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公开(公告)号：US20240355107A1
公开(公告)日：2024-10-24
申请号：US18684883
申请日：2021-08-23
申请人： Google LLC
发明人： Orly Liba , Michael Garth Milne , Navin Padman Sarma , Doron Kukliansky , Huizhong Chen , Yael Pritch Knaan
CPC分类号： G06V10/82 , G06T5/60 , G06V10/462 , G06T2207/20084 , G06T2207/20132 , G06V2201/07 , G06V2201/10
摘要： A method includes receiving training data comprising a plurality of images. one or more identified objects in each of the plurality of images. and a detection score associated with each of the one or more identified objects. wherein the detection score for an object is indicative of a degree to which a portion of an image corresponds to the object. The method also includes training a neural network based on the training data to predict a distractor score for at least one object of the one or more identified objects in an input image, wherein the at least one object is selected based on an associated detection score, and wherein the distractor score for the at least one object is indicative of a perceived visual distraction caused by a presence of the at least one object in the input image. The method additionally includes outputting the trained neural network.
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公开(公告)号：US20240296596A1
公开(公告)日：2024-09-05
申请号：US18569844
申请日：2023-08-23
申请人： Google LLC
发明人： Kfir Aberman , Nataniel Ruiz Gutierrez , Michael Rubinstein , Yuanzhen Li , Yael Pritch Knaan , Varun Jampani
IPC分类号： G06T11/00 , G06V10/764
CPC分类号： G06T11/00 , G06V10/764 , G06V2201/07
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text-to-image model so that the text-to-image model generates images that each depict a variable instance of an object class when the object class without the unique identifier is provided as a text input, and that generates images that each depict a same subject instance of the object class when the unique identifier is provided as the text input.
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公开(公告)号：US20230325998A1
公开(公告)日：2023-10-12
申请号：US18334700
申请日：2023-06-14
申请人： Google LLC
发明人： Kfir Aberman , Yotam Nitzan , Orly Liba , Yael Pritch Knaan , Qiurui He , Inbar Mosseri , Yossi Gandelsman , Michal Yarom
CPC分类号： G06T5/50 , G06T5/001 , G06T3/40 , G06T2207/20081 , G06T2207/20084
摘要： Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.
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公开(公告)号：US20220230323A1
公开(公告)日：2022-07-21
申请号：US17617560
申请日：2019-07-15
申请人： Google LLC
发明人： Orly Liba , Florian Kainz , Longqi Cai , Yael Pritch Knaan
IPC分类号： G06T7/11 , G06T5/00 , G06V10/764
摘要： A device automatically segments an image into different regions and automatically adjusts perceived exposure-levels or other characteristics associated with each of the different regions, to produce pictures that exceed expectations for the type of optics and camera equipment being used and in some cases, the pictures even resemble other high-quality photography created using professional equipment and photo editing software. A machine-learned model is trained to automatically segment an image into distinct regions. The model outputs one or more masks that define the distinct regions. The mask(s) are refined using a guided filter or other technique to ensure that edges of the mask(s) conform to edges of objects depicted in the image. By applying the mask(s) to the image, the device can individually adjust respective characteristics of each of the different regions to produce a higher-quality picture of a scene.
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公开(公告)号：US11599747B2
公开(公告)日：2023-03-07
申请号：US17090948
申请日：2020-11-06
申请人： Google LLC
发明人： Yael Pritch Knaan , Marc Levoy , Neal Wadhwa , Rahul Garg , Sameer Ansari , Jiawen Chen
IPC分类号： G06K9/62
摘要： Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.
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公开(公告)号：US20230015117A1
公开(公告)日：2023-01-19
申请号：US17856370
申请日：2022-07-01
申请人： Google LLC
发明人： Kfir Aberman , David Edward Jacobs , Kai Jochen Kohlhoff , Michael Rubinstein , Yossi Gandelsman , Junfeng He , Inbar Mosseri , Yael Pritch Knaan
摘要： Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.
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公开(公告)号：US20200242788A1
公开(公告)日：2020-07-30
申请号：US16652568
申请日：2017-12-05
申请人： Google LLC
发明人： David Jacobs , Rahul Garg , Yael Pritch Knaan , Neal Wadhwa , Marc Levoy
摘要： A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.
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公开(公告)号：US20240046532A1
公开(公告)日：2024-02-08
申请号：US18489539
申请日：2023-10-18
申请人： Google LLC
CPC分类号： G06T11/001 , G06N20/00 , G06T5/005 , G06T11/60
摘要： Techniques for reducing a distractor object in a first image are presented herein. A system can access a mask and the first image. A distractor object in the first image can be inside a region of interest and can have a pixel with an original attribute. Additionally, the system can process, using a machine-learned inpainting model, the first image and the mask to generate an inpainted image. The pixel of the distractor object in the inpainted image can have an inpainted attribute in chromaticity channels. Moreover, the system can determine a palette transform based on a comparison of the first image and the inpainted image. The transform attribute can be different from the inpainted attribute. Furthermore, the system can process the first image to generate a recolorized image. The pixel in the recolorized image can have a recolorized attribute based on the transform attribute of the palette transform.
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公开(公告)号：US11854120B2
公开(公告)日：2023-12-26
申请号：US17487741
申请日：2021-09-28
申请人： Google LLC
CPC分类号： G06T11/001 , G06N20/00 , G06T5/005 , G06T11/60
摘要： Techniques for reducing a distractor object in a first image are presented herein. A system can access a mask and the first image. A distractor object in the first image can be inside a region of interest and can have a pixel with an original attribute. Additionally, the system can process, using a machine-learned inpainting model, the first image and the mask to generate an inpainted image. The pixel of the distractor object in the inpainted image can have an inpainted attribute in chromaticity channels. Moreover, the system can determine a palette transform based on a comparison of the first image and the inpainted image. The transform attribute can be different from the inpainted attribute. Furthermore, the system can process the first image to generate a recolorized image. The pixel in the recolorized image can have a recolorized attribute based on the transform attribute of the palette transform.
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公开(公告)号：US11792553B2
公开(公告)日：2023-10-17
申请号：US17097184
申请日：2020-11-13
申请人： Google LLC
CPC分类号： H04Q9/00 , A61H33/06 , A61H33/063 , G08C19/00 , H04L12/40 , A61H2201/0176 , A61H2201/0207 , A61H2201/5007 , A61H2201/5012 , A61H2201/5071 , A61H2201/5082 , A61H2201/5089 , A61H2201/5092
摘要： The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.
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