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公开(公告)号：US08380711B2
公开(公告)日：2013-02-19
申请号：US13045092
申请日：2011-03-10
CPC分类号： G06F17/30277 , G06F17/16 , G06F17/30256 , G06F17/30265 , G06F17/30271 , G06F17/30528 , G06F17/3053 , G06F17/3069 , G06K9/00268 , G06K9/6228 , Y10S707/915
摘要： In response to a query of discernible facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors in a bipartite graph to a base layer ranking sequence as a function of edge weights parameterized by an associated base layer parameter vector. Second-layer rankings define second-layer attribute vectors as bilinear combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched in a bipartite graph to a second-layer ranking sequence as a function of edge weights parameterized by associated second-layer parameter vectors. The images are thus ranked for relevance to the query as a function of fusing the second-layer rankings.
摘要翻译： 响应于可辨别的面部属性的查询，根据与不同属性相关的面部图像数据来估计不同和不同面部区域的位置。 从数据库面部图像从估计的面部区域提取不同的特征，其通过将二分图中的特征向量与基本层排序序列相匹配来排列在基本层排名中，作为由相关联的基本层参数向量参数化的边缘权重的函数。 第二层次排列将第二层属性向量定义为用于共同属性的基层特征向量和相关联的基层参数向量的双线性组合，其在二分图中与作为边权的函数的第二层排序序列匹配 由相关的第二层参数向量参数化。 因此，这些图像作为将第二层排名融合的功能被排列为与查询相关。
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公开(公告)号：US20120233159A1
公开(公告)日：2012-09-13
申请号：US13045092
申请日：2011-03-10
IPC分类号： G06F17/30
CPC分类号： G06F17/30277 , G06F17/16 , G06F17/30256 , G06F17/30265 , G06F17/30271 , G06F17/30528 , G06F17/3053 , G06F17/3069 , G06K9/00268 , G06K9/6228 , Y10S707/915
摘要： In response to a query of discernible facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors in a bipartite graph to a base layer ranking sequence as a function of edge weights parameterized by an associated base layer parameter vector. Second-layer rankings define second-layer attribute vectors as bilinear combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched in a bipartite graph to a second-layer ranking sequence as a function of edge weights parameterized by associated second-layer parameter vectors. The images are thus ranked for relevance to the query as a function of fusing the second-layer rankings.
摘要翻译： 响应于可辨别的面部属性的查询，根据与不同属性相关的面部图像数据来估计不同和不同面部区域的位置。 从数据库面部图像从估计的面部区域提取不同的特征，其通过将二分图中的特征向量与基本层排序序列相匹配来排列在基本层排名中，作为由相关联的基本层参数向量参数化的边缘权重的函数。 第二层次排列将第二层属性向量定义为用于共同属性的基层特征向量和相关联的基层参数向量的双线性组合，其在二分图中与作为边权的函数的第二层排序序列匹配 由相关的第二层参数向量参数化。 因此，这些图像作为将第二层排名融合的功能被排列为与查询相关。
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公开(公告)号：US08917934B2
公开(公告)日：2014-12-23
申请号：US13523074
申请日：2012-06-14
CPC分类号： G06T7/11 , G06K9/00536 , G06K9/00785 , G06K9/3241 , G06K9/38 , G06K9/4642 , G06T7/13 , G06T7/194 , G06T2207/10016 , G06T2207/10024 , G06T2207/20021 , G06T2207/30236
摘要： Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells that are each smaller than that a foreground object of interest. More particularly, image data of the foreground object of interest spans a contiguous plurality of the cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations in view of one or more combination rules.
摘要翻译： 将感兴趣的前景物体与背景模型区分开，将视频数据图像的感兴趣区域划分为各自小于感兴趣的前景对象的各个单元格的网格阵列。 更具体地，感兴趣的前景对象的图像数据跨越连续的多个单元。 如果每个单元内的不同颜色的颜色强度与颜色强度差异阈值相差，或者作为所述确定的组合的函数，则单元格内的累积边缘能量满足边缘能量阈值时，每个单元格被标记为前景 一个或多个组合规则。
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公开(公告)号：US08903198B2
公开(公告)日：2014-12-02
申请号：US13152615
申请日：2011-06-03
CPC分类号： G06F17/30256 , G06F17/30277 , G06F17/3028 , G06F17/3053 , G06K9/00268 , G06K9/481 , G06K9/6263 , G06K9/66 , G06N99/005
摘要： Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may then be searched via the trained attribute detectors for images comprising attributes in a multi-attribute query, wherein images are retrieved from the searching that each comprise one or more of the query attributes and also in response to information from the trained attribute detectors corresponding to attributes that are not a part of the query but are relevant to the query attributes as a function of the learned plurality of pair-wise correlations. The retrieved images are ranked as a function of respective total numbers of attributes within the query subset attributes.
