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公开(公告)号：US20070198971A1
公开(公告)日：2007-08-23
申请号：US10544894
申请日：2004-02-05
IPC分类号： G06F9/45
CPC分类号： G06F8/433 , G06F8/4432 , G06F17/5045 , Y02D10/41
摘要： A method of producing a reconfigurable circuit device for running a computer program of moderate complexity such as multimedia processing. Code for the application is compiled into Control Flow Graphs representing distinct parts of the application to be run. From those Control Flow Graphs are extracted basic blocks. The basic blocks are converted to Data Flow Graphs by a compiler utility. From two or more Data Flow Graphs, a largest common subgraph is determined. The largest common subgraph is ASAP scheduled and substituted back into the Data Flow Graphs which also have been scheduled. The separate Data Flow Graphs containing the scheduled largest common subgraph are converted to data paths that are then combined to form code for operating the application. The largest common subgraph is effected in hardware that is shared among the parts of the application from which the Data Flow Graphs were developed. Scheduling of the overall code is effected for sequencing, providing fastest run times and the code is implemented in hardware by partitioning and placement of processing elements on a chip and design of the connective fabric for the design elements.
摘要翻译： 一种用于运行诸如多媒体处理等中等复杂度的计算机程序的可重构电路装置的方法。 应用程序的代码被编译成表示要运行的应用程序的不同部分的控制流图。 从这些控制流图中提取基本块。 基本块由编译器实用程序转换为数据流图。 从两个或更多数据流图，确定最大的公共子图。 最大的公共子图是ASAP预定的，并被替换为也被安排的数据流图。 包含预定最大公共子图的单独数据流图被转换为数据路径，然后将其组合以形成用于操作应用程序的代码。 最大的共同子图是在开发数据流图的应用程序部分之间共享的硬件实现的。 总体代码的调度受到排序的影响，提供最快的运行时间，并且代码通过在处理元件上分配和放置芯片以及为设计元素设计的连接结构来实现。
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公开(公告)号：US08281297B2
公开(公告)日：2012-10-02
申请号：US10544894
申请日：2004-02-05
IPC分类号： G06F9/45
CPC分类号： G06F8/433 , G06F8/4432 , G06F17/5045 , Y02D10/41
摘要： A method of producing a reconfigurable circuit device for running a computer program of moderate complexity such as multimedia processing. Code for the application is compiled into Control Flow Graphs representing distinct parts of the application to be run. From those Control Flow Graphs are extracted basic blocks. The basic blocks are converted to Data Flow Graphs by a compiler utility. From two or more Data Flow Graphs, a largest common subgraph is determined. The largest common subgraph is ASAP scheduled and substituted back into the Data Flow Graphs which also have been scheduled. The separate Data Flow Graphs containing the scheduled largest common subgraph are converted to data paths that are then combined to form code for operating the application. The largest common subgraph is effected in hardware that is shared among the parts of the application from which the Data Flow Graphs were developed. Scheduling of the overall code is effected for sequencing, providing fastest run times and the code is implemented in hardware by partitioning and placement of processing elements on a chip and design of the connective fabric for the design elements.
摘要翻译： 一种用于运行诸如多媒体处理等中等复杂度的计算机程序的可重构电路装置的方法。 应用程序的代码被编译成表示要运行的应用程序的不同部分的控制流图。 从这些控制流图中提取基本块。 基本块由编译器实用程序转换为数据流图。 从两个或更多数据流图，确定最大的公共子图。 最大的公共子图是ASAP预定的，并被替换为也被安排的数据流图。 包含预定最大公共子图的单独数据流图被转换为数据路径，然后将其组合以形成用于操作应用程序的代码。 最大的共同子图是在开发数据流图的应用程序部分之间共享的硬件实现的。 总体代码的调度受到排序的影响，提供最快的运行时间，并且代码通过在处理元件上分配和放置芯片以及为设计元素设计的连接结构来实现。
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公开(公告)号：US06980716B1
公开(公告)日：2005-12-27
申请号：US10112006
申请日：2002-03-29
申请人： Rodolfo E. Diaz , Ampere A. Tseng , Karl S. Booksh , Jose Menendez , Sethuraman Panchanathan , Michael Wagner
发明人： Rodolfo E. Diaz , Ampere A. Tseng , Karl S. Booksh , Jose Menendez , Sethuraman Panchanathan , Michael Wagner
IPC分类号： G01N21/45 , G01N21/956 , G02B6/122 , G02B6/42
CPC分类号： B82Y15/00 , B82Y20/00 , G01N21/453 , G01N21/956 , G02B6/1226
摘要： Methods and apparatus for gathering image information from nanostructures includes a composite waveguide of conductive nanoparticles in a dielectric medium. The waveguide is irradiated with preferably coherent blue light to form a slow surface wave. The evanescent wave that is the “tail” of the surface wave exists outside the waveguide contiguous to its surface. The nanostructures are located to encounter the evanescent wave. The slowing of the wave that occurs in the waveguide reduces the wave's speed and wavelength sufficiently such that nanostructures can be imaged. Upon encountering the evanescent wave, the nanostructures radiate. This radiation causes a backward scattering from the structures and a forward perturbation of the wavefront of the surface wave. From the scattering and perturbation information about the physical characteristics of the nanostructures sufficient to form an image is derived.
