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    Soft picture/graphics classification system and method
    1.
    发明授权
    Soft picture/graphics classification system and method 失效
    软图片/图形分类系统和方法

    公开(公告)号：US06947597B2

    公开(公告)日：2005-09-20

    申请号：US09965880

    申请日：2001-09-28

    申请人： Ying-wei Lin ,  Stuart A. Schweid ,  Jeng-nan Shiau ,  Raja Bala ,  Zhigang Fan

    发明人： Ying-wei Lin ,  Stuart A. Schweid ,  Jeng-nan Shiau ,  Raja Bala ,  Zhigang Fan

    IPC分类号： G06K9/20 ,  G06T5/00 ,  H04N1/40 ,  G06K9/62

    CPC分类号： H04N1/40062 ,  G06K9/00456

    摘要： A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R_L, R_U, and R_V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a “soft” image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The “soft” classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.

    摘要翻译： 一种用于图像处理的方法和系统，结合使用SGLD纹理（例如，方差，偏差，偏度和适应度）的自然图像和合成图像之间的图像分类，颜色离散性（例如，R_L，R_U和R_V归一化直方图 ）或边缘特征（例如，每个检测到的边缘的像素，水平边缘和垂直边缘）。 在另一个实施例中，提供了使用SGLD纹理，颜色离散性和边缘特征的组合的图片/图形分类器。 在另一个实施例中，提供了使用两（2）或更多SGLD纹理，颜色离散性和边缘特征的组合的“软”图像分类器。 “软”分类器使用图像特征来对图像，图形或模糊类中的输入图像的区域进行分类。

    Personalized medical record
    3.
    发明申请
    Personalized medical record 有权
    个性化医疗记录

    公开(公告)号：US20140075295A1

    公开(公告)日：2014-03-13

    申请号：US13609541

    申请日：2012-09-11

    申请人： Beilei Xu ,  Stuart A. Schweid ,  Raja Bala ,  Rinku Gajera

    发明人： Beilei Xu ,  Stuart A. Schweid ,  Raja Bala ,  Rinku Gajera

    IPC分类号： G06F17/21

    CPC分类号： G06F17/248 ,  G06F17/289

    摘要： The present disclosure provides a method of producing a personalized medical record, comprising: sensing capabilities of a receiving device; retrieving stock information; retrieving personalized information; combining at least a portion of the stock information and at least a portion of the personalized information into the personalized record; formatting the personalized record based on a combination of the capabilities of the receiving device and a user's preference; and, transmitting the formatted personalized record to the device.

    摘要翻译： 本公开提供了一种产生个性化医疗记录的方法，包括：感测接收设备的能力; 检索股票信息; 检索个性化信息; 将至少一部分股票信息和至少一部分个性化信息合并到个性化记录中; 基于接收设备的能力和用户偏好的组合来格式化个性化记录; 并将格式化的个性化记录发送到设备。

    Methods and system for analyzing and rating images for personalization
    5.
    发明授权
    Methods and system for analyzing and rating images for personalization 有权
    用于个性化分析和评估图像的方法和系统

    公开(公告)号：US09042640B2

    公开(公告)日：2015-05-26

    申请号：US13349751

    申请日：2012-01-13

    申请人： Raja Bala ,  Zhigang Fan ,  Hengzhou Ding ,  Jan P. Allebach ,  Charles A. Bouman ,  Reuven J. Sherwin

    发明人： Raja Bala ,  Zhigang Fan ,  Hengzhou Ding ,  Jan P. Allebach ,  Charles A. Bouman ,  Reuven J. Sherwin

    IPC分类号： G06K9/62 ,  G06K9/00 ,  G06K9/32

    CPC分类号： G06K9/00671 ,  G06K9/3258

    摘要： As set forth herein, a computer-implemented method facilitates pre-analyzing an image and automatically suggesting to the user the most suitable regions within an image for text-based personalization. Image regions that are spatially smooth and regions with existing text (e.g. signage, banners, etc.) are primary candidates for personalization. This gives rise to two sets of corresponding algorithms: one for identifying smooth areas, and one for locating text regions. Smooth regions are found by dividing the image into blocks and applying an iterative combining strategy, and those regions satisfying certain spatial properties (e.g. size, position, shape of the boundary) are retained as promising candidates. In one embodiment, connected component analysis is performed on the image for locating text regions. Finally, based on the smooth and text regions found in the image, several alternative approaches are described herein to derive an overall metric for “suitability for personalization.”

