公开(公告)号：US20090252413A1

公开(公告)日：2009-10-08

申请号：US12098026

申请日：2008-04-04

申请人： Gang Hua ,  Paul Viola

发明人： Gang Hua ,  Paul Viola

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06F17/3025 ,  G06F17/30262 ,  G06K9/00664 ,  G06K9/4642 ,  G06K9/4652 ,  G06K9/6256 ,  G06K9/6285

摘要： Images are classified as photos (e.g., natural photographs) or graphics (e.g., cartoons, synthetically generated images), such that when searched (online) with a filter, an image database returns images corresponding to the filter criteria (e.g., either photos or graphics will be returned). A set of image statistics pertaining to various visual cues (e.g., color, texture, shape) are identified in classifying the images. These image statistics, combined with pre-tagged image metadata defining an image as either a graphic or a photo, may be used to train a boosting decision tree. The trained boosting decision tree may be used to classify additional images as graphics or photos based on image statistics determined for the additional images.

摘要翻译： 图像被分类为照片（例如，自然照片）或图形（例如，漫画，综合生成的图像）​​，使得当用过滤器搜索（在线）时，图像数据库返回与过滤标准相对应的图像（例如，照片或 图形将被返回）。 在对图像进行分类时，识别关于各种视觉提示（例如，颜色，纹理，形状）的一组图像统计信息。 这些图像统计信息与将图像定义为图形或照片的预先标记的图像元数据可以用于训练增强决策树。 经训练的增强决策树可以用于基于为附加图像确定的图像统计来将附加图像分类为图形或照片。

公开(公告)号：US20090018980A1

公开(公告)日：2009-01-15

申请号：US11777464

申请日：2007-07-13

申请人： Cha Zhang ,  Paul Viola

发明人： Cha Zhang ,  Paul Viola

IPC分类号： G06F15/18

CPC分类号： G06K9/6256 ,  G06K9/00288 ,  G06K9/6282

摘要： A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier.

摘要翻译： “分类器训练器”训练用于检测信号中的特定对象的组合分类器（例如，图像中的面部，语音中的词，信号中的模式等）。 在一个实施例中，引入了用于训练组合分类器的弱分类器或“特征”的“多实例修剪”（MIP）。 具体来说，将训练有素的组合分类器和用于设置假正/负操作点的相关联的最终阈值与学习的中间拒绝阈值组合以构建组合分类器。 使用修剪过程学习拒绝阈值，确保由组合分类器检测到原始组合分类器检测到的对象，从而保证修剪后训练集上的相同检测率。 训练所需的唯一参数是最终级联系统的目标检测率。 在另外的实施例中，组合分类器使用称为“胖树桩”分类器的重量修剪，自举和弱分类器的各种组合进行训练。

公开(公告)号：US20080187213A1

公开(公告)日：2008-08-07

申请号：US11671760

申请日：2007-02-06

申请人： Cha Zhang ,  Paul Viola ,  Sang Min Oh

发明人： Cha Zhang ,  Paul Viola ,  Sang Min Oh

IPC分类号： G06K9/62

CPC分类号： G06K9/00281

摘要： A landmark detection technique that can quickly detect both objects of interest and landmarks within the objects in an image using regression methods. The present fast landmark detection scheme reuses existing feature values used for object detection (e.g., face detection) to find the landmarks in an object (e.g., the eyes and mouth of the face). Hence, the technique provides landmark detection functionality at almost no cost.

摘要翻译： 一种地标检测技术，可以使用回归方法快速检测图像中对象内的感兴趣对象和地标。 现在的快速地标检测方案重用用于对象检测（例如，面部检测）的现有特征值以找到对象（例如，脸部的眼睛和嘴巴）中的界标。 因此，该技术几乎没有成本地提供地标检测功能。

公开(公告)号：US20060109110A1

公开(公告)日：2006-05-25

申请号：US11272095

申请日：2005-11-14

申请人： Maurice Hill ,  Michael Mocenter ,  Joseph Reiter ,  Paul Viola ,  Brian Moran

发明人： Maurice Hill ,  Michael Mocenter ,  Joseph Reiter ,  Paul Viola ,  Brian Moran

IPC分类号： G08B1/08

CPC分类号： G07C9/00111

摘要： A device for monitoring movement of an object is provided. A first module is configured to secure to the object. A second module, capable of electrically connecting to the first module, includes at least a rechargeable battery and a memory capable of storing a history of movement data. A third module, capable of electrically connecting with the second module, includes a data modem capable of connecting to a remote station, and a battery charger. When the second module is connected to the first module, the memory periodically records available location data representing a position of the device at the time of recording. When the second module is connected to the third module, the memory downloads through the data modem and the battery charger charges the battery.

