Teachable object contour mapping for biology image region partition
    1.
    发明申请
    Teachable object contour mapping for biology image region partition 有权
    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

    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。

    Method for kinetic characterization from temporal image sequence
    2.
    发明申请
    Method for kinetic characterization from temporal image sequence 审中-公开
    从时间图像序列的动力学表征方法

    公开(公告)号:US20110274339A1

    公开(公告)日:2011-11-10

    申请号:US13135711

    申请日:2011-07-13

    IPC分类号: G06K9/00

    CPC分类号: G06K9/00127 G06K2009/3291

    摘要: A computerized derivable kinetic characterization measurement method for live cell kinetic characterization inputs kinetic recognition data for a plurality of time frames. A single cell measurement step is performed using the kinetic recognition data for a plurality of time frames to generate single cell feature for a plurality of time frames output. The single cell feature includes cell morphological profiling feature. A kinetic measurement step uses the single cell feature for a plurality of time frames to generate kinetic feature output. A trajectory measurement step uses the single cell feature for a plurality of time frames and the kinetic feature to generate trajectory feature output. An interval measurement step uses the kinetic feature to generate interval feature output. A cell state classifier step uses the interval feature to generate cell state output. A state based measurement uses the single cell feature, the kinetic feature and the cell state to generate state based feature output.

    摘要翻译: 用于活细胞动力学特征的计算机可推导动力学表征测量方法输入多个时间帧的动力学识别数据。 使用多个时间帧的动力学识别数据来执行单个小区测量步骤,以生成多个时间帧输出的单个小区特征。 单细胞特征包括细胞形态分析特征。 动力学测量步骤使用多个时间帧的单细胞特征来产生动力特征输出。 轨迹测量步骤使用单个小区特征用于多个时间帧,并且所述动力特征生成轨迹特征输出。 间隔测量步骤使用动力学特征来产生间隔特征输出。 单元状态分类器步骤使用间隔特征来生成单元格状态输出。 基于状态的测量使用单细胞特征,动力学特征和细胞状态来产生基于状态的特征输出。

    Fast high precision matching method
    3.
    发明授权
    Fast high precision matching method 有权
    快速高精度匹配方法

    公开(公告)号:US07463773B2

    公开(公告)日:2008-12-09

    申请号:US10723397

    申请日:2003-11-26

    IPC分类号: G06K9/68 G06K9/62 G06K9/00

    摘要: An initial search method uses the input image and the template to create an initial search result output. A high precision match uses the initial search result, the input image, and the template to create a high precision match result output. The high precision match method estimates high precision parameters by image interpolation and interpolation parameter optimization. The method also performs robust matching by limiting pixel contribution or pixel weighting. An invariant high precision match method estimates subpixel position and subsampling scale and rotation parameters by image interpolation and interpolation parameter optimization on the log-converted radial-angular transformation domain.

    摘要翻译: 初始搜索方法使用输入图像和模板来创建初始搜索结果输出。 高精度匹配使用初始搜索结果,输入图像和模板来创建高精度匹配结果输出。 高精度匹配方法通过图像插值和插值参数优化来估计高精度参数。 该方法还通过限制像素贡献或像素加权来执行鲁棒匹配。 不变高精度匹配方法通过对数转换的径向角变换域的图像插值和插值参数优化来估计子像素位置和子采样比例尺和旋转参数。

    Method for kinetic characterization from temporal image sequence
    4.
    发明申请
    Method for kinetic characterization from temporal image sequence 审中-公开
    从时间图像序列的动力学表征方法

    公开(公告)号:US20080120077A1

    公开(公告)日:2008-05-22

    申请号:US11604590

    申请日:2006-11-22

    IPC分类号: G06G7/48

    CPC分类号: G06K9/00127 G06K2009/3291

    摘要: A computerized derivable kinetic characterization measurement method for live cell kinetic characterization inputs kinetic recognition data for a plurality of time frames. A single cell measurement step is performed using the kinetic recognition data for a plurality of time frames to generate single cell feature for a plurality of time frames output. The single cell feature includes cell morphological profiling feature. A kinetic measurement step uses the single cell feature for a plurality of time frames to generate kinetic feature output. A trajectory measurement step uses the single cell feature for a plurality of time frames and the kinetic feature to generate trajectory feature output. An interval measurement step uses the kinetic feature to generate interval feature output. A cell state classifier step uses the interval feature to generate cell state output. A state based measurement uses the single cell feature, the kinetic feature and the cell state to generate state based feature output.

