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1. 发明授权

US08351736B2 Automatic dust removal in digital images 有权
标题翻译：数字图像中自动除尘
公开(公告)号：US08351736B2
公开(公告)日：2013-01-08
申请号：US12476514
申请日：2009-06-02
申请人： Denis Demandolx , Eric Paul Bennett , Antonio Criminisi , Vladimir Farbman , Steven James White
发明人： Denis Demandolx , Eric Paul Bennett , Antonio Criminisi , Vladimir Farbman , Steven James White
IPC分类号： G06K9/40 , G06T5/00
CPC分类号： H04N5/3572 , H04N5/367 , H04N9/045
摘要： Methods and a processing device are provided for restoring pixels damaged by artifacts caused by dust, or other particles, entering a digital image capturing device. A user interface may be provided for a user to indicate an approximate location of an artifact appearing in a digital image. Dust attenuation may be estimated and an inverse transformation, based on the estimated dust attenuation, may be applied to damaged pixels in order to recover an estimate of the underlying digital image. One or many candidate source patch may be selected based on having smallest pixel distances, with respect to a target patch area. The damaged pixels included in the target patch area may be considered when calculating the pixel distance with respect to candidate source patches. RGB values of corresponding pixels of source patches may be used to restore the damaged pixels included in the target patch area.
摘要翻译：提供了方法和处理装置，用于恢复由于灰尘或其他颗粒引起的伪影所损坏的像素进入数字图像捕获装置。可以为用户提供用户界面来指示出现在数字图像中的人造物的大致位置。可以估计灰尘衰减，并且可以将基于估计的灰尘衰减的逆变换应用于损坏的像素，以便恢复底层数字图像的估计。可以基于相对于目标贴片区域具有最小像素距离来选择一个或多个候选源贴片。当计算相对于候选源贴片的像素距离时，可以考虑包括在目标贴片区域中的损伤像素。可以使用源贴片的相应像素的RGB值来恢复包括在目标贴片区域中的损伤像素。

2. 发明申请

US20100303380A1 Automatic Dust Removal In Digital Images 有权
标题翻译：数字图像中自动除尘
公开(公告)号：US20100303380A1
公开(公告)日：2010-12-02
申请号：US12476514
申请日：2009-06-02
申请人： Denis Demandolx , Eric Paul Bennett , Antonio Criminisi , Valadimir Farbman , Steven James White
发明人： Denis Demandolx , Eric Paul Bennett , Antonio Criminisi , Valadimir Farbman , Steven James White
IPC分类号： G06K9/40
CPC分类号： H04N5/3572 , H04N5/367 , H04N9/045
摘要： Methods and a processing device are provided for restoring pixels damaged by artifacts caused by dust, or other particles, entering a digital image capturing device. A user interface may be provided for a user to indicate an approximate location of an artifact appearing in a digital image. Dust attenuation may be estimated and an inverse transformation, based on the estimated dust attenuation, may be applied to damaged pixels in order to recover an estimate of the underlying digital image. One or many candidate source patch may be selected based on having smallest pixel distances, with respect to a target patch area. The damaged pixels included in the target patch area may be considered when calculating the pixel distance with respect to candidate source patches. RGB values of corresponding pixels of source patches may be used to restore the damaged pixels included in the target patch area.
摘要翻译：提供了方法和处理装置，用于恢复由于灰尘或其他颗粒引起的伪影所损坏的像素进入数字图像捕获装置。可以为用户提供用户界面来指示出现在数字图像中的人造物的大致位置。可以估计灰尘衰减，并且可以将基于估计的灰尘衰减的逆变换应用于损坏的像素，以便恢复底层数字图像的估计。可以基于相对于目标贴片区域具有最小像素距离来选择一个或多个候选源贴片。当计算相对于候选源贴片的像素距离时，可以考虑包括在目标贴片区域中的损伤像素。可以使用源贴片的相应像素的RGB值来恢复包括在目标贴片区域中的损伤像素。