摘要翻译： 根据与训练数据集中注释的不同属性的训练图像属性检测器，根据与多属性查询的属性的相关性来检索和排列图像。 在图像训练数据集的注释属性对之间学习成对相关。 然后可以经由经训练的属性检测器搜索包括多属性查询中的属性的图像的图像数据集，其中从搜索中检索每个包括一个或多个查询属性的图像，并且还响应于来自经训练的属性检测器的信息 对应于不是查询的一部分但与所学习的多个成对相关性的函数的查询属性相关的属性。 检索到的图像根据查询子集属性内的各个属性总数的顺序排列。
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公开(公告)号：US08620026B2
公开(公告)日：2013-12-31
申请号：US13085547
申请日：2011-04-13
IPC分类号： G06K9/00
CPC分类号： G06K9/4604 , G06K9/00751
摘要： Training data object images are clustered as a function of motion direction attributes and resized from respective original into same aspect ratios. Motionlet detectors are learned for each of the sets from features extracted from the resized object blobs. A deformable sliding window is applied to detect an object blob in input by varying window size, shape or aspect ratio to conform to a shape of the detected input video object blob. A motion direction of an underlying image patch of the detected input video object blob is extracted and motionlet detectors selected and applied that have similar motion directions. An object is thus detected within the detected blob and semantic attributes of an underlying image patch extracted if a motionlet detectors fires, the extracted semantic attributes available for use for searching for the detected object.
摘要翻译： 训练数据对象图像作为运动方向属性的函数进行聚类，并从相应的原始尺寸变为相同的宽高比。 通过从调整大小的对象斑点中提取的特征，为每个集合学习运动检测器。 应用可变形滑动窗口通过改变窗口尺寸，形状或宽高比来检测输入中的对象斑点，以符合检测到的输入视频对象斑点的形状。 提取检测到的输入视频对象斑点的底层图像块的运动方向，并选择并应用具有相似运动方向的运动检测器。 因此，如果移动检测器触发，则所提取的底层图像块的检测到的blob和语义属性中的对象被检测到，所提取的语义属性可用于搜索检测到的对象。
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公开(公告)号：US20130336581A1
公开(公告)日：2013-12-19
申请号：US13523074
申请日：2012-06-14
CPC分类号： G06T7/11 , G06K9/00536 , G06K9/00785 , G06K9/3241 , G06K9/38 , G06K9/4642 , G06T7/13 , G06T7/194 , G06T2207/10016 , G06T2207/10024 , G06T2207/20021 , G06T2207/30236
摘要： Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells that are each smaller than that a foreground object of interest. More particularly, image data of the foreground object of interest spans a contiguous plurality of the cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations in view of one or more combination rules.
摘要翻译： 将感兴趣的前景物体与背景模型区分开，将视频数据图像的感兴趣区域划分为各自小于感兴趣的前景对象的各个单元格的网格阵列。 更具体地，感兴趣的前景对象的图像数据跨越连续的多个单元。 如果每个单元内的不同颜色的颜色强度与颜色强度差异阈值相差，或者作为所述确定的组合的函数，则单元格内的累积边缘能量满足边缘能量阈值时，每个单元格被标记为前景 一个或多个组合规则。
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公开(公告)号：US20120263346A1
公开(公告)日：2012-10-18
申请号：US13085547
申请日：2011-04-13
IPC分类号： G06K9/00
CPC分类号： G06K9/4604 , G06K9/00751
摘要： Training data object images are clustered as a function of motion direction attributes and resized from respective original into same aspect ratios. Motionlet detectors are learned for each of the sets from features extracted from the resized object blobs. A deformable sliding window is applied to detect an object blob in input by varying window size, shape or aspect ratio to conform to a shape of the detected input video object blob. A motion direction of an underlying image patch of the detected input video object blob is extracted and motionlet detectors selected and applied that have similar motion directions. An object is thus detected within the detected blob and semantic attributes of an underlying image patch extracted if a motionlet detectors fires, the extracted semantic attributes available for use for searching for the detected object.