    摘要翻译: 用于活细胞动力学特征的计算机可推导动力学表征测量方法输入多个时间帧的动力学识别数据。 使用多个时间帧的动力学识别数据来执行单个小区测量步骤,以生成多个时间帧输出的单个小区特征。 单细胞特征包括细胞形态分析特征。 动力学测量步骤使用多个时间帧的单细胞特征来产生动力特征输出。 轨迹测量步骤使用单个小区特征用于多个时间帧,并且所述动力特征生成轨迹特征输出。 间隔测量步骤使用动力学特征来产生间隔特征输出。 单元状态分类器步骤使用间隔特征来生成单元状态输出。 基于状态的测量使用单细胞特征,动力学特征和细胞状态来产生基于状态的特征输出。

    Fast pattern searching
    5.
    发明授权
    Fast pattern searching 有权
    快速搜索模式

    公开(公告)号:US07142718B2

    公开(公告)日:2006-11-28

    申请号:US10283380

    申请日:2002-10-28

    IPC分类号: G06K9/62 G06K9/54

    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。

    Online learning method in a decision system
    6.
    发明授权
    Online learning method in a decision system 有权
    决策系统中的在线学习方法

    公开(公告)号:US06941288B2

    公开(公告)日:2005-09-06

    申请号:US10118553

    申请日:2002-04-08

    IPC分类号: G06N5/02 G06F15/18

    CPC分类号: G06N5/025

    摘要: A learning model is initiated during start-up learning to activate operation of a decision system. During operation of the decision system, data is qualified for use in online learning. Online learning allows a system to adapt or learn application dependent parameters to optimize or maintain its performance during normal operation. Methods for qualifying data for use in online learning include thresholding of features, restriction of score space for qualified objects, and using a different source of information than is used in the decision process. Clustering methods are used to improve the quality of the learning model. Using the cumulative distribution function to compare two distributions and produce a measure of similarity derives a metric for learning maturity.

    摘要翻译: 在启动学习过程中启动学习模型以激活决策系统的运作。 在决策系统运行过程中,数据有资格用于在线学习。 在线学习允许系统适应或学习应用依赖参数,以在正常操作期间优化或维持其性能。 用于在线学习中使用资料的方法包括特征阈值,限定对象的分数空间限制,以及使用不同于决策过程中使用的信息源。 聚类方法用于提高学习模型的质量。 使用累积分布函数来比较两个分布并产生相似性度量来获得学习成熟度的度量。

    Fast high precision matching method
    7.
    发明申请
    Fast high precision matching method 有权
    快速高精度匹配方法

    公开(公告)号:US20050114332A1

    公开(公告)日:2005-05-26

    申请号:US10723397

    申请日:2003-11-26

    申请人: Shih-Jong Lee Seho Oh

    发明人: Shih-Jong Lee Seho Oh

    IPC分类号: G06F17/30 G06K9/68 G06T7/00

    摘要: A fast high precision matching method receives an input image and a template. An initial search method uses the input image and the template to create an initial search result output. A high precision match uses the initial search result, the input image, and the template to create a high precision match result output. The high precision match method estimates high precision parameters by image interpolation and interpolation parameter optimization. The high precision match method also performs robust matching by limiting pixel contribution or pixel weighting. An invariant high precision match method estimates subpixel position and subsampling scale and rotation parameters by image interpolation and interpolation parameter optimization on the log-converted radial-angular transformation domain. This invention provides a fast method for high precision matching with the equivalent subpixel and subsampling interpolation in the image or template domain without actual performing the subpixel interpolation and/or subsampling. It achieves the high precision through sampling parameter optimization. Therefore, very fine sampling precision can be accomplished without the difficulty of high resolution image/template storage and expensive computation for actual matching at high resolution. This invention is generalized to include the high precision scale and rotation invariant matching through parameter optimization on log-converted radial-angular coordinate. This invention can be easily generalized to three-dimensional or higher dimensional invariant high precision pattern search and can achieve even greater speed advantage comparing to the prior art methods. Therefore, it can be used in applications such as 3D medical imaging, dynamic medical imaging, confocal microscopy, live cell assays in drug discovery, or ultrasound imaging.

