Patents by Inventor Radomir Mech
Radomir Mech has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10249061Abstract: 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.Type: GrantFiled: November 11, 2015Date of Patent: April 2, 2019Assignee: Adobe Inc.Inventors: Zeke Koch, Gavin Stuart Peter Miller, Jonathan W. Brandt, Nathan A. Carr, Radomir Mech, Walter Wei-Tuh Chang, Scott D. Cohen, Hailin Jin
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Patent number: 10198590Abstract: Content creation collection and navigation techniques and systems are described. In one example, a representative image is used by a content sharing service to interact with a collection of images provided as part of a search result. In another example, a user interface image navigation control is configured to support user navigation through images based on one or more metrics. In a further example, a user interface image navigation control is configured to support user navigation through images based on one or more metrics identified for an object selected from the image. In yet another example, collections of images are leveraged as part of content creation. In another example, data obtained from a content sharing service is leveraged to indicate suitability of images of a user for licensing as part of the service.Type: GrantFiled: November 11, 2015Date of Patent: February 5, 2019Assignee: Adobe Inc.Inventors: Zeke Koch, Gavin Stuart Peter Miller, Jonathan W. Brandt, Nathan A. Carr, Radomir Mech, Walter Wei-Tuh Chang, Scott D. Cohen, Hailin Jin
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Publication number: 20190026609Abstract: 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.Type: ApplicationFiled: July 24, 2017Publication date: January 24, 2019Applicant: Adobe Systems IncorporatedInventors: Xiaohui Shen, Zhe Lin, Radomir Mech, Jian Ren
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Publication number: 20180357803Abstract: Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.Type: ApplicationFiled: June 12, 2017Publication date: December 13, 2018Inventors: Jianming Zhang, Zhe Lin, Radomir Mech, Xiaohui Shen
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Publication number: 20180300912Abstract: 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.Type: ApplicationFiled: April 12, 2017Publication date: October 18, 2018Applicant: Adobe Systems IncorporatedInventors: Vojtech Krs, Radomir Mech, Nathan Aaron Carr, Mehmet Ersin Yumer
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Publication number: 20180268535Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.Type: ApplicationFiled: May 16, 2018Publication date: September 20, 2018Inventors: Xiaohui Shen, Zhe Lin, Shu Kong, Radomir Mech
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Publication number: 20180268533Abstract: Digital image defect identification and correction techniques are described. In one example, a digital medium environment is configured to identify and correct a digital image defect through identification of a defect in a digital image using machine learning. The identification includes generating a plurality of defect type scores using a plurality of defect type identification models, as part of machine learning, for a plurality of different defect types and determining the digital image includes the defect based on the generated plurality of defect type scores. A correction is generated for the identified defect and the digital image is output as included the generated correction.Type: ApplicationFiled: March 14, 2017Publication date: September 20, 2018Applicant: Adobe Systems IncorporatedInventors: Radomir Mech, Ning Yu, Xiaohui Shen, Zhe Lin
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Patent number: 10062215Abstract: Methods and systems are directed to improving the convenience of drawing applications. Some examples include generating 3D drawing objects using a drawing application and selecting one based on a 2D design (in some cases a hand-drawn sketch) provided by a user. The user provided 2D design is separated into an outline perimeter and interior design, and corresponding vectors are then generated. These vectors are then used with analogous vectors generated for drawing objects. The selection of a drawing object to correspond to the 2D design is based on finding a drawing object having a minimum difference between its vectors and the vectors of the 2D design. The selected drawing object is then used to generate a drawing object configured to receive edits from the user. This reduces the inconvenience required to manually reproduce the 2D design in the drawing application.Type: GrantFiled: February 3, 2016Date of Patent: August 28, 2018Assignee: Adobe Systems IncorporatedInventors: Radomir Mech, Mehmet Ersin Yumer, Haibin Huang
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Publication number: 20180211135Abstract: 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.Type: ApplicationFiled: March 26, 2018Publication date: July 26, 2018Applicant: Adobe Systems IncorporatedInventors: Zhe Lin, Yufei Wang, Radomir Mech, Xiaohui Shen, Gavin Stuart Peter Miller