3. 发明授权

US08867802B2 Automatic organ localization 有权
标题翻译：自动器官定位
公开(公告)号：US08867802B2
公开(公告)日：2014-10-21
申请号：US13090108
申请日：2011-04-19
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Duncan Paul Robertson , Sayan D. Pathak , Steven James White , Khan Mohammed Siddiqui
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Duncan Paul Robertson , Sayan D. Pathak , Steven James White , Khan Mohammed Siddiqui
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06T7/0048 , G06K2209/051 , G06T7/77 , G06T2207/10072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30004
摘要： Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.
摘要翻译：描述自动器官定位。在一个示例中，使用一个或多个经过训练的回归树将医学图像中的器官定位。将医学图像的每个图像元素应用于经过训练的回归树，以计算与从每个图像元素到器官的距离相关的概率分布。选择和聚合概率分布的至少一个子集以计算器官的定位估计。在另一示例中，使用具有预定义的器官位置的训练图像来训练回归树。在树的每个节点，生成测试参数，确定每个训练图像元素传递到哪个后续节点。这是重复的，直到每个图像元素到达树的叶节点。基于从叶节点的图像元素到器官的距离，在每个叶节点处生成并存储概率分布。

4. 发明授权

US09626766B2 Depth sensing using an RGB camera 有权
公开(公告)号：US09626766B2
公开(公告)日：2017-04-18
申请号：US14193079
申请日：2014-02-28
申请人： Antonio Criminisi , Duncan Paul Robertson , Peter Kontschieder , Pushmeet Kohli , Henrik Turbell , Adriana Dumitras , Indeera Munasinghe , Jamie Daniel Joseph Shotton
发明人： Antonio Criminisi , Duncan Paul Robertson , Peter Kontschieder , Pushmeet Kohli , Henrik Turbell , Adriana Dumitras , Indeera Munasinghe , Jamie Daniel Joseph Shotton
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06T7/50 , G06T2207/10016 , G06T2207/10024 , G06T2207/20072 , G06T2207/20081
摘要： A method of sensing depth using an RGB camera. In an example method, a color image of a scene is received from an RGB camera. The color image is applied to a trained machine learning component which uses features of the image elements to assign all or some of the image elements a depth value which represents the distance between the surface depicted by the image element and the RGB camera. In various examples, the machine learning component comprises one or more entangled geodesic random decision forests.

5. 发明申请

US20130346346A1 SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING 有权
标题翻译：半自动监控机器学习的随机决策林
公开(公告)号：US20130346346A1
公开(公告)日：2013-12-26
申请号：US13528876
申请日：2012-06-21
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06N5/02 , G06N7/005
摘要： Semi-supervised random decision forests for machine learning are described, for example, for interactive image segmentation, medical image analysis, and many other applications. In examples, a random decision forest comprising a plurality of hierarchical data structures is trained using both unlabeled and labeled observations. In examples, a training objective is used which seeks to cluster the observations based on the labels and similarity of the observations. In an example, a transducer assigns labels to the unlabeled observations on the basis of the clusters and certainty information. In an example, an inducer forms a generic clustering function by counting examples of class labels at leaves of the trees in the forest. In an example, an active learning module identifies regions in a feature space from which the observations are drawn using the clusters and certainty information; new observations from the identified regions are used to train the random decision forest.
摘要翻译：描述了用于机器学习的半监督随机决策树，例如用于交互式图像分割，医学图像分析和许多其他应用。在示例中，使用未标记和标记的观察来训练包括多个分级数据结构的随机决策林。在实例中，使用了一个训练目标，其目的是根据观察结果的标签和相似性对观测进行聚类。在一个示例中，传感器基于集群和确定性信息将标签分配给未标记的观察。在一个例子中，诱导者通过计算森林中树的树叶上的类标签的示例来形成通用聚类函数。在一个示例中，主动学习模块识别特征空间中的区域，使用聚类和确定性信息从中绘制观察值; 来自确定地区的新观察用于训练随机决策林。

