(公告)日：2020-06-23
申请号：US16035410
申请日：2018-07-13
Applicant: Adobe Inc.
Inventor： I-Ming Pao , Zhe Lin
Abstract: A digital medium environment is described to automatically generate a trimap and segment a digital image, independent of any user intervention. An image processing system receives an image and a low-resolution mask for the image, which provides a probability map indicating a likelihood that a pixel in the image mask corresponds to a foreground object in the image. The image processing system analyzes the image to identify content in the image's foreground and background portions, and adaptively generates a trimap for the image based on differences between the identified foreground and background content. By identifying content of the image prior to generating the trimap, the techniques described herein can be applied to a wide range of images, such as images where foreground content is visually similar to background content, and vice versa. Thus, the image processing system can automatically generate trimaps for images having diverse visual characteristics.
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公开(公告)号：US20200175651A1
公开(公告)日：2020-06-04
申请号：US16204675
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Oliver Wang , Lijun Wang
Abstract: Techniques of generating depth-of-field blur effects on digital images by digital effect generation system of a computing device are described. The digital effect generation system is configured to generate depth-of-field blur effects on objects based on focal depth value that defines a depth plane in the digital image and a aperture value that defines an intensity of blur effect applied to the digital image. The digital effect generation system is also configured to improve the accuracy with which depth-of-field blur effects are generated by performing up-sampling operations and implementing a unique focal loss algorithm that minimizes the focal loss within digital images effectively.
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公开(公告)号：US10664719B2
公开(公告)日：2020-05-26
申请号：US15043174
申请日：2016-02-12
Applicant: ADOBE INC.
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang , Chen Fang
IPC: G06K9/62 , G06K9/46 , G06F16/583 , G06N20/10 , G06F16/51 , G06F16/28 , G06F16/2457 , G06N3/04 , G06N3/08
Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.
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公开(公告)号：US20200026956A1
公开(公告)日：2020-01-23
申请号：US16039311
申请日：2018-07-18
Applicant: Adobe Inc.
Inventor： Jayant Kumar , Zhe Lin , Vipulkumar C. Dalal
IPC: G06K9/62
Abstract: There is described a computing device and method in a digital medium environment for custom auto tagging of multiple objects. The computing device includes an object detection network and multiple image classification networks. An image is received at the object detection network and includes multiple visual objects. First feature maps are applied to the image at the object detection network and generate object regions associated with the visual objects. The object regions are assigned to the multiple image classification networks, and each image classification network is assigned to a particular object region. The second feature maps are applied to each object region at each image classification network, and each image classification network outputs one or more classes associated with a visual object corresponding to each object region.
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公开(公告)号：US20200019758A1
公开(公告)日：2020-01-16
申请号：US16036757
申请日：2018-07-16
Applicant: ADOBE INC.
Inventor： Haoxiang Li , Zhe Lin , Muhammad Abdullah Jamal
Abstract: Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.
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Patent Agency Ranking
公开(公告)号：US10516830B2
公开(公告)日：2019-12-24
申请号：US15730614
申请日：2017-10-11
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zijun Wei , Zhe Lin , Xiaohui Shen , Radomir Mech
Abstract: Various embodiments describe facilitating real-time crops on an image. In an example, an image processing application executed on a device receives image data corresponding to a field of view of a camera of the device. The image processing application renders a major view on a display of the device in a preview mode. The major view presents a previewed image based on the image data. The image processing application receives a composition score of a cropped image from a deep-learning system. The image processing application renders a sub-view presenting the cropped image based on the composition score in a preview mode. Based on a user interaction, the image processing application renders the cropped image in the major view with the sub-view in the preview mode.
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公开(公告)号：US20190355102A1
公开(公告)日：2019-11-21
申请号：US15980691
申请日：2018-05-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Digital image completion by learning generation and patch matching jointly 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 dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
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公开(公告)号：US10481774B2
公开(公告)日：2019-11-19
申请号：US15291462
申请日：2016-10-12
Applicant: Adobe Inc.
Inventor： Zhe Lin , Byungmoon Kim , Yuan Gao
IPC: G06K9/40 , G06F3/0484 , G06T5/00 , G06F3/041 , G06F3/0488 , G06F3/0481
Abstract: This document describes techniques and apparatuses for area-dependent image enhancement. These techniques are capable of enabling selection, through a touch-enabled mobile-device display, of an area of a photographic image through movement of a spatially-variable implement, such as brush icon moved over the image. Selected areas can be enhanced differently than other areas, such as to apply sharpening to the selected area and blurring to a non-selected area.
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公开(公告)号：US10460154B2
公开(公告)日：2019-10-29
申请号：US16049322
申请日：2018-07-30
Applicant: Adobe Inc.
Inventor： Jonathan Brandt , Zhe Lin , Xiaohui Shen , Haoxiang Li
Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.
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公开(公告)号：US10430649B2
公开(公告)日：2019-10-01
申请号：US15650669
申请日：2017-07-14
Applicant: Adobe Inc.
Inventor： I-Ming Pao , Jue Wang , Ke Ma , Zhe Lin
Abstract: Text region detection techniques and systems for digital images using image tag filtering are described. These techniques and systems support numerous advantages over conventional techniques through use of image tags to filter text region candidates. A computing device, for instance, may first generate text region candidates through use of a variety of different techniques, such as text line detection. The computing device then assigns image tags to the text region candidates. The assigned image tags are then used by the computing device to filter the text region candidates based on whether image tags assigned to respective candidates are indicative of text.
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