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1. 发明授权

US4849940A Optical neural net memory 失效
标题翻译：光学神经网络存储器
公开(公告)号：US4849940A
公开(公告)日：1989-07-18
申请号：US131012
申请日：1987-12-10
申请人： Robert J. Marks, II , Les E. Atlas , Seho Oh
发明人： Robert J. Marks, II , Les E. Atlas , Seho Oh
IPC分类号： G06N3/067 , G11C11/54 , G11C15/00
CPC分类号： G06N3/067 , G11C11/54 , G11C15/00
摘要： An optical neural net, content addressable memory that does not include electronics or slow optics in the feedback path. The memory comprises an optical vector-matrix multiplier that includes a spatial light modulator and input and output optics, and optical feedback means for returning the outputs of the multiplier to the inputs. Presentation of partial data at selected inputs causes the neural net to iterate at light speed to determine the corresponding output data. An arrangement is described for providing bipolar operations and for letting any group of elements act as the net stimulus.
摘要翻译：光学神经网络，内容可寻址存储器，其不包括反馈路径中的电子器件或慢光学器件。存储器包括光学矢量矩阵乘法器，其包括空间光调制器和输入和输出光学器件，以及光学反馈装置，用于将乘法器的输出返回到输入端。在所选择的输入处呈现部分数据使得神经网络以光速迭代以确定相应的输出数据。描述了一种用于提供双极性操作和让任何一组元件作为净刺激的装置。

2. 发明授权

US07263509B2 Intelligent spatial reasoning 有权
标题翻译：智能空间推理
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。

3. 发明授权

US07076093B2 Structure-guided image inspection 有权
标题翻译：结构导向图像检查
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。

4. 发明申请

US20060147105A1 Alignment template goodness qualification method 审中-公开
标题翻译：对齐模板善良鉴定方法
公开(公告)号：US20060147105A1
公开(公告)日：2006-07-06
申请号：US11035867
申请日：2005-01-05
申请人： Shih-Jong Lee , Yuhui Cheng , Seho Oh , Shinichi Nakajima , Yuji Kokumai
发明人： Shih-Jong Lee , Yuhui Cheng , Seho Oh , Shinichi Nakajima , Yuji Kokumai
IPC分类号： G06K9/00
CPC分类号： G06T7/0006 , G06K9/6204 , G06T7/33 , G06T7/74 , G06T2207/30148 , H05K3/0008
摘要： An alignment template goodness qualification method receives a pattern image and a pattern based alignment template and performs template goodness measurement using the pattern image and the pattern based alignment template to generate template goodness result output. A template qualification is performed using the template goodness result to generate template qualification result output. If the template qualification result is acceptable, the pattern based alignment template is outputted as the qualified pattern based alignment template. Otherwise, an alternative template selection is performed using the pattern image, the pattern based alignment template and the template goodness result to generate alternative pattern based alignment template output. The template goodness measurements include signal content measurement, spatial discrimination measurement and pattern ambiguity measurement.
摘要翻译：对齐模板品质鉴定方法接收图案图像和基于图案的对准模板，并使用图案图像和基于图案的对准模板进行模板优良度测量，以生成模板优良结果输出。使用模板优化结果执行模板限定，以生成模板验证结果输出。如果模板限定结果可以接受，则基于模式的对齐模板将作为合格模式对齐模板输出。否则，使用模式图像，基于模式的对准模板和模板优点结果来执行替代模板选择，以生成基于替代模式的对准模板输出。模板优点测量包括信号内容测量，空间辨别测量和模式模糊度测量。

