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    Estimation of object properties in 3D world
    1.
    发明授权
    Estimation of object properties in 3D world 有权
    3D世界中对象属性的估计

    公开(公告)号：US08842163B2

    公开(公告)日：2014-09-23

    申请号：US13154843

    申请日：2011-06-07

    申请人： Ankur Datta ,  Rogerio S. Feris ,  Yun Zhai

    发明人： Ankur Datta ,  Rogerio S. Feris ,  Yun Zhai

    IPC分类号： H04N13/00 ,  G06T7/20

    CPC分类号： G06T17/10 ,  G06K9/00711 ,  G06K9/00771 ,  G06K9/46 ,  G06K9/4604 ,  G06K9/52 ,  G06K2009/4666 ,  G06T3/20 ,  G06T3/40 ,  G06T7/20 ,  G06T7/251 ,  G06T7/62 ,  G06T7/75 ,  G06T15/205 ,  G06T19/20 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/30232 ,  G06T2207/30236 ,  G06T2219/2004 ,  G06T2219/2016 ,  H04N13/264 ,  H04N13/293 ,  H04N2013/0088

    摘要： Objects within two-dimensional (2D) video data are modeled by three-dimensional (3D) models as a function of object type and motion through manually calibrating a 2D image to the three spatial dimensions of a 3D modeling cube. Calibrated 3D locations of an object in motion in the 2D image field of view of a video data input are computed and used to determine a heading direction of the object as a function of the camera calibration and determined movement between the computed 3D locations. The 2D object image is replaced in the video data input with an object-type 3D polygonal model having a projected bounding box that best matches a bounding box of an image blob, the model oriented in the determined heading direction. The bounding box of the replacing model is then scaled to fit the object image blob bounding box, and rendered with extracted image features.

    摘要翻译： 二维（2D）视频数据中的对象通过三维（3D）模型建模，作为对象类型和运动的函数，通过手动校准2D图像到3D建模立方体的三个空间维度。 计算视频数据输入的2D图像视场中的运动对象的校准3D位置，并用于根据相机校准和所计算的3D位置之间确定的运动来确定对象的航向方向。 2D对象图像被替换为具有对象型3D多边形模型的视频数据输入，该模型具有最佳匹配图像斑点的边界框的投影边界框，该模型在确定的方位方向上定向。 然后将替换模型的边界框缩放以适合对象图像Blob边界框，并使用提取的图像特征进行渲染。

    DETERMINATION OF TRAIN PRESENCE AND MOTION STATE IN RAILWAY ENVIRONMENTS
    2.
    发明申请
    DETERMINATION OF TRAIN PRESENCE AND MOTION STATE IN RAILWAY ENVIRONMENTS 有权
    铁路环境中火车存在和运动状态的确定

    公开(公告)号：US20140056479A1

    公开(公告)日：2014-02-27

    申请号：US13590269

    申请日：2012-08-21

    申请人： Russell P. Bobbitt ,  Rogerio S. Feris ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Rogerio S. Feris ,  Yun Zhai

    IPC分类号： G06K9/62

    CPC分类号： G06K9/00771 ,  G06K9/00718 ,  G06K9/00765 ,  G06K9/6212 ,  G06T7/11

    摘要： Foreground feature data and motion feature data is determined for frames of video data acquired from a train track area region of interest. The frames are labeled as “train present” if the determined foreground feature data value meets a threshold value, else as “train absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no train present” segment for groups with “train absent” and “static” labels; within a “train present and in transition” segment for groups “train present” and “motion present” labels; and within a “train present and stopped” segment for groups with “train present” and “static” labels. The presence or motion state of a train at a time of inquiry is thereby determined from the respective segment classification.

