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公开(公告)号：US10284835B2
公开(公告)日：2019-05-07
申请号：US14864650
申请日：2015-09-24
Applicant: Apple Inc.
Inventor： Thomas E. Bishop , Alexander Lindskog , Claus Molgaard , Frank Doepke
IPC: H04N13/106 , G06T5/00 , H04N5/235 , H04N5/232 , G06T7/593
Abstract: Generating an image with a selected level of background blur includes capturing, by a first image capture device, a plurality of frames of a scene, wherein each of the plurality of frames has a different focus depth, obtaining a depth map of the scene, determining a target object and a background in the scene based on the depth map, determining a goal blur for the background, and selecting, for each pixel in an output image, a corresponding pixel from the focus stack.
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公开(公告)号：US20200082541A1
公开(公告)日：2020-03-12
申请号：US16566082
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Mark N. Jouppi , Alexander Lindskog , Michael W. Tao
IPC: G06T7/194 , G06T5/00 , G06T5/20 , H04N13/128
Abstract: This disclosure relates to techniques for generating robust depth estimations for captured images using semantic segmentation. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Such segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as depth and/or disparity estimation, to yield improved results in a wide range of image capture scenarios. In some embodiments, an optimization framework may be employed to optimize a camera device's initial scene depth/disparity estimates that employs both semantic segmentation and color regularization in a robust fashion.
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公开(公告)号：US09565356B2
公开(公告)日：2017-02-07
申请号：US14864565
申请日：2015-09-24
Applicant: Apple Inc.
Inventor： Alexander Lindskog , Frank Doepke , Ralf Brunner , Thomas E. Bishop
CPC classification number: H04N5/23212 , G02B7/36 , G02B27/0075 , G06T7/50 , H04N5/2254 , H04N5/23293 , H04N5/2356 , H04N5/357
Abstract: Generating a focus stack, including receiving initial focus data that identifies a plurality of target depths, positioning a lens at a first position to capture a first image at a first target depth of the plurality of target depths, determining, in response to capturing the first image and prior to capturing additional images, a sharpness metric for the first image, capturing, in response to determining that the sharpness metric for the first image is an unacceptable value, a second image at a second position based on the sharpness metric, wherein the second position is not included in the plurality of target depths, determining that a sharpness metric for the second image is an acceptable value, and generating a focus stack using the second image.
Abstract translation: 生成焦点堆叠，包括接收识别多个目标深度的初始聚焦数据，将透镜定位在第一位置以在多个目标深度的第一目标深度捕获第一图像，响应于捕获第一 图像，并且在捕获附加图像之前，针对第一图像的锐度度量，响应于确定第一图像的锐度度量是不可接受的值，捕获，基于锐度度量在第二位置处的第二图像，其中 第二位置不包括在多个目标深度中，确定第二图像的锐度度量是可接受的值，并且使用第二图像生成焦点堆叠。
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公开(公告)号：US10410327B2
公开(公告)日：2019-09-10
申请号：US15990154
申请日：2018-05-25
Applicant: Apple Inc.
Inventor： Richard D. Seely , Michael W. Tao , Alexander Lindskog , Geoffrey T. Anneheim
Abstract: This disclosure relates to techniques for synthesizing out of focus effects in digital images. Digital single-lens reflex (DSLR) cameras and other cameras having wide aperture lenses typically capture images with a shallow depth of field (SDOF). SDOF photography is often used in portrait photography, since it emphasizes the subject, while deemphasizing the background via blurring. Simulating this kind of blurring using a large depth of field (LDOF) camera may require a large amount of computational resources, i.e., in order to simulate the physical effects of using a wide aperture lens while constructing a synthetic SDOF image. However, cameras having smaller lens apertures, such as mobile phones, may not have the processing power to simulate the spreading of all background light sources in a reasonable amount of time. Thus, described herein are techniques to synthesize out-of-focus background blurring effects in a computationally-efficient manner for images captured by LDOF cameras.
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公开(公告)号：US20180350043A1
公开(公告)日：2018-12-06
申请号：US15990154
申请日：2018-05-25
Applicant: Apple Inc.
Inventor： Richard D. Seely , Michael W. Tao , Alexander Lindskog , Geoffrey T. Anneheim
CPC classification number: G06T5/002 , G06T5/50 , G06T7/90 , G06T2207/10024 , H04N5/23216 , H04N5/23229
Abstract: This disclosure relates to techniques for synthesizing out of focus effects in digital images. Digital single-lens reflex (DSLR) cameras and other cameras having wide aperture lenses typically capture images with a shallow depth of field (SDOF). SDOF photography is often used in portrait photography, since it emphasizes the subject, while deemphasizing the background via blurring. Simulating this kind of blurring using a large depth of field (LDOF) camera may require a large amount of computational resources, i.e., in order to simulate the physical effects of using a wide aperture lens while constructing a synthetic SDOF image. However, cameras having smaller lens apertures, such as mobile phones, may not have the processing power to simulate the spreading of all background light sources in a reasonable amount of time. Thus, described herein are techniques to synthesize out-of-focus background blurring effects in a computationally-efficient manner for images captured by LDOF cameras.
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Patent Agency Ranking
公开(公告)号：US20170070731A1
公开(公告)日：2017-03-09
申请号：US15256526
申请日：2016-09-03
Applicant: Apple Inc.
