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公开(公告)号：US06507675B1
公开(公告)日：2003-01-14
申请号：US09815466
申请日：2001-03-23
申请人： Shih-Jong J. Lee , Seho Oh , Chi-Chou Huang
发明人： Shih-Jong J. Lee , Seho Oh , Chi-Chou Huang
IPC分类号： G06K940
CPC分类号： G06K9/00127 , G06K9/4604 , G06T5/002 , G06T5/30 , G06T2207/20081
摘要： A structure-guided automatic learning system for image feature enhancement uses a learning image together with an application domain structure and detection target specification to produce a feature enhancement image processing recipe. An enhancement goodness measure is used to select between alternatives in the learning process. The feature enhancement recipe is used in an application module to process input images and produce a feature enhanced image output. Calipers are used for application domain structure and detection target specification. To unify the processing steps for all caliper specifications, a non-directional box caliper defined region such as a circle caliper or an arc caliper or other connected structures can be converted into a directional box caliper defined region so that a directional box caliper based feature enhancement method can be applied. The process can be inverted to convert a converted directional box caliper region back to the original format.
摘要翻译： 用于图像特征增强的结构引导自动学习系统使用学习图像以及应用领域结构和检测目标规范来产生特征增强图像处理配方。 增强善良度量被用于在学习过程中选择替代方案。 特征增强配方在应用模块中用于处理输入图像并产生特征增强图像输出。 卡尺用于应用领域结构和检测目标规范。 为了统一所有卡尺规格的加工步骤，可以将诸如圆形卡尺或弧形卡尺或其他连接结构的非方向盒卡尺定义的区域转换成定向盒卡尺规定的区域，以便基于方向盒卡尺的特征增强 方法可以应用。 该过程可以反转，以将转换的方向盒卡尺区域转换回原始格式。
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公开(公告)号：US5740269A
公开(公告)日：1998-04-14
申请号：US309209
申请日：1994-09-20
IPC分类号： G06K9/00
CPC分类号： G06K9/00127
摘要： A robust biological specimen classifier. An automated microscopy system obtains features from an image of a biological specimen slide. A computer system in the automated microscopy system computes feature variations. Clusters are created that comprise samples of similar characteristics. A cluster membership generator generates membership values for each cluster. Classifiers specialized to certain feature sets are used to provide independent outputs. These outputs are offset and biased by the output of the membership generator. The output of the adjusted classification values are summed to create a slide score output.
摘要翻译： 强大的生物样本分类器。 自动显微镜系统从生物标本载玻片的图像中获得特征。 自动显微镜系统中的计算机系统计算特征变化。 创建了包含类似特征样本的集群。 集群成员资格生成器为每个集群生成成员资格值。 专门用于某些特征集的分类器用于提供独立的输出。 这些输出由成员生成器的输出偏移和偏移。 将调整后的分类值的输出相加以创建幻灯片分数输出。
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43.发明授权
公开(公告)号：US5619428A
公开(公告)日：1997-04-08
申请号：US455388
申请日：1995-05-31
CPC分类号： G01N1/312
摘要： An automated biological screening system obtains biological and procedural data from a slide set of a selected clinical laboratory. The integration system tests the data on standardized criteria and passes and fails the data in selected categories. The results of the assessment are used to make process adjustment recommendations based on the results of a laboratory process adjustment procedure. Assessment and adjustment may continue until data from a slide set from the selected clinical laboratory passes in each category. The integration system then sets up, calibrates and installs the automated biological screening system. During operation, the integration system continuously monitors biological data generated by the automated biological screening system. The biological data may also be stored in a central product/service database for additional monitoring. The integration system also serves as an objective standard for reviewing and improving laboratory practices.
摘要翻译： 自动生物筛选系统从选定的临床实验室的幻灯片中获得生物学和程序性数据。 集成系统对标准化标准的数据进行测试，并对所选类别的数据进行传递和失败。 评估结果用于根据实验室过程调整程序的结果制定过程调整建议。 评估和调整可以继续，直到来自所选临床实验室的幻灯片的数据在每个类别中通过。 整合系统建立，校准和安装自动化生物筛选系统。 在运行过程中，集成系统会持续监控自动化生物筛选系统产生的生物数据。 生物数据也可以存储在中央产品/服务数据库中以用于附加监控。 整合系统也是检查和改进实验室实践的客观标准。
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44.发明申请Method for moving cell detection from temporal image sequence model estimation 审中-公开从时间图像序列模型估计中移动细胞检测的方法公开(公告)号：US20110268342A1
公开(公告)日：2011-11-03
申请号：US13135710
申请日：2011-07-13
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： G06K9/0014 , G06T7/254 , G06T2207/10056 , G06T2207/30024
摘要： A computerized robust cell kinetic recognition method for moving cell detection from temporal image sequence receives an image sequence containing a current image. A dynamic spatial-temporal reference generation is performed to generate dynamic reference image output. A reference based object segmentation is performed to generate initial object segmentation output. An object matching and detection refinement is performed to generate kinetic recognition results output. The dynamic spatial-temporal reference generation step performs frame look ahead and the reference images contain a reference intensity image and at least one reference variation image.