    摘要翻译： 确定从感兴趣的列车轨道区域获取的视频数据的帧的前景特征数据和运动特征数据。 如果确定的前景特征数据值满足阈值，则将帧标记为“列车存在”，否则，如果运动特征数据满足运动阈值，否则为“不存在运动;如运动特征数据满足运动阈值，否则为”静态“。 标签用于对包括连续视频帧组的视频数据的段进行分类，即对于具有“列车不存在”和“静态”标签的组的“无列车存在”段内;在“列车存在和转换”段内 在“火车现在”和“静态”标签组中，“列车现在”和“动作现状”标签组成的“火车现在和停止”部分中，列车在询问时的存在或运动状态为 由各分段分类确定。

    ESTIMATION OF OBJECT PROPERTIES IN 3D WORLD
    4.
    发明申请
    ESTIMATION OF OBJECT PROPERTIES IN 3D WORLD 有权
    3D世界对象特性的估计

    公开(公告)号：US20120314030A1

    公开(公告)日：2012-12-13

    申请号：US13154843

    申请日：2011-06-07

    申请人： Ankur Datta ,  Rogerio S. Feris ,  Yun Zhai

    发明人： Ankur Datta ,  Rogerio S. Feris ,  Yun Zhai

    IPC分类号： H04N13/00

    CPC分类号： G06T17/10 ,  G06K9/00711 ,  G06K9/00771 ,  G06K9/46 ,  G06K9/4604 ,  G06K9/52 ,  G06K2009/4666 ,  G06T3/20 ,  G06T3/40 ,  G06T7/20 ,  G06T7/251 ,  G06T7/62 ,  G06T7/75 ,  G06T15/205 ,  G06T19/20 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/30232 ,  G06T2207/30236 ,  G06T2219/2004 ,  G06T2219/2016 ,  H04N13/264 ,  H04N13/293 ,  H04N2013/0088

    摘要： Objects within two-dimensional (2D) video data are modeled by three-dimensional (3D) models as a function of object type and motion through manually calibrating a 2D image to the three spatial dimensions of a 3D modeling cube. Calibrated 3D locations of an object in motion in the 2D image field of view of a video data input are computed and used to determine a heading direction of the object as a function of the camera calibration and determined movement between the computed 3D locations. The 2D object image is replaced in the video data input with an object-type 3D polygonal model having a projected bounding box that best matches a bounding box of an image blob, the model oriented in the determined heading direction. The bounding box of the replacing model is then scaled to fit the object image blob bounding box, and rendered with extracted image features.

    摘要翻译： 二维（2D）视频数据中的对象通过三维（3D）模型建模，作为对象类型和运动的函数，通过手动校准2D图像到3D建模立方体的三个空间维度。 计算视频数据输入的2D图像视场中的运动对象的校准3D位置，并用于根据相机校准和所计算的3D位置之间的确定的运动来确定对象的方位方向。 2D对象图像被替换为具有对象型3D多边形模型的视频数据输入，该模型具有最佳匹配图像斑点的边界框的投影边界框，该模型在确定的方位方向上定向。 然后将替换模型的边界框缩放以适合对象图像Blob边界框，并使用提取的图像特征进行渲染。

    OBJECT DETECTION SYSTEM BASED ON A POOL OF ADAPTIVE FEATURES
    5.
    发明申请
    OBJECT DETECTION SYSTEM BASED ON A POOL OF ADAPTIVE FEATURES 有权
    基于自适应特征的对象检测系统

    公开(公告)号：US20120121170A1

    公开(公告)日：2012-05-17

    申请号：US13353485

    申请日：2012-01-19

    申请人： Rogerio S. Feris ,  Arun Hampapur ,  Ying-Li Tian

    发明人： Rogerio S. Feris ,  Arun Hampapur ,  Ying-Li Tian

    IPC分类号： G06K9/62

    CPC分类号： G06K9/6228

    摘要： A method, system and computer program product for detecting presence of an object in an image are disclosed. According to an embodiment, a method for detecting a presence of an object in an image comprises: receiving multiple training image samples; determining a set of adaptive features for each training image sample, the set of adaptive features matching the local structure of each training image sample; integrating the sets of adaptive features of the multiple training image samples to generate an adaptive feature pool; determining a general feature based on the adaptive feature pool; and examining the image using a classifier determined based on the general feature to detect the presence of the object.