摘要翻译： 用于从纳米结构收集图像信息的方法和装置包括介电介质中的导电纳米颗粒的复合波导。 用优选的相干蓝光照射波导以形成缓慢的表面波。 作为表面波的“尾”的ev逝波存在于与其表面相邻的波导的外侧。 纳米结构位于遇到ev逝波。 在波导中发生的波的减慢使得波的速度和波长充分地降低，使得可以对纳米结构进行成像。 当遇到ev逝波时，纳米结构辐射。 这种辐射导致结构的反向散射和表面波的波前的向前扰动。 从散射和扰动信息得到关于形成图像的纳米结构的物理特征。
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公开(公告)号：US20120310864A1
公开(公告)日：2012-12-06
申请号：US13484696
申请日：2012-05-31
IPC分类号： G06F15/18
CPC分类号： G06K9/6262 , G06N20/00
摘要： This disclosure includes various embodiments of apparatuses, systems, and methods for adaptive batch mode active learning for evolving a classifier. A corpus of unlabeled data elements to be classified is received, a batch size is determined based on a score function, a batch of unlabeled data elements having the determined batch size is selected from the corpus and labeled using a labeling agent or oracle, a classifier is retrained with the labeled data elements, these steps are repeated until a stop criterion has been met, for example, the classifier obtains a desired performance on unlabeled data elements in the corpus. The batch size determination and selection of a batch unlabeled data elements may be based on a single score function. The data elements may be video, image, audio, web text, and/or other data elements.
摘要翻译： 本公开包括用于进化分类器的自适应批模式主动学习的装置，系统和方法的各种实施例。 收到要分类的未标记数据元素的语料库，基于分数函数确定批量大小，从语料库中选择一批具有确定的批量大小的未标记的数据元素，并使用标签代理或分类器 用标记的数据元素重新训练，重复这些步骤，直到满足停止标准为止，例如，分类器在语料库中的未标记的数据元素上获得期望的性能。 批量未标记数据元素的批量大小确定和选择可以基于单分数函数。 数据元素可以是视频，图像，音频，web文本和/或其他数据元素。
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公开(公告)号：US07123783B2
公开(公告)日：2006-10-17
申请号：US10349371
申请日：2003-01-21
CPC分类号： G06K9/00268
摘要： An image classification system uses curvature-based multi-scale morphology to classify an image by its most distinguishing features. The image is recorded in digital form. Curvature features associated with the image are determined. A structuring element is modulated based on the curvature features. The shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. The structuring element modulated with the curvature features is superimposed on the image to determine a feature vector of the image using mathematical morphology. When this Curvature-based Multi-scale Morphology (CMM) technique is applied to face images, a high-dimensional feature vector is obtained. The dimensionality of this feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance.
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公开(公告)号：US06901110B1
公开(公告)日：2005-05-31
申请号：US09522882
申请日：2000-03-10
CPC分类号： H04N5/145 , G06T7/207 , G06T7/223 , G06T7/269 , G06T2207/10016 , H04N19/162 , H04N19/17 , H04N19/51 , H04N19/55
摘要： A method for tracking one or multiple objects from an input video sequence allows a user to select one or more regions that contain the object(s) of interest in the first and the last frame of their choice. An initialization component selects the current and the search frame and divides the selected region into equal sized macroblocks. An edge detection component computes the gradient of the current frame for each macroblock and a threshold component decides then which of the macroblocks contain sufficient information for tracking the desired object. A motion estimation component computes for each macroblock in the current frame its position in the search frame. The motion estimation component utilizes a search component that executes a novel search algorithm to find the best match. The mean absolute difference between two macroblocks is used as the matching criterion. The motion estimation component returns the estimated displacement vector for each block. An output component collects the motion vectors of all the predicted blocks and calculates the new position of the object in the next frame.
摘要翻译： 用于从输入视频序列跟踪一个或多个对象的方法允许用户在其选择的第一和最后一帧中选择包含感兴趣对象的一个或多个区域。 初始化组件选择当前和搜索帧，并将所选择的区域划分成相等大小的宏块。 边缘检测组件计算每个宏块的当前帧的梯度，并且阈值分量确定哪个宏块包含用于跟踪所需对象的足够信息。 运动估计分量针对当前帧中的每个宏块计算其在搜索帧中的位置。 运动估计组件利用执行新颖搜索算法的搜索组件来找到最佳匹配。 使用两个宏块之间的平均绝对差作为匹配标准。 运动估计分量返回每个块的估计位移矢量。 输出组件收集所有预测块的运动矢量，并计算下一帧中对象的新位置。
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