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1. 发明授权

US08116548B2 Method and system for detecting 3D anatomical structures using constrained marginal space learning 有权
标题翻译：使用约束边际空间学习检测3D解剖结构的方法和系统
公开(公告)号：US08116548B2
公开(公告)日：2012-02-14
申请号：US12471761
申请日：2009-05-26
申请人： Yefeng Zheng , Bogdan Georgescu , Haibin Ling , Michael Scheuering , Dorin Comaniciu
发明人： Yefeng Zheng , Bogdan Georgescu , Haibin Ling , Michael Scheuering , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/3233 , G06K2209/051 , G06T7/75 , G06T2207/10081 , G06T2207/30004
摘要： A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume.
摘要翻译：公开了一种使用受限边际空间学习（MSL）检测医学图像中3D解剖学对象的方法和装置。基于训练数据确定输入医学图像体积的约束搜索范围。第一训练分类器用于检测约束搜索范围内的位置候选。使用训练数据中的取向示例从位置候选者生成位置取向假设。第二训练分类器用于从位置定向假设检测位置方向候选。基于训练数据中的比例示例，从位置定位候选生成相似度转换假设。第三训练分类器用于从相似变换假设检测相似变换候选，并且相似变换候选定义医学图像体积中的3D解剖对象的位置，平移和比例。

2. 发明申请

US20090304251A1 Method and System for Detecting 3D Anatomical Structures Using Constrained Marginal Space Learning 有权
标题翻译：使用约束边际空间学习检测3D解剖结构的方法和系统
公开(公告)号：US20090304251A1
公开(公告)日：2009-12-10
申请号：US12471761
申请日：2009-05-26
申请人： Yefeng Zheng , Bogdan Georgescu , Haibin Ling , Michael Scheuering , Dorin Comaniciu
发明人： Yefeng Zheng , Bogdan Georgescu , Haibin Ling , Michael Scheuering , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/3233 , G06K2209/051 , G06T7/75 , G06T2207/10081 , G06T2207/30004
摘要： A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume.
摘要翻译：公开了一种使用受限边际空间学习（MSL）检测医学图像中3D解剖学对象的方法和装置。基于训练数据确定输入医学图像体积的约束搜索范围。第一训练分类器用于检测约束搜索范围内的位置候选。使用训练数据中的取向示例从位置候选者生成位置取向假设。第二训练分类器用于从位置定向假设检测位置方向候选。基于训练数据中的比例示例，从位置定位候选生成相似度转换假设。第三训练分类器用于从相似变换假设检测相似变换候选，并且相似变换候选定义医学图像体积中的3D解剖对象的位置，平移和比例。

3. 发明申请

US20070110306A1 Diffusion distance for histogram comparison 失效
标题翻译：扩散距离用于直方图比较
公开(公告)号：US20070110306A1
公开(公告)日：2007-05-17
申请号：US11598538
申请日：2006-11-13
申请人： Haibin Ling , Kazunori Okada
发明人： Haibin Ling , Kazunori Okada
IPC分类号： G06K9/00
CPC分类号： G06K9/6212 , G06K9/4671
摘要： A new measure to compare histogram-based descriptors, a diffusion distance, is disclosed. The difference between two histograms is defined to be a temperature field. The relationship between histogram similarity and diffusion process is discussed and it is shown how the diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher The proposed approach is tested on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurate as the Earth Mover's Distance with a much greater efficiency.
摘要翻译：公开了一种比较基于直方图的描述符（扩散距离）的新措施。两个直方图之间的差异被定义为温度场。讨论了直方图相似性和扩散过程之间的关系，并展示了扩散如何处理变形以及量化效应。结果，扩散距离被导出为与尺度之间的不相似之和。作为一个跨仓直方图的距离，扩散距离对基于直方图的局部描述符中的变形，照明变化和噪声是鲁棒的。此外，它具有线性计算复杂度，其显着地改善了先前提出的二进制复杂度或更高的交叉仓距。使用几种基于多维直方图的描述符（包括形状上下文，SIFT）来对形状识别和兴趣点匹配任务进行测试。和旋转图像。在所有实验中，与其他最先进的距离测量相比，扩散距离在精度和效率方面都表现出色。特别地，它以更大的效率执行与地球移动器的距离一样准确。

4. 发明授权

US07715623B2 Diffusion distance for histogram comparison 失效
标题翻译：扩散距离用于直方图比较
公开(公告)号：US07715623B2
公开(公告)日：2010-05-11
申请号：US11598538
申请日：2006-11-13
申请人： Haibin Ling , Kazunori Okada
发明人： Haibin Ling , Kazunori Okada
IPC分类号： G06K9/00 , G06K9/38 , G06K9/46
CPC分类号： G06K9/6212 , G06K9/4671
摘要： A new measure to compare histogram-based descriptors, a diffusion distance, is disclosed. The difference between two histograms is defined to be a temperature field. The relationship between histogram similarity and diffusion process is discussed and it is shown how the diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher The proposed approach is tested on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurate as the Earth Mover's Distance with a much greater efficiency.
摘要翻译：公开了一种比较基于直方图的描述符（扩散距离）的新措施。两个直方图之间的差异被定义为温度场。讨论了直方图相似性和扩散过程之间的关系，并展示了扩散如何处理变形以及量化效应。结果，扩散距离被导出为与尺度之间的不相似之和。作为交叉点直方图的距离，扩散距离对基于直方图的局部描述符中的变形，照明变化和噪声是鲁棒的。此外，它具有线性计算复杂度，其显着地改善了先前提出的二进制复杂度或更高的交叉仓距。使用几种基于多维直方图的描述符（包括形状上下文，SIFT）来对形状识别和兴趣点匹配任务进行测试。和旋转图像。在所有实验中，与其他最先进的距离测量相比，扩散距离在精度和效率方面都表现出色。特别地，它以更大的效率执行与地球移动器的距离一样准确。

5. 发明授权

US07298903B2 Method and system for separating text and drawings in digital ink 有权
标题翻译：数字墨水分离文字和图纸的方法和系统
公开(公告)号：US07298903B2
公开(公告)日：2007-11-20
申请号：US09895429
申请日：2001-06-28
申请人： Jian Wang , Haibin Ling , Siwei Lyu , Yu Zou
发明人： Jian Wang , Haibin Ling , Siwei Lyu , Yu Zou
IPC分类号： G06K9/18 , G06K9/00
CPC分类号： G06K9/00456
摘要： A system for separating text and drawings in a digital ink file (e.g., a handwritten digital ink file). A stroke analyzer classifies single strokes that have been input by a user as “text” or “unknown.” The stroke analyzer utilizes a trainable classifier, such as a support vector machine. A grouping component is provided that groups text strokes in an attempt to form text objects (e.g., words, characters, or letters). The grouping component also groups unknown strokes in an attempt to form objects (e.g., shapes, drawings, or even text). A trainable classifier, such as a support vector machine, evaluates the grouped strokes to determine if they are objects.
摘要翻译：用于分离数字墨水文件（例如，手写数字墨水文件）中的文本和图纸的系统。笔划分析器将用户输入的单笔划分为“文字”或“未知”。笔划分析仪使用可分类的分类器，例如支持向量机。提供了分组组件，其组合文本笔画以试图形成文本对象（例如，单词，字符或字母）。分组组件还组合未知笔画以试图形成对象（例如，形状，图形，甚至文本）。可训练的分类器，例如支持向量机，评估分组的笔画以确定它们是否是对象。

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