公开(公告)号：US20110113323A1

公开(公告)日：2011-05-12

申请号：US12616423

申请日：2009-11-11

申请人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan

发明人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan

IPC分类号： G06F17/00

CPC分类号： G06F17/2229 ,  G06F17/211

摘要： A system resizes content within a document that includes a document segmenter that receives a document that contains content. The document segmenter analyzes the content within the document and segments the content into a plurality of object types. An object priority applicator determines a class value associated with each object type. A location scaler identifies a datum point for each object type within the document, wherein each datum point maintains a relative location to one another regardless of document resizing. An object sizing component resizes each object based at least in part upon the class value.

摘要翻译： 系统调整文档内的内容大小，其中包含文档分割器，该文档分割器接收包含内容的文档。 文档分割器分析文档内的内容并将内容分段成多个对象类型。 对象优先级施加器确定与每个对象类型相关联的类值。 位置缩放器标识文档中每个对象类型的基准点，其中每个基准点保持彼此的相对位置，而不管文档大小调整。 至少部分基于类值，对象大小调整组件调整每个对象的大小。

公开(公告)号：US08423900B2

公开(公告)日：2013-04-16

申请号：US12544561

申请日：2009-08-20

申请人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

发明人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

IPC分类号： G06F3/00 ,  G06F3/14

CPC分类号： G06T11/60 ,  G06K9/00456 ,  G06T2207/30176

摘要： What is disclosed is a resizing method that utilizes segmentation information to classify objects found within a document and then selects the most appropriate resizing technique for each identified object. The present method employs readily available document parsers to reliably extract objects. e.g. text, background, images, graphics, etc., which compose the document. Information obtained from a document parser is utilized to identify the document components for classification. The extracted objects are then classified according to their object type. Each of classified objects are then resized using a resizing technique having been pre-selected for the object type based on their respective abilities to resize certain types of document content over other resizing techniques. The present method advantageously extends smart or content-based scaling and is especially useful for N-up or variable-information printing. The present method finds its intended uses in enhancing N-up and handout options currently provided in a variety of print-drivers.

摘要翻译： 所公开的是一种调整大小的方法，其利用分段信息对在文档中找到的对象进行分类，然后为每个识别的对象选择最合适的调整大小的技术。 本方法使用容易获得的文档解析器来可靠地提取对象。 例如 文本，背景，图像，图形等等。 从文档解析器获得的信息用于识别用于分类的文档组件。 然后将提取的对象根据其对象类型进行分类。 然后，使用已经针对对象类型预先选择的调整大小的技术来调整每个分类对象的大小，这些大小基于它们各自的能力，以便通过其他大小调整技术调整某些类型的文档内容的大小。 本方法有利地扩展智能或基于内容的缩放，并且对于N上或可变信息打印特别有用。 本方法用于增强目前在各种打印驱动程序中提供的N-up和Handout选项。

公开(公告)号：US20110047505A1

公开(公告)日：2011-02-24

申请号：US12544561

申请日：2009-08-20

申请人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

发明人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

IPC分类号： G06F3/048

CPC分类号： G06T11/60 ,  G06K9/00456 ,  G06T2207/30176

摘要： What is disclosed is a resizing method that utilizes segmentation information to classify objects found within a document and then selects the most appropriate resizing technique for each identified object. The present method employs readily available document parsers to reliably extract objects. e.g. text, background, images, graphics, etc., which compose the document. Information obtained from a document parser is utilized to identify the document components for classification. The extracted objects are then classified according to their object type. Each of classified objects are then resized using a resizing technique having been pre-selected for the object type based on their respective abilities to resize certain types of document content over other resizing techniques. The present method advantageously extends smart or content-based scaling and is especially useful for N-up or variable-information printing. The present method finds its intended uses in enhancing N-up and handout options currently provided in a variety of print-drivers.

摘要翻译： 所公开的是一种调整大小的方法，其利用分段信息对在文档中找到的对象进行分类，然后为每个识别的对象选择最合适的调整大小的技术。 本方法使用容易获得的文档解析器来可靠地提取对象。 例如 文本，背景，图像，图形等等。 从文档解析器获得的信息用于识别用于分类的文档组件。 然后将提取的对象根据其对象类型进行分类。 然后，使用已经针对对象类型预先选择的调整大小的技术来调整每个分类对象的大小，这些大小基于它们各自的能力，以便通过其他大小调整技术调整某些类型的文档内容的大小。 本方法有利地扩展智能或基于内容的缩放，并且对于N上或可变信息打印特别有用。 本方法用于增强目前在各种打印驱动程序中提供的N-up和Handout选项。

公开(公告)号：US20100201711A1

公开(公告)日：2010-08-12

申请号：US12369790

申请日：2009-02-12

申请人： CLAUDE S. FILLION ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

发明人： CLAUDE S. FILLION ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

IPC分类号： G09G5/00

CPC分类号： G06T3/0012

摘要： What is disclosed is a system and method for performing a background deletion that exploits both local and global context to remove background and other white space between objects with the aim of retaining structural relationships between objects in the document. A document image is received and seams are carved through the image. Seams composed of uniform background pixels are identified. Adjacent seams containing background pixels are collected into groups of seams. The background seam groups are classified according to their widths. A target number of seams to be removed for each background seam group is then determined based on the classification. Seam groups which are wider will have at least the same or a greater target number of seams to be deleted therefrom than will seam groups of narrower widths. The document image is then resized by deleting seams from the seam groups based on the assigned target number.

