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公开(公告)号：US20140241612A1
公开(公告)日：2014-08-28
申请号：US13775179
申请日：2013-02-23
发明人： Christoph Rhemann , Carsten Curt Eckard Rother , Christopher Zach , Shahram Izadi , Adam Garnet Kirk , Oliver Whyte , Michael Bleyer
IPC分类号： G06T7/00
CPC分类号： G06T7/593 , G06T7/521 , G06T2207/10021 , G06T2207/10024 , G06T2207/10048 , H04N2013/0081
摘要： Real-time stereo matching is described, for example, to find depths of objects in an environment from an image capture device capturing a stream of stereo images of the objects. For example, the depths may be used to control augmented reality, robotics, natural user interface technology, gaming and other applications. Streams of stereo images, or single stereo images, obtained with or without patterns of illumination projected onto the environment are processed using a parallel-processing unit to obtain depth maps. In various embodiments a parallel-processing unit propagates values related to depth in rows or columns of a disparity map in parallel. In examples, the values may be propagated according to a measure of similarity between two images of a stereo pair; propagation may be temporal between disparity maps of frames of a stream of stereo images and may be spatial within a left or right disparity map.
摘要翻译： 描述了实时立体匹配，例如，从捕获物体的立体图像流的图像捕获设备中查找环境中的物体的深度。 例如，深度可以用于控制增强现实，机器人，自然界面技术，游戏和其他应用。 使用或不使用投影到环境上的照明图案获得的立体图像或单个立体图像的流被使用并行处理单元来处理以获得深度图。 在各种实施例中，并行处理单元并行地传播与视差图的行或列相关的深度值。 在示例中，可以根据立体对的两个图像之间的相似度的度量来传播值; 传播可以是立体图像流的帧的视差图之间的时间，并且可以在左视差图或右视差图内是空间的。
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公开(公告)号：US20130208983A1
公开(公告)日：2013-08-15
申请号：US13847436
申请日：2013-03-19
IPC分类号： G06T7/00
摘要： A method of up-sampling binary images for segmentation is described. In an embodiment, digital images are down-sampled before segmentation. The resulting initial binary segmentation, which has a lower resolution than the original image, is then up-sampled and smoothed to generate an interim non-binary solution which has a higher resolution than the initial binary segmentation. The final binary segmentation for the image is then computed from the interim non-binary solution based on a threshold. This method does not use the original image data in inferring the final binary segmentation solution from the initial binary segmentation. In an embodiment, the method may be applied to all images and in another embodiment, the method may be used for images which comprise a large number of pixels in total or in single dimension and smaller images may not be down-sampled before segmentation.
摘要翻译： 描述了用于分割的二进制图像的上采样方法。 在一个实施例中，在分割之前对数字图像进行下采样。 然后，所得到的具有比原始图像更低分辨率的初始二进制分割被上采样和平滑以产生具有比初始二进制分割更高分辨率的临时非二进制解。 然后基于阈值从临时非二进制解决方案计算图像的最终二进制分割。 该方法不使用原始图像数据从最初的二进制分割推断最终的二进制分割解决方案。 在一个实施例中，该方法可以应用于所有图像，并且在另一个实施例中，该方法可以用于总共或单个维度中包含大量像素的图像，并且在分割之前可能不会对较小的图像进行下采样。
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公开(公告)号：US09857470B2
公开(公告)日：2018-01-02
申请号：US13729324
申请日：2012-12-28
CPC分类号： G06T15/00 , G01S17/89 , G06T7/246 , G06T7/586 , G06T17/00 , G06T19/006 , G06T2207/10016 , G06T2207/10024 , G06T2207/10028 , G06T2207/30244 , H04N13/20
摘要： Detecting material properties such reflectivity, true color and other properties of surfaces in a real world environment is described in various examples using a single hand-held device. For example, the detected material properties are calculated using a photometric stereo system which exploits known relationships between lighting conditions, surface normals, true color and image intensity. In examples, a user moves around in an environment capturing color images of surfaces in the scene from different orientations under known lighting conditions. In various examples, surfaces normals of patches of surfaces are calculated using the captured data to enable fine detail such as human hair, netting, textured surfaces to be modeled. In examples, the modeled data is used to render images depicting the scene with realism or to superimpose virtual graphics on the real world in a realistic manner.
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公开(公告)号：US09396523B2
公开(公告)日：2016-07-19
申请号：US13949940
申请日：2013-07-24
CPC分类号： G06T5/001 , G06K9/6282 , G06T2207/20081
摘要： Image restoration cascades are described, for example, where digital photographs containing noise are restored using a cascade formed from a plurality of layers of trained machine learning predictors connected in series. For example, noise may be from sensor noise, motion blur, dust, optical low pass filtering, chromatic aberration, compression and quantization artifacts, down sampling or other sources. For example, given a noisy image, each trained machine learning predictor produces an output image which is a restored version of the noisy input image; each trained machine learning predictor in a given internal layer of the cascade also takes input from the previous layer in the cascade. In various examples, a loss function expressing dissimilarity between input and output images of each trained machine learning predictor is directly minimized during training. In various examples, data partitioning is used to partition a training data set to facilitate generalization.