    摘要翻译： 如本文所述，计算机实现的方法有助于预分析图像并且自动地向用户建议图像内的最合适的区域用于基于文本的个性化。 具有空间平滑的图像区域和具有现有文本的区域（例如标牌，横幅等）是用于个性化的主要候选者。 这产生了两组相应的算法：一种用于识别平滑区域，一种用于定位文本区域。 通过将图像划分成块并应用迭代组合策略来找到平滑区域，并且满足某些空间属性（例如，边界的大小，位置，形状）的那些区域被保留为有希望的候选者。 在一个实施例中，对用于定位文本区域的图像执行连接分量分析。 最后，基于图像中发现的平滑和文本区域，本文描述了几种替代方法，以得出“适合个性化”的总体度量。

    FINDING TEXT IN NATURAL SCENES
    6.
    发明申请
    FINDING TEXT IN NATURAL SCENES 有权
    在自然景观中寻找文字

    公开(公告)号：US20130330004A1

    公开(公告)日：2013-12-12

    申请号：US13494173

    申请日：2012-06-12

    申请人： Raja Bala ,  Zhigang Fan ,  Hengzhou Ding ,  Jan P. Allebach ,  Charles A. Bouman

    发明人： Raja Bala ,  Zhigang Fan ,  Hengzhou Ding ,  Jan P. Allebach ,  Charles A. Bouman

    IPC分类号： G06K9/18 ,  G06K9/34

    CPC分类号： G06K9/4604 ,  G06K9/3258 ,  G06K9/4638 ,  G06K2209/01 ,  G06T7/13 ,  G06T7/181 ,  G06T2207/10004

    摘要： As set forth herein, systems and methods facilitate providing an efficient edge-detection and closed-contour based approach for finding text in natural scenes such as photographic images, digital, and/or electronic images, and the like. Edge information (e.g., edges of structures or objects in the images) is obtained via an edge detection technique. Edges from text characters form closed contours even in the presence of reasonable levels of noise. Closed contour linking and candidate text line formation are two additional features of the described approach. A candidate text line classifier is applied to further screen out false-positive text identifications. Candidate text regions for placement of text in the natural scene of the electronic image are highlighted and presented to a user.

    摘要翻译： 如本文所述，系统和方法有助于提供有效的边缘检测和基于闭合轮廓的方法，用于在诸如照相图像，数字和/或电子图像等的自然场景中查找文本。 通过边缘检测技术获得边缘信息（例如，图像中的结构或对象的边缘）。 即使存在合理的噪声水平，文本字符的边缘也会形成封闭的轮廓。 闭合轮廓链接和候选文本线形成是所述方法的两个附加特征。 应用候选文本行分类器进一步筛选出假阳性文本标识。 用于在电子图像的自然场景中放置文本的候选文本区域被突出显示并呈现给用户。

    METHOD AND SYSTEM FOR AUTOMATICALLY DETECTING MULTI-OBJECT ANOMALIES UTILIZING JOINT SPARSE RECONSTRUCTION MODEL
    7.
    发明申请
    METHOD AND SYSTEM FOR AUTOMATICALLY DETECTING MULTI-OBJECT ANOMALIES UTILIZING JOINT SPARSE RECONSTRUCTION MODEL 有权
    使用联合稀疏重建模型自动检测多对象异常的方法和系统

    公开(公告)号：US20130286208A1

    公开(公告)日：2013-10-31

    申请号：US13476239

    申请日：2012-05-21

    申请人： Raja Bala ,  Zhigang Fan ,  Aaron Burry ,  Vishal Monga ,  Xuan Mo

    发明人： Raja Bala ,  Zhigang Fan ,  Aaron Burry ,  Vishal Monga ,  Xuan Mo

    IPC分类号： H04N7/18

    CPC分类号： G06K9/00771 ,  G06K9/6249 ,  G08G1/0129 ,  H04N7/181

    摘要： Methods and systems for automatically detecting multi-object anomalies at a traffic intersection utilizing a joint sparse reconstruction model. A first input video sequence at a first traffic location can be received and at least one normal event involving P moving objects (where P is greater than or equal to 1) can be identified in an offline training phase. The normal event in the first input video sequence can be assigned to at least one normal event class and a training dictionary suitable for joint sparse reconstruction can be built in the offline training phase. A second input video sequence captured at a second traffic location similar to the first traffic location can be received and at least one event involving P moving objects can be identified in an online detection phase.

    摘要翻译： 利用联合稀疏重建模型自动检测交通路口多物体异常的方法和系统。 可以在离线训练阶段中识别在第一交通位置处的第一输入视频序列，并且可以在离线训练阶段识别涉及P个运动对象（其中P大于或等于1）的至少一个正常事件。 可以将第一输入视频序列中的正常事件分配给至少一个正常事件类，并且可以在离线训练阶段中构建适合关联稀疏重建的训练字典。 可以接收在类似于第一业务位置的第二业务位置处捕获的第二输入视频序列，并且可以在在线检测阶段中识别涉及P个移动对象的至少一个事件。

    METHODS AND SYSTEM FOR ANALYZING AND RATING IMAGES FOR PERSONALIZATION
    8.
    发明申请
    METHODS AND SYSTEM FOR ANALYZING AND RATING IMAGES FOR PERSONALIZATION 有权
    用于分析和评估个性化图像的方法和系统

    公开(公告)号：US20130182946A1

    公开(公告)日：2013-07-18

    申请号：US13349751

    申请日：2012-01-13

    申请人： Raja Bala ,  Zhigang Fan ,  Hengzhou Ding ,  Jan P. Allebach ,  Charles A. Bouman ,  Reuven J. Sherwin

