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公开(公告)号：US20190244327A1
公开(公告)日：2019-08-08
申请号：US16384593
申请日：2019-04-15
Applicant: Adobe Inc.
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：US10460033B2
公开(公告)日：2019-10-29
申请号：US14978421
申请日：2015-12-22
Applicant: Adobe Inc.
Inventor： Scott D. Cohen , Walter Wei-Tuh Chang , Brian L. Price , Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny
Abstract: Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.
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公开(公告)号：US20190236808A1
公开(公告)日：2019-08-01
申请号：US16380903
申请日：2019-04-10
Applicant: Adobe Inc.
Inventor： Scott D. Cohen , Brian L. Price
CPC classification number: G06T7/90 , G06T11/001 , G06T2207/10024
Abstract: Techniques and systems are described to recolor a group of images for color consistency. Techniques include extracting color palettes for images of the group of images and generating a group theme color palette based on the color palettes for the images. Image color palettes are then mapped to the group theme color palette and the images are modified in response to the mapping. In some examples, the mapping includes discouraging multiple colors of a single color palette from mapping to a single color of the group theme color palette. Additionally, or alternatively, the mapping includes discouraging a forced mapping of a dissimilar color of an image color palette from mapping to the group theme color palette.
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公开(公告)号：US10346996B2
公开(公告)日：2019-07-09
申请号：US14832328
申请日：2015-08-21
Applicant: Adobe Inc.
Inventor： Xiaohui Shen , Zhe Lin , Scott D. Cohen , Brian L. Price
Abstract: Image depth inference techniques and systems from semantic labels are described. In one or more implementations, a digital medium environment includes one or more computing devices to control a determination of depth within an image. Regions of the image are semantically labeled by the one or more computing devices. At least one of the semantically labeled regions is decomposed into a plurality of segments formed as planes generally perpendicular to a ground plane of the image. Depth of one or more of the plurality of segments is then inferred based on relationships of respective segments with respective locations of the ground plane of the image. A depth map is formed that describes depth for the at least one semantically labeled region based at least in part on the inferred depths for the one or more of the plurality of segments.
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公开(公告)号：US10346951B2
公开(公告)日：2019-07-09
申请号：US15448138
申请日：2017-03-02
Applicant: Adobe Inc.
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
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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Patent Agency Ranking
公开(公告)号：US10241661B2
公开(公告)日：2019-03-26
申请号：US16049793
申请日：2018-07-30
Applicant: Adobe Inc.
Inventor： Brian L. Price , Ian D. Sachs
IPC: G06F3/0484 , G06T7/11 , G06T7/13
Abstract: Probabilistic determination of selected image portions is described. In one or more implementations, a selection input is received for selecting a portion of an image. For pixels of the image that correspond to the selection input, probabilities are determined that the pixels are intended to be included as part of a selected portion of the image. In particular, the probability that a given pixel is intended to be included as part of the selected portion of the image is determined as a function of position relative to center pixels of the selection input as well as a difference in one or more visual characteristics with the center pixels. The determined probabilities can then be used to segment the selected portion of the image from a remainder of the image. Based on the segmentation of the selected portion from the remainder of the image, selected portion data can be generated that defines the selected portion of the image.
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公开(公告)号：US11514244B2
公开(公告)日：2022-11-29
申请号：US14978350
申请日：2015-12-22
Applicant: Adobe Inc.
Inventor： Scott D. Cohen , Walter Wei-Tuh Chang , Brian L. Price , Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny
Abstract: Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.
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公开(公告)号：US11222399B2
公开(公告)日：2022-01-11
申请号：US16384593
申请日：2019-04-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
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：US20200007855A1
公开(公告)日：2020-01-02
申请号：US16552883
申请日：2019-08-27
Applicant: Adobe Inc.
Inventor： Scott D. Cohen , Brian L. Price , Gowri Sornanath
IPC: H04N13/271 , G06T7/593 , H04N13/122
Abstract: Stereo correspondence and depth sensor techniques are described. In one or more implementations, a depth map generated by a depth sensor is leveraged as part of processing of stereo images to assist in identifying which parts of stereo images correspond to each other. The depth map, for instance, may be utilized to assist in identifying depth discontinuities in the stereo images. Additionally, techniques may be employed to align the depth discontinuities identified from the depth map to image edges identified from the stereo images. Techniques may also be employed to suppress image edges that do not correspond to the depth discontinuities of the depth map in comparison with image edges that do correspond to the depth discontinuities as part of the identification.
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公开(公告)号：US10455219B2
公开(公告)日：2019-10-22
申请号：US13690724
申请日：2012-11-30
Applicant: Adobe Inc.
Inventor： Scott D. Cohen , Brian L. Price , Gowri Somanath
IPC: H04N13/122 , H04N13/239 , H04N13/271 , G06T7/593 , G06F3/0346 , H04N13/00
Abstract: Stereo correspondence and depth sensor techniques are described. In one or more implementations, a depth map generated by a depth sensor is leveraged as part of processing of stereo images to assist in identifying which parts of stereo images correspond to each other. The depth map, for instance, may be utilized to assist in identifying depth discontinuities in the stereo images. Additionally, techniques may be employed to align the depth discontinuities identified from the depth map to image edges identified from the stereo images. Techniques may also be employed to suppress image edges that do not correspond to the depth discontinuities of the depth map in comparison with image edges that do correspond to the depth discontinuities as part of the identification.
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