摘要翻译： 用于从时间图像序列移动小区检测的计算机化的鲁棒小区动力学识别方法接收包含当前图像的图像序列。 执行动态空间 - 时间参考生成以生成动态参考图像输出。 执行基于参考的对象分割以产生初始对象分割输出。 执行物体匹配和检测细化以产生动力学识别结果输出。 动态空间 - 时间参考生成步骤执行帧前瞻，并且参考图像包含参考强度图像和至少一个参考变化图像。
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公开(公告)号：US08045783B2
公开(公告)日：2011-10-25
申请号：US11595611
申请日：2006-11-09
申请人： Shih-Jong J. Lee , Seho Oh
发明人： Shih-Jong J. Lee , Seho Oh
CPC分类号： G06K9/0014 , G06T7/254 , G06T2207/10056 , G06T2207/30024
摘要： A computerized robust cell kinetic recognition method for moving cell detection from temporal image sequence receives an image sequence containing a current image. A dynamic spatial-temporal reference generation is performed to generate dynamic reference image output. A reference based object segmentation is performed to generate initial object segmentation output. An object matching and detection refinement is performed to generate kinetic recognition results output. The dynamic spatial-temporal reference generation step performs frame look ahead and the reference images contain a reference intensity image and at least one reference variation image.
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公开(公告)号：US07974456B2
公开(公告)日：2011-07-05
申请号：US11516351
申请日：2006-09-05
申请人： Shih-Jong J. Lee , Seho Oh , Hansang Cho
发明人： Shih-Jong J. Lee , Seho Oh , Hansang Cho
IPC分类号： G06K9/00
CPC分类号： G06K9/036 , G06K9/0014 , G06K9/40
摘要： A computerized spatial-temporal regulation method for accurate spatial-temporal model estimation receives a spatial temporal sequence containing object confidence mask. A spatial-temporal weight regulation is performed to generate weight sequence output. A weighted model estimation is performed using the spatial temporal sequence and the weight sequence to generate at least one model parameter output. An iterative weight update is performed to generate weight sequence output. A weighted model estimation is performed to generate estimation result output. A stopping criteria is checked and the next iteration iterative weight update and weighted model estimation is performed until the stopping criteria is met. A model estimation is performed to generate model parameter output. An outlier data identification is performed to generate outlier data output. A spatial-temporal data integrity check is performed and the outlier data is disqualified.
摘要翻译： 用于精确空间 - 时间模型估计的计算机空间 - 时间调节方法接收包含对象置信掩模的空间时间序列。 执行空间 - 时间权重调节以产生权重序列输出。 使用空间时间序列和权重序列来执行加权模型估计，以生成至少一个模型参数输出。 执行迭代权重更新以产生权重序列输出。 执行加权模型估计以产生估计结果输出。 检查停止标准，并执行下一次迭代迭代权重更新和加权模型估计，直到满足停止标准。 执行模型估计以产生模型参数输出。 执行异常值数据识别以产生离群数据输出。 执行空间 - 时间数据完整性检查，异常值数据被取消资格。
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公开(公告)号：US20070127834A1
公开(公告)日：2007-06-07
申请号：US11301292
申请日：2005-12-07
申请人： Shih-Jong Lee , Seho Oh
发明人： Shih-Jong Lee , Seho Oh
CPC分类号： G06K9/6219 , G06K9/38 , G06K9/6277
摘要： A directed pattern enhancement method receives a learning image and pattern enhancement directive. Pattern enhancement learning is performed using the learning image and the pattern enhancement directive to generate pattern enhancement recipe. An application image is received and a pattern enhancement application is performed using the application image and the pattern enhancement recipe to generate pattern enhanced image. A recognition thresholding is performed using the pattern enhanced image to generate recognition result. The pattern enhancement directive consists of background directive, patterns to enhance directive, and patterns to suppress directive. A partitioned modeling method receives an image region and performs feature extraction on the image region to generate characterization feature. A hierarchical partitioning is performed using the characterization feature to generate hierarchical partitions. A model generation is performed using the hierarchical partitions to generate partition model. The partitioned modeling further performs a partitioned matching step that matches an input point to the partition model to generate a matching score output. A partition model update method receives a partition model and input data for model update. A partition model update is performed using the partition model and the data to generate an updated partition model.