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Patent number: 10019823Abstract: In techniques of combined composition and change-based models for image cropping, a composition application is implemented to apply one or more image composition modules of a learned composition model to evaluate multiple composition regions of an image. The learned composition model can determine one or more cropped images from the image based on the applied image composition modules, and evaluate a composition of the cropped images and a validity of change from the image to the cropped images. The image composition modules of the learned composition model include a salient regions module that iteratively determines salient image regions of the image, and include a foreground detection module that determines foreground regions of the image. The image composition modules also include one or more imaging models that reduce a number of the composition regions of the image to facilitate determining the cropped images from the image.Type: GrantFiled: October 24, 2013Date of Patent: July 10, 2018Assignee: ADOBE SYSTEMS INCORPORATEDInventors: Zhe Lin, Radomir Mech, Peng Wang
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Patent number: 10002415Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.Type: GrantFiled: April 12, 2016Date of Patent: June 19, 2018Assignee: ADOBE SYSTEMS INCORPORATEDInventors: Xiaohui Shen, Zhe Lin, Shu Kong, Radomir Mech
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Patent number: 9996768Abstract: 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.Type: GrantFiled: November 19, 2014Date of Patent: June 12, 2018Assignee: ADOBE SYSTEMS INCORPORATEDInventors: Xiaohui Shen, Xin Lu, Zhe Lin, Radomir Mech
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Patent number: 9940544Abstract: 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.Type: GrantFiled: June 8, 2016Date of Patent: April 10, 2018Assignee: ADOBE SYSTEMS INCORPORATEDInventors: Zhe Lin, Yufei Wang, Radomir Mech, Xiaohui Shen, Gavin Stuart Peter Miller
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Patent number: 9908291Abstract: 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.Type: GrantFiled: September 30, 2013Date of Patent: March 6, 2018Assignee: ADOBE SYSTEMS INCORPORATEDInventor: Radomir Mech
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Publication number: 20170357892Abstract: In embodiments of convolutional neural network joint training, a computing system memory maintains different data batches of multiple digital image items, where the digital image items of the different data batches have some common features. A convolutional neural network (CNN) receives input of the digital image items of the different data batches, and classifier layers of the CNN are trained to recognize the common features in the digital image items of the different data batches. The recognized common features are input to fully-connected layers of the CNN that distinguish between the recognized common features of the digital image items of the different data batches. A scoring difference is determined between item pairs of the digital image items in a particular one of the different data batches. A piecewise ranking loss algorithm maintains the scoring difference between the item pairs, and the scoring difference is used to train CNN regression functions.Type: ApplicationFiled: June 8, 2016Publication date: December 14, 2017Applicant: Adobe Systems IncorporatedInventors: Zhe Lin, Yufei Wang, Radomir Mech, Xiaohui Shen, Gavin Stuart Peter Miller
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Publication number: 20170357877Abstract: 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.Type: ApplicationFiled: June 8, 2016Publication date: December 14, 2017Applicant: Adobe Systems IncorporatedInventors: Zhe Lin, Yufei Wang, Radomir Mech, Xiaohui Shen, Gavin Stuart Peter Miller
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Patent number: 9817847Abstract: 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.Type: GrantFiled: March 27, 2017Date of Patent: November 14, 2017Assignee: Adobe Systems IncorporatedInventors: Xiaohui Shen, Xin Lu, Zhe Lin, Radomir Mech
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Patent number: 9805445Abstract: 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.Type: GrantFiled: October 27, 2014Date of Patent: October 31, 2017Assignee: Adobe Systems IncorporatedInventors: Zhe Lin, Radomir Mech, Xiaohui Shen, Brian L. Price, Jianming Zhang
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Publication number: 20170294010Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.Type: ApplicationFiled: April 12, 2016Publication date: October 12, 2017Inventors: Xiaohui Shen, Zhe Lin, Shu Kong, Radomir Mech
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Publication number: 20170221257Abstract: Methods and systems are directed to improving the convenience of drawing applications. Some examples include generating 3D drawing objects using a drawing application and selecting one based on a 2D design (in some cases a hand-drawn sketch) provided by a user. The user provided 2D design is separated into an outline perimeter and interior design, and corresponding vectors are then generated. These vectors are then used with analogous vectors generated for drawing objects. The selection of a drawing object to correspond to the 2D design is based on finding a drawing object having a minimum difference between its vectors and the vectors of the 2D design. The selected drawing object is then used to generate a drawing object configured to receive edits from the user. This reduces the inconvenience required to manually reproduce the 2D design in the drawing application.Type: ApplicationFiled: February 3, 2016Publication date: August 3, 2017Applicant: Adobe Systems IncorporatedInventors: Radomir Mech, Mehmet Ersin Yumer, Haibin Huang