公开(公告)号：US07142718B2

公开(公告)日：2006-11-28

申请号：US10283380

申请日：2002-10-28

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Donglok Kim

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Donglok Kim

IPC分类号： G06K9/62 ,  G06K9/54

CPC分类号： G06K9/4647 ,  G06K9/6203 ,  G06K9/6857

摘要： An accumulation transformation method for fast pattern search accurately locates general patterns of interest. The method can be used for fast invariant search to match patterns of interest in images where the searched pattern varies in size or orientation or aspect ratio, when pattern appearance is degraded, when the pattern is partially occluded, where the searched image is large, multidimensional, or very high resolution, or where the pattern size is large. The accumulation transformations of the input image are determined based upon the searched projection directions. Projection profile result images are derived from the accumulation transformed input image and used for fast matching with template pattern projection profiles.

摘要翻译： 用于快速图案搜索的积累变换方法准确地定位了感兴趣的一般模式。 该方法可用于快速不变搜索，以匹配图像中所关注的图案，其中搜索的图案在尺寸或取向或纵横比上变化，当图案外观劣化时，当图案被部分遮挡时，搜索图像大，多维度 ，或非常高的分辨率，或者图案尺寸大的地方。 基于搜索到的投影方向来确定输入图像的累积变换。 投影轮廓结果图像从积累变换的输入图像中导出，用于与模板图案投影轮廓的快速匹配。

公开(公告)号：US07096207B2

公开(公告)日：2006-08-22

申请号：US10104647

申请日：2002-03-22

申请人： Donglok Kim ,  Shih-Jong J. Lee ,  Seho Oh

发明人： Donglok Kim ,  Shih-Jong J. Lee ,  Seho Oh

IPC分类号： G06N5/00

CPC分类号： G06K9/6255

摘要： A learning acceleration method is disclosed that can be applied to multiple types and stages of learning to enhance the learning efficiency and outcome. Artificially created training samples can improve representation of all classes in the training set, decrease the difficulty of obtaining sufficient training samples, and decrease the difficulty of unequal sample prevalence. Two specific embodiments of learning acceleration are disclosed: learning accelerated algorithm training and learning accelerated start-up learning. Three objects of interest implantation methods are disclosed: texture mapping of defects, parametric synthesis of negative samples, and manual image editing.

公开(公告)号：US07233931B2

公开(公告)日：2007-06-19

申请号：US10746169

申请日：2003-12-26

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Donglok Kim

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Donglok Kim

IPC分类号： G06F15/18 ,  G06N3/08 ,  G06K9/64

CPC分类号： G06K9/6282 ,  G06K9/6228 ,  G06N5/003 ,  G06N99/005

摘要： A feature regulation application method for hierarchical decision learning systems receives feature regulation training data and invokes a plurality of hierarchical decision learning to create feature subset information output. The method receives learning data and uses the feature subset information and the learning data to create feature subset learning data output. The hierarchical decision learning method uses the feature subset learning data to create hierarchical decision output. The feature regulation method also outputs feature ranking information that can be used to create hierarchical decision output. The invention provides a computationally feasible method for feature selection that considers the hierarchical decision learning systems used for decision making.

摘要翻译： 用于分层决策学习系统的特征调节应用方法接收特征调节训练数据，并且调用多个分层决策学习来创建特征子集信息输出。 该方法接收学习数据，并使用特征子集信息和学习数据来创建特征子集学习数据输出。 层次决策学习方法使用特征子集学习数据来创建分层决策输出。 特征调节方法还输出可用于创建分层决策输出的特征排序信息。 本发明提供了一种考虑用于决策的层次决策学习系统的特征选择的计算可行方法。

公开(公告)号：US07054492B2

公开(公告)日：2006-05-30

申请号：US10255016

申请日：2002-09-24

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Donglok Kim

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Donglok Kim

IPC分类号： G06K9/62

CPC分类号： G06K9/6203 ,  G06K9/4604

摘要： An accumulation method for fast pattern search can accurately locate regular shaped patterns of interest. The method can be used for invariant search to match patterns of interest in images where the searched pattern varies in size or orientation or aspect ratio, when pattern appearance is degraded, and even when the pattern is partially occluded, where the searched image is large, multidimensional, or very high resolution, or where the pattern size is large. The computation requirement is independent of the size of the pattern region.