公开(公告)号：US08010471B2

公开(公告)日：2011-08-30

申请号：US11777464

申请日：2007-07-13

申请人： Cha Zhang ,  Paul Viola

发明人： Cha Zhang ,  Paul Viola

IPC分类号： G06F17/00 ,  G06N5/00

CPC分类号： G06K9/6256 ,  G06K9/00288 ,  G06K9/6282

摘要： A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a “fat stump” classifier.

摘要翻译： “分类器训练器”训练用于检测信号中的特定对象的组合分类器（例如，图像中的面部，语音中的词，信号中的模式等）。 在一个实施例中，引入了用于训练组合分类器的弱分类器或“特征”的“多实例修剪”（MIP）。 具体来说，将训练有素的组合分类器和用于设置假正/负操作点的相关联的最终阈值与学习的中间拒绝阈值组合以构建组合分类器。 使用修剪过程学习拒绝阈值，确保由组合分类器检测到原始组合分类器检测到的对象，从而保证修剪后训练集上相同的检测率。 训练所需的唯一参数是最终级联系统的目标检测率。 在另外的实施例中，组合分类器使用称为“胖树桩”分类器的重量修剪，自举和弱分类器的各种组合进行训练。

公开(公告)号：US07099510B2

公开(公告)日：2006-08-29

申请号：US09992795

申请日：2001-11-12

申请人： Michael J. Jones ,  Paul Viola

发明人： Michael J. Jones ,  Paul Viola

IPC分类号： G06K9/62

CPC分类号： G06K9/00248 ,  G06K9/4642 ,  G06K9/6257

摘要： An object detection system for detecting instances of an object in a digital image includes an image integrator and an object detector, which includes a classifier (classification function) and image scanner. The image integrator receives an input image and calculates an integral image representation of the input image. The image scanner scans the image in same sized subwindows. The object detector uses a cascade of homogenous classification functions or classifiers to classify the subwindows as to whether each subwindow is likely to contain an instance of the object. Each classifier evaluates one or more features of the object to determine the presence of such features in a subwindow that would indicate the likelihood of an instance of the object in the subwindow.

公开(公告)号：US20060085466A1

公开(公告)日：2006-04-20

申请号：US10981474

申请日：2004-11-05

申请人： Ming Ye ,  Paul Viola

发明人： Ming Ye ,  Paul Viola

IPC分类号： G06F17/30

CPC分类号： G06F17/242 ,  G06F17/2705 ,  Y10S707/99934 ,  Y10S707/99942

摘要： A system and method for determining hierarchical information is described. Aspects include using the Collins model for parsing non-textual information into hierarchical content. The system and process assign labels to lines that indicate how the lines relate to one another.

摘要翻译： 描述了一种用于确定层次信息的系统和方法。 方面包括使用Collins模型将非文本信息解析为分层内容。 系统和过程将标签分配给指示线如何相互关联的线。

公开(公告)号：US06992582B2

公开(公告)日：2006-01-31

申请号：US10677272

申请日：2003-10-03

申请人： Maurice L. Hill ,  Michael Mocenter ,  Joeseph S. Reiter ,  Paul Viola ,  Brian Moran

发明人： Maurice L. Hill ,  Michael Mocenter ,  Joeseph S. Reiter ,  Paul Viola ,  Brian Moran

IPC分类号： G08B1/08

CPC分类号： G07C9/00111

摘要： A device for monitoring movement of an object is provided. A first module is configured to secure to the object. A second module, capable of electrically connecting to the first module, includes at least a rechargeable battery and a memory capable of storing a history of movement data. A third module, capable of electrically connecting with the second module, includes a data modem capable of connecting to a remote station, and a battery charger. When the second module is connected to the first module, the memory periodically records available location data representing a position of the device at the time of recording. When the second module is connected to the third module, the memory downloads through the data modem and the battery charger charges the battery.