    发明人： Raja Bala ,  Zhigang Fan ,  Hengzhou Ding ,  Jan P. Allebach ,  Charles A. Bouman ,  Reuven J. Sherwin

    IPC分类号： G06K9/46 ,  G06K9/62

    CPC分类号： G06K9/00671 ,  G06K9/3258

    摘要： As set forth herein, a computer-implemented method facilitates pre-analyzing an image and automatically suggesting to the user the most suitable regions within an image for text-based personalization. Image regions that are spatially smooth and regions with existing text (e.g. signage, banners, etc.) are primary candidates for personalization. This gives rise to two sets of corresponding algorithms: one for identifying smooth areas, and one for locating text regions. Smooth regions are found by dividing the image into blocks and applying an iterative combining strategy, and those regions satisfying certain spatial properties (e.g. size, position, shape of the boundary) are retained as promising candidates. In one embodiment, connected component analysis is performed on the image for locating text regions. Finally, based on the smooth and text regions found in the image, several alternative approaches are described herein to derive an overall metric for “suitability for personalization.”

    摘要翻译： 如本文所述，计算机实现的方法有助于预分析图像并且自动地向用户建议图像内的最合适的区域用于基于文本的个性化。 具有空间平滑的图像区域和具有现有文本的区域（例如标牌，横幅等）是用于个性化的主要候选者。 这产生了两组相应的算法：一种用于识别平滑区域，一种用于定位文本区域。 通过将图像划分成块并应用迭代组合策略来找到平滑区域，并且满足某些空间属性（例如，边界的大小，位置，形状）的那些区域被保留为有希望的候选者。 在一个实施例中，对用于定位文本区域的图像执行连接分量分析。 最后，基于图像中发现的平滑和文本区域，本文描述了几种替代方法，以得出“适合个性化”的总体度量。

    Anomaly detection using a kernel-based sparse reconstruction model
    9.
    发明授权
    Anomaly detection using a kernel-based sparse reconstruction model 有权

    公开(公告)号：US09710727B2

    公开(公告)日：2017-07-18

    申请号：US13773097

    申请日：2013-02-21

    申请人： Raja Bala ,  Vishal Monga ,  Xuan Mo ,  Zhigang Fan

    发明人： Raja Bala ,  Vishal Monga ,  Xuan Mo ,  Zhigang Fan

    IPC分类号： G06K9/62 ,  G06K9/00

    CPC分类号： G06K9/6256 ,  G06K9/00771 ,  G06K9/6249

    摘要： A method and system for detecting anomalies in video footage. A training dictionary can be configured to include a number of event classes, wherein events among the event classes can be defined with respect to n-dimensional feature vectors. One or more nonlinear kernel function can be defined, which transform the n-dimensional feature vectors into a higher dimensional feature space. One or more test events can then be received within an input video sequence of the video footage. Thereafter, a determination can be made if the test event(s) is anomalous by applying a sparse reconstruction with respect to the training dictionary in the higher dimensional feature space induced by the nonlinear kernel function.

    ANOMALY DETECTION USING A KERNEL-BASED SPARSE RECONSTRUCTION MODEL
    10.
    发明申请
    ANOMALY DETECTION USING A KERNEL-BASED SPARSE RECONSTRUCTION MODEL 有权
    使用基于KERNEL的SPARSE重建模型进行异常检测

    公开(公告)号：US20140232862A1

    公开(公告)日：2014-08-21

    申请号：US13773097

    申请日：2013-02-21

    申请人： Raja Bala ,  Vishal Monga ,  Xuan Mo ,  Zhigang Fan

    发明人： Raja Bala ,  Vishal Monga ,  Xuan Mo ,  Zhigang Fan

    IPC分类号： G06K9/62

    CPC分类号： G06K9/6256 ,  G06K9/00771 ,  G06K9/6249

    摘要： A method and system for detecting anomalies in video footage. A training dictionary can be configured to include a number of event classes, wherein events among the event classes can be defined with respect to n-diminensional feature vectors. One or more nonlinear kernel function can be defined, which transform the n-dimensional feature vectors into a higher dimensional feature space. One or more test events can then be received within an input video sequence of the video footage. Thereafter, a determination can be made if the test event(s) is anomalous by applying a sparse reconstruction with respect to the training dictionary in the higher dimensional feature space induced by the nonlinear kernel function.

    摘要翻译： 一种用于检测视频画面异常的方法和系统。 训练词典可以被配置为包括多个事件类，其中事件类中的事件可以相对于n维特征向量来定义。 可以定义一个或多个非线性内核函数，其将n维特征向量变换成更高维度的特征空间。 然后可以在视频录像的输入视频序列内接收一个或多个测试事件。 此后，如果通过对由非线性内核函数引起的较高维特征空间中的训练词典应用稀疏重建来测试事件是异常的，则可以进行确定。