    摘要翻译: 快速高精度匹配方法接收输入图像和模板。 初始搜索方法使用输入图像和模板来创建初始搜索结果输出。 高精度匹配使用初始搜索结果,输入图像和模板来创建高精度匹配结果输出。 高精度匹配方法通过图像插值和插值参数优化来估计高精度参数。 高精度匹配方法还通过限制像素贡献或像素加权来执行鲁棒匹配。 不变高精度匹配方法通过对数转换的径向角变换域的图像插值和插值参数优化来估计子像素位置和子采样比例尺和旋转参数。 本发明提供了一种用于与图像或模板域中的等效子像素和子采样内插进行高精度匹配而不实际执行子像素内插和/或二次采样的快速方法。 通过采样参数优化实现了高精度。 因此,可以实现非常精细的采样精度,而不需要高分辨率图像/模板存储的难度,并且在高分辨率下实际匹配的昂贵的计算。 本发明概括为包括通过对数转换的径向角坐标的参数优化的高精度尺度和旋转不变匹配。 本发明可以容易地推广到三维或更高维度不变高精度图案搜索,并且与现有技术方法相比可以获得更大的速度优势。 因此,它可以用于3D医学成像,动态医学成像,共聚焦显微镜,药物发现中的活细胞测定或超声成像等应用。

    Structure-guided automatic alignment for image processing
    8.
    发明授权
    Structure-guided automatic alignment for image processing 有权
    结构引导自动对准图像处理

    公开(公告)号:US06829382B2

    公开(公告)日:2004-12-07

    申请号:US09882734

    申请日:2001-06-13

    IPC分类号: G06K900

    摘要: When application domain structure information is erroneously encoded into parameters for image processing and measurements the accuracy of the result can degrade. A structure-guided automatic alignment system for image processing receives an image input and application domain structure input and automatically creates an estimated structure output having improved alignment. Measurement and image processing robustness are improved.

    摘要翻译: 当应用程序域结构信息被错误地编码为用于图像处理和测量的参数时,结果的精度可能降低。 用于图像处理的结构引导自动对准系统接收图像输入和应用域结构输入,并自动创建具有改进的对准的估计结构输出。 改善了测量和图像处理的鲁棒性。

    Structure-guided image measurement method
    9.
    发明授权
    Structure-guided image measurement method 有权
    结构导向图像测量方法

    公开(公告)号:US06456741B1

    公开(公告)日:2002-09-24

    申请号:US09739084

    申请日:2000-12-15

    IPC分类号: G06K946

    CPC分类号: G06K9/4609 G06T7/13

    摘要: Structure-guided image estimation and measurement methods are described for computer vision applications. Results of the structure-guided estimation are symbolic representations of geometry entities such as lines, points, arcs and circles. The symbolic representation facilitates sub-pixel measurements by increasing the number of pixels used in the matching of image features to structural entities, improving the detection of structural entities within the image, weighting the contribution of each image sample to the measurement that is being made and optimizing that contribution. After the structure-guided estimation, geometric entities are represented by their symbolic representations. Structure-guided measurements can be conducted using the symbolic representation of the geometric entities. Measurements performed from the symbolic representation are not limited by image resolution or pixel quantization error and therefore can yield sub-pixel accuracy and repeatability.

    摘要翻译: 针对计算机视觉应用描述了结构引导图像估计和测量方法。 结构指导估计的结果是几何实体的象征性表示,如线,点,弧和圆。 符号表示通过增加在图像特征与结构实体的匹配中使用的像素数量来促进子像素测量,改善图像内的结构实体的检测,对每个图像样本对正在进行的测量的贡献加权,以及 优化该贡献。在结构指导估计之后,几何实体由其符号表示来表示。 可以使用几何实体的符号表示来进行结构指导测量。 从符号表示进行的测量不受图像分辨率或像素量化误差的限制,因此可以产生子像素精度和重复性。

    Optical neural net memory
    10.
    发明授权
    Optical neural net memory 失效
    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

    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。