摘要翻译： 公开的是用于执行背景删除的系统和方法，其利用本地和全局上下文来移除对象之间的背景和其他空白空间，目的是保留文档中的对象之间的结构关系。 收到文件图像，并通过图像刻成接缝。 识别由均匀背景像素构成的接缝。 包含背景像素的相邻接缝被收集成一组接缝。 背景缝组根据其宽度进行分类。 然后基于分类确定要为每个背景接缝组去除的目标接缝数目。 与较窄宽度的接缝组相比，更宽的接缝组将具有至少相同或更大的目标数量的接缝。 然后通过基于分配的目标号码从接缝组中删除接缝来调整文档图像的大小。

公开(公告)号：US08352856B2

公开(公告)日：2013-01-08

申请号：US12616423

申请日：2009-11-11

申请人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan

发明人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan

IPC分类号： G06F17/00

CPC分类号： G06F17/2229 ,  G06F17/211

摘要： A system resizes content within a document that includes a document segmenter that receives a document that contains content. The document segmenter analyzes the content within the document and segments the content into a plurality of object types. An object priority applicator determines a class value associated with each object type. A location scaler identifies a datum point for each object type within the document, wherein each datum point maintains a relative location to one another regardless of document resizing. An object sizing component resizes each object based at least in part upon the class value.

摘要翻译： 系统调整文档内的内容大小，其中包含文档分割器，该文档分割器接收包含内容的文档。 文档分割器分析文档内的内容并将内容分段成多个对象类型。 对象优先级施加器确定与每个对象类型相关联的类值。 位置缩放器识别文档中每个对象类型的基准点，其中每个基准点保持彼此的相对位置，而不管文档大小调整。 至少部分基于类值，对象大小调整组件调整每个对象的大小。

公开(公告)号：US08274533B2

公开(公告)日：2012-09-25

申请号：US12369790

申请日：2009-02-12

申请人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

发明人： Claude S. Fillion ,  Vishal Monga ,  Zhigang Fan ,  Ramesh Nagarajan

IPC分类号： G09G5/02

CPC分类号： G06T3/0012

摘要： What is disclosed is a system and method for performing a background deletion that exploits both local and global context to remove background and other white space between objects with the aim of retaining structural relationships between objects in the document. A document image is received and seams are carved through the image. Seams composed of uniform background pixels are identified. Adjacent seams containing background pixels are collected into groups of seams. The background seam groups are classified according to their widths. A target number of seams to be removed for each background seam group is then determined based on the classification. Seam groups which are wider will have at least the same or a greater target number of seams to be deleted therefrom than will seam groups of narrower widths. The document image is then resized by deleting seams from the seam groups based on the assigned target number.

摘要翻译： 公开的是用于执行背景删除的系统和方法，其利用本地和全局上下文来移除对象之间的背景和其他空白空间，目的是保留文档中的对象之间的结构关系。 收到文件图像，并通过图像刻成接缝。 识别由均匀背景像素构成的接缝。 包含背景像素的相邻接缝被收集成一组接缝。 背景缝组根据其宽度进行分类。 然后基于分类确定要为每个背景接缝组去除的目标接缝数目。 与较窄宽度的接缝组相比，更宽的接缝组将具有至少相同或更大的目标数量的接缝。 然后通过基于分配的目标号码从接缝组中删除接缝来调整文档图像的大小。

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。

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.

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。

公开(公告)号：US20100183220A1

公开(公告)日：2010-07-22

申请号：US12356865

申请日：2009-01-21

申请人： Vishal Monga ,  Zhigang Fan

发明人： Vishal Monga ,  Zhigang Fan

IPC分类号： G06K9/00

CPC分类号： H04N1/6033 ,  H04N1/6058

摘要： A model-based method and apparatus for characterizing the performance of a printing device comprising printing a target set of patches with the device and measuring device response when the target is set; compiling a LUT from the target set and measured response; and representing the LUT as a tensor. Tensor decomposition/parallel factor analysis is employed for compacting the tensor representation of the LUT.

摘要翻译： 一种用于表征打印设备的性能的基于模型的方法和装置，包括：当设置所述目标时，利用所述设备和测量设备响应来打印目标组的补丁; 从目标集合和测量响应中编译LUT; 并将LUT表示为张量。 张量分解/并行因子分析用于压缩LUT的张量表示。