6. 发明申请

US20120087575A1 RECOGNIZING HAND POSES AND/OR OBJECT CLASSES 审中-公开
标题翻译：识别手势和/或对象类
公开(公告)号：US20120087575A1
公开(公告)日：2012-04-12
申请号：US13326166
申请日：2011-12-14
申请人： John Winn , Antonio Criminisi , Ankur Agarwal , Thomas Deselaers
发明人： John Winn , Antonio Criminisi , Ankur Agarwal , Thomas Deselaers
IPC分类号： G06K9/62
CPC分类号： G06K9/00355 , G06F3/017 , G06F3/0425 , G06K9/6282
摘要： There is a need to provide simple, accurate, fast and computationally inexpensive methods of object and hand pose recognition for many applications. For example, to enable a user to make use of his or her hands to drive an application either displayed on a tablet screen or projected onto a table top. There is also a need to be able to discriminate accurately between events when a user's hand or digit touches such a display from events when a user's hand or digit hovers just above that display. A random decision forest is trained to enable recognition of hand poses and objects and optionally also whether those hand poses are touching or not touching a display surface. The random decision forest uses image features such as appearance, shape and optionally stereo image features. In some cases, the training process is cost aware. The resulting recognition system is operable in real-time.
摘要翻译：需要为许多应用提供简单，准确，快速和计算上便宜的对象和手姿态识别方法。例如，为了使用户能够利用他或她的手来驱动显示在平板电脑屏幕上或投影到桌面上的应用程序。当用户的手或数字在该显示器的正上方移动时，当用户的手或数字触发这样的显示时，还需要能够精确地区分事件之间的事件。对随机决策林进行训练，以便能够识别手姿势和物体，并且可选地还可以确定那些手姿势是触摸还是不接触显示表面。随机决策林使用图像特征，如外观，形状和可选的立体图像特征。在某些情况下，培训过程是意识到成本。所得到的识别系统可以实时操作。

7. 发明申请

US20110274352A1 Image Segmentation Using Star-Convexity Constraints 有权
标题翻译：使用星形凸度约束的图像分割
公开(公告)号：US20110274352A1
公开(公告)日：2011-11-10
申请号：US12776082
申请日：2010-05-07
申请人： Andrew Blake , Varun Gulshan , Carsten Rother , Antonio Criminisi
发明人： Andrew Blake , Varun Gulshan , Carsten Rother , Antonio Criminisi
IPC分类号： G06K9/34
CPC分类号： G06T7/11 , G06T7/194 , G06T2207/20101 , G06T2207/20168
摘要： Image segmentation using star-convexity constraints is described. In an example, user input specifies positions of one or more star centers in a foreground to be segmented from a background of an image. In embodiments, an energy function is used to express the problem of segmenting the image and that energy function incorporates a star-convexity constraint which limits the number of possible solutions. For example, the star-convexity constraint may be that, for any point p inside the foreground, all points on a shortest path (which may be geodesic or Euclidean) between the nearest star center and p also lie inside the foreground. In some examples continuous star centers such as lines are used. In embodiments a user may iteratively edit the star centers by adding brush strokes to the image in order to progressively change the star-convexity constraints and obtain an accurate segmentation.
摘要翻译：描述了使用星形凸度约束的图像分割。在一个示例中，用户输入指定要从图像的背景分割的前景中的一个或多个星形中心的位置。在实施例中，能量函数用于表示分割图像的问题，并且能量函数包含限制可能解决方案数量的星形 - 凸度约束。例如，星凸约束可以是，对于前景中的任何点p，最近的星中心和p之间的最短路径上的所有点（可以是测地线或欧几里德）也位于前景内。在一些示例中，使用诸如线的连续星形中心。在实施例中，用户可以通过向图像中添加画笔笔触来迭代地编辑星形中心，以逐渐改变星形凸度约束并获得准确的分割。

8. 发明授权

US07991228B2 Stereo image segmentation 有权
标题翻译：立体图像分割
公开(公告)号：US07991228B2
公开(公告)日：2011-08-02
申请号：US12780857
申请日：2010-05-14
申请人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
发明人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/342 , G06K9/38 , G06K9/4652 , G06T7/11 , G06T7/162 , G06T7/194 , G06T2207/10021 , G06T2207/10024 , G06T2207/20072
摘要： Real-time segmentation of foreground from background layers in binocular video sequences may be provided by a segmentation process which may be based on one or more factors including likelihoods for stereo-matching, color, and optionally contrast, which may be fused to infer foreground and/or background layers accurately and efficiently. In one example, the stereo image may be segmented into foreground, background, and/or occluded regions using stereo disparities. The stereo-match likelihood may be fused with a contrast sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as dynamic programming or graph cut. In a second example, the stereo-match likelihood may be marginalized over foreground and background hypotheses, and fused with a contrast-sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as a binary graph cut.
摘要翻译：可以通过分割过程来提供来自双目视频序列中的背景层的前景的实时分割，分割过程可以基于一个或多个因素，包括立体匹配，颜色和可选对比的可能性，其可以融合到推断前景和 /或背景层准确高效。在一个示例中，立体图像可以使用立体声差异被分割成前景，背景和/或遮挡区域。立体匹配似然率可以与从训练数据初始化或学习的对比度敏感颜色模型融合。然后可以通过诸如动态规划或图形切割的优化算法来解决分割。在第二个例子中，立体匹配似然度在前景和背景假设上可能被边缘化，并且与从训练数据初始化或学习的对比度敏感颜色模型融合。然后可以通过诸如二进制图切割的优化算法来解决分割。