公开(公告)号：US20050259866A1

公开(公告)日：2005-11-24

申请号：US10850335

申请日：2004-05-20

申请人： Charles Jacobs ,  James Rinker ,  Patrice Simard ,  Paul Viola

发明人： Charles Jacobs ,  James Rinker ,  Patrice Simard ,  Paul Viola

IPC分类号： G06K9/20 ,  G06K9/34 ,  G06K9/46 ,  G06K9/66 ,  G06K9/72 ,  G06N3/00 ,  G06K9/62 ,  G06K9/18

CPC分类号： G06K9/344 ,  G06K9/00463 ,  G06K9/4614 ,  G06K9/4628 ,  G06K9/723 ,  G06K2209/01

摘要： A global optimization framework for optical character recognition (OCR) of low-resolution photographed documents that combines a binarization-type process, segmentation, and recognition into a single process. The framework includes a machine learning approach trained on a large amount of data. A convolutional neural network can be employed to compute a classification function at multiple positions and take grey-level input which eliminates binarization. The framework utilizes preprocessing, layout analysis, character recognition, and word recognition to output high recognition rates. The framework also employs dynamic programming and language models to arrive at the desired output.

摘要翻译： 低分辨率拍摄文档的光学字符识别（OCR）的全局优化框架，将二值化类型过程，分割和识别结合到一个过程中。 该框架包括对大量数据进行培训的机器学习方法。 可以采用卷积神经网络来计算多个位置的分类函数，并采用消除二值化的灰度级输入。 该框架利用预处理，布局分析，字符识别和字识别来输出高识别率。 该框架还采用动态编程和语言模型来达到所需的输出。

公开(公告)号：US20120042337A1

公开(公告)日：2012-02-16

申请号：US12211777

申请日：2008-09-16

申请人： Jeremy S. De Bonet ,  Paul Viola

发明人： Jeremy S. De Bonet ,  Paul Viola

IPC分类号： H04N7/025 ,  H04N7/173

CPC分类号： H04L65/4069 ,  G06F17/30029 ,  G06F17/30749 ,  G06F17/30761 ,  G06F17/30766 ,  G06F17/30772 ,  G06F17/30828 ,  G06F17/30867 ,  G06Q30/02 ,  G06Q30/0251 ,  G06Q30/0255 ,  G06Q30/0269 ,  G06Q30/0271 ,  H04L12/18 ,  H04L29/06 ,  H04L65/00 ,  H04L65/4076 ,  H04L65/60 ,  H04L67/306 ,  H04N7/165 ,  H04N21/25891 ,  H04N21/2668 ,  H04N21/4667 ,  H04N21/8106 ,  H04N21/812

摘要： A personal broadcast server system provides a customized broadcast to one or more users over a transmission media. A data storage device stores a plurality of broadcast elements. A data management system stores a user profile and a user state for each of the one or more users and also stores information associated with each of the plurality of broadcast elements. A broadcast element selector, having at least one broadcast element selector function, selects broadcast elements from the data storage device based on information contained in the data management system. A broadcast server receives the selected broadcast elements from the data storage device and provides the selected broadcast elements to a user over the transmission media. The personal broadcast server system may provide streaming audio, streaming video, or other forms of broadcast signals.

摘要翻译： 个人广播服务器系统通过传输媒体向一个或多个用户提供定制的广播。 数据存储装置存储多个广播元件。 数据管理系统存储一个或多个用户中的每一个的用户简档和用户状态，并且还存储与多个广播元素中的每一个相关联的信息。 具有至少一个广播元素选择器功能的广播元素选择器基于数据管理系统中包含的信息从数据存储设备中选择广播元素。 广播服务器从数据存储设备接收所选择的广播元素，并通过传输媒体将所选择的广播元素提供给用户。 个人广播服务器系统可以提供流式音频，流式视频或其他形式的广播信号。