5. 发明申请

US20060078192A1 Partition pattern match and integration method for alignment 有权
标题翻译：分区模式匹配和集成方法进行对齐
公开(公告)号：US20060078192A1
公开(公告)日：2006-04-13
申请号：US10961663
申请日：2004-10-08
申请人： Seho Oh , Shih-Jong Lee , Shinichi Nakajima , Yuji Kokumai
发明人： Seho Oh , Shih-Jong Lee , Shinichi Nakajima , Yuji Kokumai
IPC分类号： G06K9/00
CPC分类号： G06K9/3216 , G06T7/001 , G06T2207/30148
摘要： A partition pattern template generation method for alignment receives a learning image and performs partition template generation using the learning image to generate a plurality of partition template result output. A partition template acceptance test is performed using the plurality of partition template results to generate partition templates or failure result. A partition template search method for alignment receives an alignment image and partition templates and performs a plurality of template search steps to generate a plurality of matching scores output. A partition integration method is performed using the plurality of matching scores to generate a partition template search result. A partition integration error self checking method receives a preliminary template search result position and a plurality of the matching scores. A matching score profile comparison is performed using the plurality of the matching scores and the expected matching score profile to generate the template search result.
摘要翻译：用于对准的分割图案模板生成方法接收学习图像，并使用学习图像来执行分割模板生成，以生成多个分割模板结果输出。使用多个分区模板结果执行分区模板验收测试，以生成分区模板或故障结果。用于对准的分割模板搜索方法接收对准图像和分割模板，并且执行多个模板搜索步骤以生成多个匹配分数输出。使用多个匹配分数来执行分区集成方法以生成分区模板搜索结果。分区整合错误自检方法接收初步模板搜索结果位置和多个匹配分数。使用多个匹配分数和预期匹配分数分布来执行匹配分数分布比较，以生成模板搜索结果。

6. 发明授权

US06842538B2 Automatic detection of alignment or registration marks 有权
标题翻译：自动检测对准或对准标记
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。

7. 发明授权

US06404934B1 High speed image processing apparatus using a cascade of elongated filters programmed in a computer 有权
标题翻译：使用在计算机中编程的细长滤波器级联的高速图像处理装置
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。

8. 发明授权

US06181811B2 Method and apparatus for optimizing biological and cytological specimen screening and diagnosis 失效
标题翻译：用于优化生物学和细胞学标本筛选和诊断的方法和装置
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。

9. 发明授权

US6134354A Apparatus for the identification of free-lying cells 失效
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.

10. 发明申请

US20120106809A1 Teachable object contour mapping for biology image region partition 有权
标题翻译：用于生物图像区域划分的可对象轮廓映射
公开(公告)号：US20120106809A1
公开(公告)日：2012-05-03
申请号：US12925874
申请日：2010-11-01
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： G06K9/342 , G06K9/0014
摘要： A teachable object contour mapping method for region partition receives an object boundary and a teaching image. An object contour mapping recipe creation is performed using the object boundary and the teaching image to generate object contour mapping recipe output. An object contour mapping is applied to an application image using the object contour mapping recipe and the application image to generate object contour map output. An object region partition using the object contour map to generate object region partition output An updateable object contour mapping method receives a contour mapping recipe and a validation image. An object contour mapping is performed using the object contour mapping recipe and the validation image to generate validation contour map output. An object region partition receives a region mask to generate validation object region partition output. A boundary correction is performed using the validation object region partition to generate corrected object boundary output. An update contour mapping is performed using the corrected object boundary, the validation image and the contour mapping recipe to generate updated contour mapping recipe output.
摘要翻译：区域分区的可教对象轮廓映射方法接收对象边界和教学图像。使用对象边界和教学图像执行对象轮廓映射配方创建，以生成对象轮廓映射配方输出。使用对象轮廓映射配方和应用图像将对象轮廓映射应用于应用图像以生成对象轮廓图输出。使用对象轮廓图生成对象区域分区输出的对象区域分区可更新对象轮廓映射方法接收轮廓映射配方和验证图像。使用对象轮廓映射配方和验证图像执行对象轮廓映射以生成验证轮廓图输出。对象区域分区接收区域掩码以生成验证对象区域分区输出。使用验证对象区域分区执行边界校正，以生成校正对象边界输出。使用校正的对象边界，验证图像和轮廓映射配方来执行更新轮廓映射以生成更新的轮廓映射配方输出。

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