摘要翻译： 描述了图像恢复级联，例如，其中使用由多个连续串联的训练机器学习预测器层形成的级联来恢复含有噪声的数字照片。 例如，噪声可能来自传感器噪声，运动模糊，灰尘，光学低通滤波，色差，压缩和量化伪像，下采样或其他源。 例如，给定嘈杂的图像，每个经过训练的机器学习预测器产生作为噪声输入图像的恢复版本的输出图像; 在级联的给定内部层中的每个经过训练的机器学习预测器也在级联中从前一层输入。 在各种示例中，在训练期间直接最小化表示每个训练机器学习预测器的输入和输出图像之间的不相似性的损失函数。 在各种示例中，使用数据划分来分割训练数据集以便于泛化。
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公开(公告)号：US09430817B2
公开(公告)日：2016-08-30
申请号：US14077247
申请日：2013-11-12
发明人： Kevin Schelten , Reinhard Sebastian Bernhard Nowozin , Jeremy Jancsary , Carsten Curt Eckard Rother
CPC分类号： G06T5/003 , G06K9/40 , G06K9/62 , G06K9/6262 , G06T2207/20081 , G06T2207/20201
摘要： Blind image deblurring with a cascade architecture is described, for example, where photographs taken on a camera phone are deblurred in a process which revises blur estimates and estimates a blur function as a combined process. In various examples the estimates of the blur function are computed using first trained machine learning predictors arranged in a cascade architecture. In various examples a revised blur estimate is calculated at each level of the cascade using a latest deblurred version of a blurred image. In some examples the revised blur estimates are calculated using second trained machine learning predictors interleaved with the first trained machine learning predictors.
摘要翻译： 描述了具有级联架构的盲目图像去模糊，例如，在修改模糊估计并且将模糊功能估计为组合处理的过程中，在照相机电话上拍摄的照片被去毛刺的情况下被描述。 在各种示例中，使用以级联架构布置的第一训练机器学习预测器来计算模糊函数的估计。 在各种示例中，使用模糊图像的最新去模糊版本在级联的每个级别处计算修正的模糊估计。 在一些示例中，使用与第一训练机器学习预测器交错的第二训练机器学习预测器来计算修正的模糊估计。
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6.发明申请公开(公告)号：US20150131898A1
公开(公告)日：2015-05-14
申请号：US14077247
申请日：2013-11-12
发明人： Kevin Schelten , Reinhard Sebastian Bernhard Nowozin , Jeremy Jancsary , Carsten Curt Eckard Rother
CPC分类号： G06T5/003 , G06K9/40 , G06K9/62 , G06K9/6262 , G06T2207/20081 , G06T2207/20201
摘要： Blind image deblurring with a cascade architecture is described, for example, where photographs taken on a camera phone are deblurred in a process which revises blur estimates and estimates a blur function as a combined process. In various examples the estimates of the blur function are computed using first trained machine learning predictors arranged in a cascade architecture. In various examples a revised blur estimate is calculated at each level of the cascade using a latest deblurred version of a blurred image. In some examples the revised blur estimates are calculated using second trained machine learning predictors interleaved with the first trained machine learning predictors.
摘要翻译： 描述了具有级联架构的盲目图像去模糊，例如，在修改模糊估计并且将模糊功能估计为组合处理的过程中，在照相机电话上拍摄的照片被去毛刺的情况下被描述。 在各种示例中，使用以级联架构布置的第一训练机器学习预测器来计算模糊函数的估计。 在各种示例中，使用模糊图像的最新去模糊版本在级联的每个级别处计算修正的模糊估计。 在一些示例中，使用与第一训练机器学习预测器交错的第二训练机器学习预测器来计算修正的模糊估计。
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公开(公告)号：US20150030237A1
公开(公告)日：2015-01-29
申请号：US13949940
申请日：2013-07-24
CPC分类号： G06T5/001 , G06K9/6282 , G06T2207/20081
摘要： Image restoration cascades are described, for example, where digital photographs containing noise are restored using a cascade formed from a plurality of layers of trained machine learning predictors connected in series. For example, noise may be from sensor noise, motion blur, dust, optical low pass filtering, chromatic aberration, compression and quantization artifacts, down sampling or other sources. For example, given a noisy image, each trained machine learning predictor produces an output image which is a restored version of the noisy input image; each trained machine learning predictor in a given internal layer of the cascade also takes input from the previous layer in the cascade. In various examples, a loss function expressing dissimilarity between input and output images of each trained machine learning predictor is directly minimized during training. In various examples, data partitioning is used to partition a training data set to facilitate generalization.