摘要翻译： 训练数据对象图像作为运动方向属性的函数进行聚类，并从相应的原始尺寸变为相同的宽高比。 通过从调整大小的对象斑点中提取的特征，为每个集合学习运动检测器。 应用可变形滑动窗口通过改变窗口尺寸，形状或宽高比来检测输入中的对象斑点，以符合检测到的输入视频对象斑点的形状。 提取检测到的输入视频对象斑点的底层图像块的运动方向，并选择并应用具有相似运动方向的运动检测器。 因此，如果移动检测器触发，则所提取的底层图像块的检测到的blob和语义属性中的对象被检测到，所提取的语义属性可用于搜索检测到的对象。
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公开(公告)号：US20140098989A1
公开(公告)日：2014-04-10
申请号：US13645831
申请日：2012-10-05
IPC分类号： G06K9/00
CPC分类号： G06K9/00778 , G06K9/38 , G06K2209/23 , G06T7/248 , G06T2207/10016 , G06T2207/30236
摘要： Multiple discrete objects within a scene image captured by a single camera track are distinguished as un-labeled from a background model within a first frame of a video data input. Object position and object appearance and/or object size attributes are determined for each of the blobs, and costs determined to assign to existing blobs of existing object tracks as a function of the determined attributes and combined to generate respective combination costs. The un-labeled object blob that has a lowest combined cost of association with any of the existing object tracks is labeled with the label of that track having the lowest combined cost, said track is removed from consideration for labeling remaining un-labeled object blobs, and the process iteratively repeated until each of the track labels have been used to label one of the un-labeled blobs.
摘要翻译： 由单个摄像机轨道拍摄的场景图像内的多个离散对象被区分为视频数据输入的第一帧内的背景模型的未标记。 确定每个斑点的对象位置和对象外观和/或对象大小属性，以及确定为根据所确定的属性分配给现有对象轨道的现有块的成本并组合以生成相应的组合成本。 与任何现有对象轨道具有最低组合成本的未标记对象斑点用具有最低组合成本的该轨道的标签进行标记，所述轨道被从考虑中去除以标记剩余的未标记对象斑点， 并且迭代地重复该过程，直到每个轨道标签已被用于标记未标记的一个斑点。
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公开(公告)号：US08457355B2
公开(公告)日：2013-06-04
申请号：US13101401
申请日：2011-05-05
CPC分类号： G06T7/20 , G06K9/00208 , G06T7/251 , G06T13/20 , G06T17/00 , G06T19/006
摘要： A moving object detected and tracked within a field of view environment of a 2D data feed of a calibrated video camera is represented by a 3D model through localizing a centroid of the object and determining an intersection with a ground-plane within the field of view environment. An appropriate 3D mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding 2D image as a function of the centroid and the determined ground-plane intersection. Nonlinear dynamics of a tracked motion path of the object are represented as a collection of different local linear models. A texture of the object is projected onto the 3D model, and 2D tracks of the object are upgraded to 3D motion to drive the 3D model by learning a weighted combination of the different local linear models that minimizes an image re-projection error of model movement.
摘要翻译： 在校准摄像机的2D数据馈送的视野环境内检测和跟踪的移动物体由3D模型表示，其通过定位对象的质心并确定视场环境内的接地平面的交点 。 通过使用对应的2D图像的反投影作为质心和确定的地面交点的函数来初始化用于对象的适当的基于3D网格的体积模型。 对象的跟踪运动路径的非线性动力学被表示为不同局部线性模型的集合。 将对象的纹理投影到3D模型上，并且将对象的2D轨迹升级到3D运动，以通过学习不同局部线性模型的加权组合来驱动3D模型，从而最小化模型运动的图像重新投影误差 。
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公开(公告)号：US20120308121A1
公开(公告)日：2012-12-06
申请号：US13152615
申请日：2011-06-03
IPC分类号： G06K9/62
CPC分类号： G06F17/30256 , G06F17/30277 , G06F17/3028 , G06F17/3053 , G06K9/00268 , G06K9/481 , G06K9/6263 , G06K9/66 , G06N99/005
摘要： Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may then be searched via the trained attribute detectors for images comprising attributes in a multi-attribute query, wherein images are retrieved from the searching that each comprise one or more of the query attributes and also in response to information from the trained attribute detectors corresponding to attributes that are not a part of the query but are relevant to the query attributes as a function of the learned plurality of pair-wise correlations. The retrieved images are ranked as a function of respective total numbers of attributes within the query subset attributes.
摘要翻译： 根据与训练数据集中注释的不同属性的训练图像属性检测器，根据与多属性查询的属性的相关性来检索和排列图像。 在图像训练数据集的注释属性对之间学习成对相关。 然后可以经由经训练的属性检测器搜索包括多属性查询中的属性的图像的图像数据集，其中从搜索中检索每个包括一个或多个查询属性的图像，并且还响应于来自经训练的属性检测器的信息 对应于不是查询的一部分但与所学习的多个成对相关性的函数的查询属性相关的属性。 检索到的图像根据查询子集属性内的各个属性总数的顺序排列。
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