9. 发明申请

US20110085705A1 DETECTION OF BODY AND PROPS 有权
标题翻译：身体和检查的检测
公开(公告)号：US20110085705A1
公开(公告)日：2011-04-14
申请号：US12972837
申请日：2010-12-20
申请人： Shahram Izadi , Jamie Shotton , John Winn , Antonio Criminisi , Otmar Hilliges , Mat Cook , David Molyneaux
发明人： Shahram Izadi , Jamie Shotton , John Winn , Antonio Criminisi , Otmar Hilliges , Mat Cook , David Molyneaux
IPC分类号： G06K9/00
CPC分类号： G06K9/00369
摘要： A system and method for detecting and tracking targets including body parts and props is described. In one aspect, the disclosed technology acquires one or more depth images, generates one or more classification maps associated with one or more body parts and one or more props, tracks the one or more body parts using a skeletal tracking system, tracks the one or more props using a prop tracking system, and reports metrics regarding the one or more body parts and the one or more props. In some embodiments, feedback may occur between the skeletal tracking system and the prop tracking system.
摘要翻译：描述了用于检测和跟踪包括身体部位和道具的目标的系统和方法。一方面，所公开的技术获取一个或多个深度图像，生成与一个或多个身体部位和一个或多个道具相关联的一个或多个分类图，使用骨骼跟踪系统跟踪该一个或多个身体部位，使用道具跟踪系统的更多道具，并且报告关于一个或多个身体部位和一个或多个道具的指标。在一些实施例中，可以在骨骼跟踪系统和支架跟踪系统之间进行反馈。

10. 发明申请

US20100272367A1 IMAGE PROCESSING USING GEODESIC FORESTS 有权
标题翻译：使用地质景观的图像处理
公开(公告)号：US20100272367A1
公开(公告)日：2010-10-28
申请号：US12431421
申请日：2009-04-28
申请人： Antonio Criminisi , Toby Sharp
发明人： Antonio Criminisi , Toby Sharp
IPC分类号： G06K9/46
CPC分类号： G06K9/6215 , G06T11/001
摘要： Image processing using geodesic forests is described. In an example, a geodesic forest engine determines geodesic shortest-path distances between each image element and a seed region specified in the image in order to form a geodesic forest data structure. The geodesic distances take into account gradients in the image of a given image modality such as intensity, color, or other modality. In some embodiments, a 1D processing engine carries out 1D processing along the branches of trees in the geodesic forest data structure to form a processed image. For example, effects such as ink painting, edge-aware texture flattening, contrast-aware image editing, forming animations using geodesic forests and other effects are achieved using the geodesic forest data structure. In some embodiments the geodesic forest engine uses a four-part raster scan process to achieve real-time processing speeds and parallelization is possible in many of the embodiments.
摘要翻译：描述了使用测地森林进行图像处理。在一个示例中，测地森林引擎确定每个图像元素与图像中指定的种子区域之间的测距最短路径距离，以形成测地森林数据结构。测距距离考虑了给定图像形态（如强度，颜色或其他形式）图像中的渐变。在一些实施例中，1D处理引擎沿着测地森林数据结构中的树的分支执行1D处理，以形成经处理的图像。例如，使用测地森林数据结构实现诸如水墨绘画，边缘感知纹理平整，对比度感知图像编辑，使用测地森林形成动画等效果。在一些实施例中，测地森林引擎使用四部分光栅扫描过程来实现实时处理速度，并且在许多实施例中并行化是可能的。

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