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公开(公告)号：US20090316986A1
公开(公告)日：2009-12-24
申请号：US12109347
申请日：2008-04-25
申请人： Gang Hua , Paul Viola , David Liu
发明人： Gang Hua , Paul Viola , David Liu
CPC分类号： G06K9/4647 , G06K9/468 , G06K9/6228
摘要： Image feature selection and extraction (e.g., for image classifier training) is accomplished in an integrated manner, such that higher-order features are merely developed from first-order features selected for image classification. That is, first-order image features are selected for image classification from an image feature pool, initially populated with pre-extracted first-order image features. The selected first-order classifying features are paired with previously selected first-order classifying features to generate higher-order features. The higher-order features are placed into the image feature pool as they are developed or “on-the-fly” (e.g., for use in image classifier training).
摘要翻译： 图像特征选择和提取（例如，用于图像分类器训练）以集成的方式实现，使得仅从为图像分类选择的一阶特征开发高阶特征。 也就是说，从图像特征池中选择用于图像分类的一阶图像特征，最初用预提取的一阶图像特征填充。 所选择的一阶分类特征与先前选择的一阶分类特征配对以产生更高阶的特征。 更高阶的特征被放置在图像特征池中，因为它们被开发或“即时”（例如，用于图像分类器训练）。
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公开(公告)号：US08244044B2
公开(公告)日：2012-08-14
申请号：US12109347
申请日：2008-04-25
申请人： Gang Hua , Paul Viola , David Liu
发明人： Gang Hua , Paul Viola , David Liu
CPC分类号： G06K9/4647 , G06K9/468 , G06K9/6228
摘要： Image feature selection and extraction (e.g., for image classifier training) is accomplished in an integrated manner, such that higher-order features are merely developed from first-order features selected for image classification. That is, first-order image features are selected for image classification from an image feature pool, initially populated with pre-extracted first-order image features. The selected first-order classifying features are paired with previously selected first-order classifying features to generate higher-order features. The higher-order features are placed into the image feature pool as they are developed or “on-the-fly” (e.g., for use in image classifier training).
摘要翻译： 图像特征选择和提取（例如，用于图像分类器训练）以集成的方式实现，使得仅从为图像分类选择的一阶特征开发高阶特征。 也就是说，从图像特征池中选择用于图像分类的一阶图像特征，最初用预提取的一阶图像特征填充。 所选择的一阶分类特征与先前选择的一阶分类特征配对以产生更高阶的特征。 更高阶的特征被放置在图像特征池中，因为它们被开发或“即时”（例如，用于图像分类器训练）。
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公开(公告)号：US20120141020A1
公开(公告)日：2012-06-07
申请号：US13371719
申请日：2012-02-13
申请人： Gang Hua , Paul Viola
发明人： Gang Hua , Paul Viola
IPC分类号： 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。
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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
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。
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公开(公告)号：US08891861B2
公开(公告)日：2014-11-18
申请号：US13371719
申请日：2012-02-13
申请人： Gang Hua , Paul Viola
发明人： Gang Hua , Paul Viola
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。
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公开(公告)号：US08131066B2
公开(公告)日：2012-03-06
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： G06K9/62
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。
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公开(公告)号：US07822696B2
公开(公告)日：2010-10-26
申请号：US11777471
申请日：2007-07-13
申请人： Cha Zhang , Paul Viola
发明人： Cha Zhang , Paul Viola
CPC分类号： G06K9/6257 , G06K9/00248
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。
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公开(公告)号：US07619513B2
公开(公告)日：2009-11-17
申请号：US11272095
申请日：2005-11-14
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.
摘要翻译： 提供了一种用于监视物体的移动的装置。 第一模块被配置为固定到对象。 能够电连接到第一模块的第二模块至少包括可充电电池和能够存储运动数据历史的存储器。 能够与第二模块电连接的第三模块包括能够连接到远程站的数据调制解调器和电池充电器。 当第二模块连接到第一模块时，存储器周期性地记录表示在记录时设备的位置的可用位置数据。 当第二个模块连接到第三个模块时，内存通过数据调制解调器下载，电池充电器为电池充电。
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9.发明申请公开(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。
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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。
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