Inventor： Benjamin A. Darling , Thomas E. Bishop , Kevin A. Gross , Paul M. Hubel , Todd S. Sachs , Guangzhi Cao , Alexander Lindskog , Stefan Weber , Jianping Zhou
CPC classification number: H04N17/002 , G06T7/337 , G06T7/80 , G06T7/85
Abstract: Camera calibration includes capturing a first image of an object by a first camera, determining spatial parameters between the first camera and the object using the first image, obtaining a first estimate for an optical center, iteratively calculating a best set of optical characteristics and test setup parameters based on the first estimate for the optical center until the difference in a most recent calculated set of optical characteristics and previously calculated set of optical characteristics satisfies a predetermined threshold, and calibrating the first camera based on the best set of optical characteristics. Multi-camera system calibration may include calibrating, based on a detected misalignment of features in multiple images, the multi-camera system using a context of the multi-camera system and one or more prior stored contexts.
Abstract translation: 相机校准包括通过第一相机捕获对象的第一图像，使用第一图像确定第一相机和对象之间的空间参数，获得光学中心的第一估计值，迭代地计算最佳的一组光学特性和测试设置 基于光学中心的第一估计的参数，直到最近计算出的光学特性集合的差异和先前计算出的光学特性的组合满足预定阈值，并且基于最佳的光学特性集来校准第一相机。 多摄像机系统校准可以包括基于检测到的多个图像中的特征的不对准，使用多摄像机系统的上下文和一个或多个先前存储的上下文来校准多摄像机系统。
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公开(公告)号：US20200082535A1
公开(公告)日：2020-03-12
申请号：US16566155
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Alexander Lindskog , Michael W. Tao , Alexandre Naaman
Abstract: This disclosure relates to techniques for the robust usage of semantic segmentation information in image processing techniques, e.g., shallow depth of field (SDOF) renderings. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as synthetic SDOF renderings, to yield improved results in a wide range of image capture scenarios. In some embodiments, a refinement operation may be employed on a camera device's initial depth, disparity and/or blur estimates that leverages semantic segmentation information.
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公开(公告)号：US09282235B2
公开(公告)日：2016-03-08
申请号：US14292789
申请日：2014-05-30
Applicant: Apple Inc.
Inventor： Alexander Lindskog , Ralph Brunner
CPC classification number: H04N5/23212 , G03B13/36 , H04N5/23258 , H04N5/23293
Abstract: A method to correct an autofocus operation of a digital image capture device based on an empirical evaluation of image capture metadata is disclosed. The method includes capturing an image of a scene (the image including one or more autofocus windows), obtaining an initial focus score for at least one of the image's one or more autofocus windows, obtaining image capture metadata for at least one of the one or more autofocus windows, determining a focus adjustment score for the one autofocus window based on a combination of the autofocus window's image capture metadata (wherein the focus adjustment score is indicative of the autofocus window's noise), and determining a corrected focus score for the one autofocus window based on the initial focus score and the focus adjustment score.
Abstract translation: 公开了一种基于图像捕捉元数据的经验评估来校正数字图像捕获装置的自动对焦操作的方法。 该方法包括捕获场景的图像（包括一个或多个自动对焦窗口的图像），获得图像的一个或多个自动聚焦窗口中的至少一个的初始聚焦分数，获得图像捕获元数据中的至少一个或 更多的自动对焦窗口，基于自动对焦窗口的图像拍摄元数据（其中焦点调整分数指示自动对焦窗口的噪声）的组合，确定一个自动对焦窗口的焦点调整分数，并且确定用于一个自动对焦窗口的校正焦点得分 基于初始焦点得分和焦点调整分数的窗口。
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公开(公告)号：US20150350522A1
公开(公告)日：2015-12-03
申请号：US14292789
申请日：2014-05-30
Applicant: Apple Inc.
Inventor： Alexander Lindskog , Ralph Brunner
CPC classification number: H04N5/23212 , G03B13/36 , H04N5/23258 , H04N5/23293
Abstract: A method to correct an autofocus operation of a digital image capture device based on an empirical evaluation of image capture metadata is disclosed. The method includes capturing an image of a scene (the image including one or more autofocus windows), obtaining an initial focus score for at least one of the image's one or more autofocus windows, obtaining image capture metadata for at least one of the one or more autofocus windows, determining a focus adjustment score for the one autofocus window based on a combination of the autofocus window's image capture metadata (wherein the focus adjustment score is indicative of the autofocus window's noise), and determining a corrected focus score for the one autofocus window based on the initial focus score and the focus adjustment score.
Abstract translation: 公开了一种基于图像捕捉元数据的经验评估来校正数字图像捕获装置的自动对焦操作的方法。 该方法包括捕获场景的图像（包括一个或多个自动对焦窗口的图像），获得图像的一个或多个自动聚焦窗口中的至少一个的初始聚焦分数，获得图像捕获元数据中的至少一个或 更多的自动对焦窗口，基于自动对焦窗口的图像拍摄元数据（其中焦点调整分数指示自动对焦窗口的噪声）的组合，确定一个自动对焦窗口的焦点调整分数，并且确定用于一个自动对焦窗口的校正焦点得分 基于初始焦点得分和焦点调整分数的窗口。
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公开(公告)号：US11526995B2
公开(公告)日：2022-12-13
申请号：US16566082
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Mark N. Jouppi , Alexander Lindskog , Michael W. Tao
IPC: G06T7/194 , G06T5/00 , G06T5/20 , H04N13/128 , H04N13/156 , H04N13/271 , H04N13/00 , G06T7/50
Abstract: This disclosure relates to techniques for generating robust depth estimations for captured images using semantic segmentation. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Such segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as depth and/or disparity estimation, to yield improved results in a wide range of image capture scenarios. In some embodiments, an optimization framework may be employed to optimize a camera device's initial scene depth/disparity estimates that employs both semantic segmentation and color regularization in a robust fashion.
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