摘要翻译： 描述了图像恢复级联，例如，其中使用由多个连续串联的训练机器学习预测器层形成的级联来恢复含有噪声的数字照片。 例如，噪声可能来自传感器噪声，运动模糊，灰尘，光学低通滤波，色差，压缩和量化伪像，下采样或其他源。 例如，给定嘈杂的图像，每个经过训练的机器学习预测器产生作为噪声输入图像的恢复版本的输出图像; 在级联的给定内部层中的每个经过训练的机器学习预测器也在级联中从前一层输入。 在各种示例中，在训练期间直接最小化表示每个训练机器学习预测器的输入和输出图像之间的不相似性的损失函数。 在各种示例中，使用数据划分来分割训练数据集以便于泛化。
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公开(公告)号：US20150016717A1
公开(公告)日：2015-01-15
申请号：US14499743
申请日：2014-09-29
发明人： Kaiming He , Jian Sun , Carsten Curt Eckard Rother , Xiao-ou Tang
IPC分类号： G06T7/40
CPC分类号： G06K9/34 , G06T7/11 , G06T7/194 , G06T7/90 , G06T2207/10024
摘要： A computing device is described herein that is configured to select a pixel pair including a foreground pixel of an image and a background pixel of the image from a global set of pixels based at least on spatial distances from an unknown pixel and color distances from the unknown pixel. The computing device is further configured to determine an opacity measure for the unknown pixel based at least on the selected pixel pair.
摘要翻译： 本文描述了一种计算设备，其被配置为至少基于与未知像素的空间距离和来自未知像素的颜色距离从全局像素集合中选择包括图像的前景像素和图像的背景像素的像素对 像素。 计算设备还被配置为至少基于所选择的像素对来确定未知像素的不透明度测量。
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公开(公告)号：US20140307950A1
公开(公告)日：2014-10-16
申请号：US13862415
申请日：2013-04-13
发明人： Jeremy Jancsary , Uwe Johann Schmidt , Reinhard Sebastian Bernhard Nowozin , Carsten Curt Eckard Rother
CPC分类号： G06T5/003 , G06K9/6219 , G06K9/6296 , G06T2207/20076 , G06T2207/20081
摘要： Image deblurring is described, for example, to remove blur from digital photographs captured at a handheld camera phone and which are blurred due to camera shake. In various embodiments an estimate of blur in an image is available from a blur estimator and a trained machine learning system is available to compute parameter values of a blur function from the blurred image. In various examples the blur function is obtained from a probability distribution relating a sharp image, a blurred image and a fixed blur estimate. For example, the machine learning system is a regression tree field trained using pairs of empirical sharp images and blurred images calculated from the empirical images using artificially generated blur kernels.
摘要翻译： 描述了图像去模糊，例如，从在手持相机手机拍摄的数字照片中消除模糊，并且由于相机抖动而被模糊。 在各种实施例中，图像中的模糊估计可以从模糊估计器获得，并且经过训练的机器学习系统可用于从模糊图像计算模糊函数的参数值。 在各种示例中，从与清晰图像，模糊图像和固定模糊估计相关联的概率分布获得模糊功能。 例如，机器学习系统是使用经验锐利图像对和使用人为生成的模糊内核从经验图像计算的模糊图像训练的回归树字段。
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公开(公告)号：US20140184749A1
公开(公告)日：2014-07-03
申请号：US13729324
申请日：2012-12-28
CPC分类号： G06T15/00 , G01S17/89 , G06T7/246 , G06T7/586 , G06T17/00 , G06T19/006 , G06T2207/10016 , G06T2207/10024 , G06T2207/10028 , G06T2207/30244 , H04N13/20
摘要： Detecting material properties such reflectivity, true color and other properties of surfaces in a real world environment is described in various examples using a single hand-held device. For example, the detected material properties are calculated using a photometric stereo system which exploits known relationships between lighting conditions, surface normals, true color and image intensity. In examples, a user moves around in an environment capturing color images of surfaces in the scene from different orientations under known lighting conditions. In various examples, surfaces normals of patches of surfaces are calculated using the captured data to enable fine detail such as human hair, netting, textured surfaces to be modeled. In examples, the modeled data is used to render images depicting the scene with realism or to superimpose virtual graphics on the real world in a realistic manner.
摘要翻译： 使用单个手持装置在各种示例中描述了在真实世界环境中检测材料性质的这种反射率，真实颜色和表面的其它性质。 例如，使用光度立体声系统计算检测到的材料性质，该系统利用照明条件，表面法线，真实颜色和图像强度之间的已知关系。 在示例中，用户在从已知照明条件下的不同取向捕获场景中的表面的彩色图像的环境中移动。 在各种示例中，使用捕获的数据计算表面贴片的法线，以使得可以对诸如人的头发，网状织物，纹理表面的精细细节进行建模。 在示例中，建模的数据用于以现实的方式呈现描绘场景的图像，或以现实的方式将虚拟图形叠加在现实世界中。
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