摘要翻译： 用于快速图案搜索的累积方法可以准确地定位感兴趣的规则形状图案。 该方法可以用于不变搜索以匹配图像中所关注的图案，其中搜索的图案在尺寸或取向或纵横比上变化，当图案外观劣化时，甚至当图案被部分遮挡时，搜索图像大， 多维或非常高的分辨率，或者图案尺寸大的地方。 计算要求与图案区域的大小无关。

公开(公告)号：US07263509B2

公开(公告)日：2007-08-28

申请号：US10411437

申请日：2003-04-09

申请人： Shih-Jong J. Lee ,  Seho Oh

发明人： Shih-Jong J. Lee ,  Seho Oh

IPC分类号： G06F12/00 ,  G06N5/02

CPC分类号： G06N5/04 ,  G06K9/342 ,  G06K9/4638 ,  G06N5/025 ,  G06N99/005

摘要： An intelligent spatial reasoning method receives a plurality of object sets. A spatial mapping feature learning method uses the plurality of object sets to create at least one salient spatial mapping feature output. It performs spatial reasoning rule learning using the at least one spatial mapping feature to create at least one spatial reasoning rule output. The spatial mapping feature learning method performs a spatial mapping feature set generation step followed by a feature learning step. The spatial mapping feature set is generated by repeated application of spatial correlation between two object sets. The feature learning method consists of a feature selection step and a feature transformation step and the spatial reasoning rule learning method uses the supervised learning method.The spatial reasoning approach of this invention automatically characterizes spatial relations of multiple sets of objects by comprehensive collections of spatial mapping features. Some of the features have clearly understandable physical, structural, or geometrical meanings. Others are statistical characterizations, which may not have clear physical, structural or geometrical meanings when considered individually. A combination of these features, however, could characterize subtle physical, structural or geometrical conditions under practical situations. One key advantage of this invention is the ability to characterize subtle differences numerically using a comprehensive feature set.

摘要翻译： 智能空间推理方法接收多个对象集。 空间映射特征学习方法使用多个对象集来创建至少一个显着的空间映射特征输出。 它使用至少一个空间映射特征来执行空间推理规则学习以创建至少一个空间推理规则输出。 空间映射特征学习方法执行空间映射特征集生成步骤，随后是特征学习步骤。 通过重复应用两个对象集之间的空间相关性来生成空间映射特征集。 特征学习方法由特征选择步骤和特征变换步骤组成，空间推理规则学习方法采用监督学习方法。 本发明的空间推理方法通过空间映射特征的综合集合自动表征多组对象的空间关系。 一些功能具有明确的理解，物理，结构或几何意义。 其他是统计特征，当单独考虑时可能没有明确的物理，结构或几何意义。 然而，这些特征的组合可以在实际情况下表征微妙的物理，结构或几何条件。 本发明的一个关键优点是能够使用综合特征集在数值上表征微妙的差异。

公开(公告)号：US07076093B2

公开(公告)日：2006-07-11

申请号：US10247723

申请日：2002-09-16

申请人： Shih-Jong J. Lee ,  Seho Oh

发明人： Shih-Jong J. Lee ,  Seho Oh

IPC分类号： G06K9/00

CPC分类号： G06T7/001 ,  G06T7/12 ,  G06T7/155 ,  G06T2207/20041

摘要： A structure-guided transformation transforms a region of an image into a region in the structure-transformed image according to the desired structure. The invention achieves efficient and accurate structure-guided processing such as filtering, detection and comparison in the transformed domain and thereby facilitates use of simple operations to enhance or detect straight lines or edges. Structure information is used to enhance and detect image features of interest even when the shape of the image structure is not regular. Both global and local structures of objects can be inspected. Global structure inspection detects gross errors in image structure; therefore side effects caused by mismatched structure-guided processing are avoided. Subtle defects along the edge of a structure can be detected by local structure inspection. Structure information guidance provides an edge detection inspection system that tolerates significant noise and contrast variations.

摘要翻译： 结构引导变换根据期望的结构将图像的区域变换为结构变换图像中的区域。 本发明实现了有效和准确的结构引导处理，如变换域中的过滤，检测和比较，从而便于使用简单的操作来增强或检测直线或边缘。 即使当图像结构的形状不规则时，结构信息也用于增强和检测感兴趣的图像特征。 可以检查对象的全局和局部结构。 全局结构检查检测图像结构中的粗略错误; 因此避免了由错配的结构引导处理引起的副作用。 沿结构边缘的微小缺陷可以通过局部结构检查来检测。 结构信息指导提供了边缘检测检测系统，其容忍显着的噪声和对比度变化。

公开(公告)号：US06842538B2

公开(公告)日：2005-01-11

申请号：US09815816

申请日：2001-03-23

申请人： Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson

发明人： Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson

IPC分类号： G06K9/46 ,  G06K9/00 ,  G06K9/36 ,  G06K9/62 ,  H04N1/00

CPC分类号： G06K9/4604

摘要： Mark detection and position determination are improved by use of directional elongated filters, symmetry, gray scale image processing, structural constraints, and learning. Directional elongated filters are used to pre-process images of registration marks to create masks and enhanced images. Working sequentially, portions of the mark are detected and classified. The input gray scale image of the mark is processed using its structural constraints in conjunction with a mask for the detected mark. A cost function estimation determines mark position and orientation with sub-pixel accuracy. Learning is used to improve specific application performance.