摘要翻译： 定向图案增强方法接收学习图像和图案增强指令。 使用学习图像和图案增强指令执行图案增强学习以产生图案增强配方。 接收应用图像，并且使用应用图像和图案增强配方来执行图案增强应用以生成图案增强图像。 使用图案增强图像执行识别阈值以产生识别结果。 模式增强指令包括背景指令，增强指令的模式和抑制指令的模式。 分区建模方法接收图像区域并对图像区域执行特征提取以产生表征特征。 使用表征特征来执行分层分区以生成分层分区。 使用分层分区执行模型生成以生成分区模型。 分区建模还执行将输入点与分区模型相匹配以产生匹配分数输出的分割匹配步骤。 分区模型更新方法接收分区模型并输入模型更新数据。 使用分区模型和数据执行分区模型更新，以生成更新的分区模型。
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公开(公告)号：US07203360B2
公开(公告)日：2007-04-10
申请号：US10410063
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： G06K9/34
CPC分类号： G06K9/00624 , G06T7/11 , G06T7/143 , G06T2200/24 , G06T2207/20081 , G06T2207/20092
摘要： A segmentation method receives a learning image and an objects of interest specification. A segmentation learning method creates a segmentation recipe output. It performs a segmentation application using the second image and the segmentation recipe to create a segmentation result output. The segmentation learning method includes an object region of interest segmentation learning step and an object type specific segmentation learning step. The segmentation application method includes an object region of interest segmentation step and an object type specific segmentation step. The learnable object segmentation method further comprises an online learning and a feedback learning step that allows the update of the segmentation recipe automatically or under user direction.
摘要翻译： 分割方法接收学习图像和兴趣对象规范。 分割学习方法创建分割配方输出。 它使用第二图像和分割配方执行分割应用以创建分割结果输出。 分割学习方法包括感兴趣的对象区域学习步骤和对象类型特定分割学习步骤。 分割应用方法包括感兴趣的对象区域分段步骤和对象类型特定分割步骤。 可学习的对象分割方法还包括在线学习和反馈学习步骤，其允许自动或在用户方向下更新分割配方。
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公开(公告)号：US06859550B2
公开(公告)日：2005-02-22
申请号：US09871991
申请日：2001-05-31
申请人： Seho Oh , Shih-Jong J. Lee
发明人： Seho Oh , Shih-Jong J. Lee
CPC分类号： G06K9/6202
摘要： Noise or outliers corrupt image non-contact measurement of a geometric structure or geometric entity. A weight image is created prior to fitting whose pixel value indicates certainty of image information or feature signal strength. Learning images can enhance the weight image or it can be adjusted by iteration to achieve robust fitting results.
摘要翻译： 噪声或异常值损坏图像的非接触式测量的几何结构或几何实体。 在拟合之前创建加权图像，其像素值表示图像信息或特征信号强度的确定性。 学习图像可以增强体重图像，或者可以通过迭代来调整体重图像，以获得稳健的拟合结果。
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公开(公告)号：US06678404B1
公开(公告)日：2004-01-13
申请号：US09703018
申请日：2000-10-31
申请人： Shih-Jong J. Lee , Seho Oh
发明人： Shih-Jong J. Lee , Seho Oh
IPC分类号： G06K900
摘要： A method for creating and using reference images in a defect detection or location system which receives a plurality of learning images containing objects of interest and creates at least one reference image output. Using the reference image, the computer vision system detects discrepancies between objects of interest in an input image and the expected object from the reference images. The defect detection system generates a discrepancy image output. The computer vision system further determines the existence of the object of interest in an input image and provides the object of interest location if detected.
摘要翻译： 一种用于在缺陷检测或定位系统中创建和使用参考图像的方法，该缺陷检测或位置系统接收包含感兴趣的对象的多个学习图像并创建至少一个参考图像输出。 使用参考图像，计算机视觉系统检测输入图像中的感兴趣对象与来自参考图像的预期对象之间的差异。 缺陷检测系统产生差异图像输出。 计算机视觉系统进一步确定输入图像中感兴趣对象的存在，并且如果检测到，则提供感兴趣的对象位置。
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