    摘要翻译： 公开了一种用于检测图像中的对象的存在的方法，系统和计算机程序产品。 根据实施例，用于检测图像中的对象的存在的方法包括：接收多个训练图像样本; 确定每个训练图像样本的一组自适应特征，与每个训练图像样本的局部结构匹配的一组自适应特征; 整合多个训练图像样本的自适应特征的集合以生成自适应特征池; 基于自适应特征池确定一般特征; 以及使用基于一般特征确定的分类器来检查图像以检测对象的存在。

    Object Segmentation at a Self-Checkout
    6.
    发明申请
    Object Segmentation at a Self-Checkout 失效
    自检中的对象分割

    公开(公告)号：US20120027297A1

    公开(公告)日：2012-02-02

    申请号：US12844340

    申请日：2010-07-27

    申请人： Rogerio S. Feris ,  Charles A. Otto ,  Sharathchandra Pankanti ,  Duan D. Tran

    发明人： Rogerio S. Feris ,  Charles A. Otto ,  Sharathchandra Pankanti ,  Duan D. Tran

    IPC分类号： G06K9/34

    CPC分类号： G06T7/0079 ,  G06K9/34 ,  G06T7/11 ,  G06T7/136 ,  G06T2207/10024 ,  G06T2207/20012 ,  G06T2207/20021

    摘要： Techniques for segmenting an object at a self-checkout are provided. The techniques include capturing an image of an object at a self-checkout, dividing the image into one or more blocks, computing one or more features of the image, computing a confidence value for each of the one or more blocks, wherein computing a confidence value for each of the one or more blocks comprises using a minimum feature distance from one or more reference backgound blocks, and eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object.

    摘要翻译： 提供了在自检结果中分割对象的技术。 这些技术包括在自检结果中捕获对象的图像，将图像划分成一个或多个块，计算图像的一个或多个特征，计算一个或多个块中的每一个的置信度值，其中计算置信度 一个或多个块中的每个块的值包括使用来自一个或多个参考背景块的最小特征距离，并且通过使用针对所述一个或多个块中的每个块的置信度值计算的自适应阈值来消除考虑中的一个或多个块 ，其中所述一个或多个块保持映射到包含所述对象的图像的区域。

    Detection of static object on thoroughfare crossings
    8.
    发明授权
    Detection of static object on thoroughfare crossings 有权

    公开(公告)号：US09008359B2

    公开(公告)日：2015-04-14

    申请号：US13535461

    申请日：2012-06-28

    申请人： Rogerio S. Feris ,  Yun Zhai

    发明人： Rogerio S. Feris ,  Yun Zhai

    IPC分类号： G06K9/00 ,  G06T7/20

    CPC分类号： G06K9/00771 ,  G06K9/00805 ,  G06T7/194 ,  G06T7/254 ,  G06T7/269 ,  G06T2207/10016 ,  G06T2207/30232 ,  G06T2207/30236 ,  G06T2207/30241

    摘要： Foreground object image features are extracted from input video via application of a background subtraction mask, and optical flow image features from a region of the input video image data defined by the extracted foreground object image features. If estimated movement features indicate that the underlying object is in motion, a dominant moving direction of the underlying object is determined. If the dominant moving direction is parallel to an orientation of the second, crossed thoroughfare, an event alarm indicating that a static object is blocking travel on the crossing second thoroughfare is not generated. If the estimated movement features indicate that the underlying object is static, or that its determined dominant moving direction is not parallel to the second thoroughfare, an appearance of the foreground object region is determined and a static-ness timer run while the foreground object region comprises the extracted foreground object image features.

    Image ranking based on attribute correlation
    10.
    发明授权
    Image ranking based on attribute correlation 有权
    基于属性相关的图像排名

    公开(公告)号：US08903198B2

    公开(公告)日：2014-12-02

    申请号：US13152615

    申请日：2011-06-03

    申请人： Ankur Datta ,  Rogerio S. Feris ,  Sharathchandra U. Pankanti ,  Behjat Siddiquie

    发明人： Ankur Datta ,  Rogerio S. Feris ,  Sharathchandra U. Pankanti ,  Behjat Siddiquie

    IPC分类号： G06K9/60 ,  G06F17/30 ,  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。