摘要翻译： 通过使用定向细长滤波器，对称性，灰度图像处理，结构约束和学习来提高标记检测和位置确定。 定向拉长过滤器用于预处理对准标记的图像以创建掩模和增强图像。 按顺序工作，对标记的部分进行检测和分类。 使用其结构约束结合用于检测标记的掩码来处理标记的输入灰度图像。 成本函数估计用子像素精度确定标记位置和方向。 学习用于提高特定的应用程序性能。

公开(公告)号：US06404934B1

公开(公告)日：2002-06-11

申请号：US09692948

申请日：2000-10-20

申请人： Shih-Jong J. Lee ,  Seho Oh

发明人： Shih-Jong J. Lee ,  Seho Oh

IPC分类号： G06T520

CPC分类号： G06T5/30

摘要： A high speed image processing apparatus is created through the use of cascaded elongated filters. The processing speed of the filters is kernel size insensitive, enabling use of general purpose computing facilities to process high resolution, monochrome, and multi-spectrum images. Elongated filters described include both linear and non-linear filters. Very large kernel and multi-dimensional image processing is accomplished with reduced complexity and portable programming instructions.

摘要翻译： 通过使用级联的细长过滤器创建高速图像处理装置。 过滤器的处理速度是内核大小不敏感的，可以使用通用计算机来处理高分辨率，单色和多光谱图像。 所描述的细长滤波器包括线性和非线性滤波器。 非常大的内核和多维图像处理通过降低的复杂性和便携式编程指令来实现。

公开(公告)号：US06181811B2

公开(公告)日：2001-01-30

申请号：US09006457

申请日：1998-01-13

申请人： Chih-Chau L. Kuan ,  Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson ,  Dayle G. Ellison ,  Florence W. Patten

发明人： Chih-Chau L. Kuan ,  Shih-Jong J. Lee ,  Seho Oh ,  Larry A. Nelson ,  Dayle G. Ellison ,  Florence W. Patten

IPC分类号： G06K900

CPC分类号： G06K9/00127

摘要： A method and apparatus for optimizing biological and cytological specimen screening and diagnosis. A slide review process is recommended for cytological specimen screening to identify abnormal sub-populations for further review and also diagnosis by a human expert. An automated screener processes a cytological specimen. Using a slide score generated by the automated screener, a slide review process using a slide score classification is determined. The recommendation of slide review processes improves overall performance of the screening process as measured by sensitivity to abnormal specimens, and at the same time reduces the work load of a human reviewer. The system also effectively and smoothly integrates the process of initial screening of the specimen with the process of further review of the specimen and final diagnosis of the specimen.

摘要翻译： 用于优化生物学和细胞学标本筛选和诊断的方法和装置。 推荐用于细胞学标本筛选的幻灯片审查过程，以识别异常亚群以供进一步审查，并由人类专家进行诊断。 自动筛选器处理细胞学标本。 使用由自动筛选器生成的幻灯片得分，确定使用幻灯片分数分类的幻灯片审阅过程。 幻灯片审查过程的推荐通过对异常标本的敏感度测量，提高了筛选过程的整体性能，同时降低了人类审阅者的工作量。 该系统还将样品初始筛选过程与样品进一步检查和样品的最终诊断过程进行了有效平滑的整合。

公开(公告)号：US6134354A

公开(公告)日：2000-10-17

申请号：US121012

申请日：1998-07-22

申请人： Shih-Jong J. Lee ,  Paul S. Wilhelm ,  Wendy R. Bannister ,  Chih-Chau L. Kuan ,  Seho Oh ,  Michael G. Meyer

发明人： Shih-Jong J. Lee ,  Paul S. Wilhelm ,  Wendy R. Bannister ,  Chih-Chau L. Kuan ,  Seho Oh ,  Michael G. Meyer

IPC分类号： G01N15/14 ,  G06K9/00 ,  G06K9/38

CPC分类号： G06K9/00127 ,  G01N15/1475 ,  G01N2015/1497

摘要： A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.