首页> 外国专利> LEARNING CONTOUR IDENTIFICATION SYSTEM USING PORTABLE CONTOUR METRICS DERIVED FROM CONTOUR MAPPINGS

LEARNING CONTOUR IDENTIFICATION SYSTEM USING PORTABLE CONTOUR METRICS DERIVED FROM CONTOUR MAPPINGS

机译:使用从等高线图得出的便携式等值线的学习等高线识别系统

摘要

A system and method that transforms data formats into contour metrics and further transforms each contour of that mapping into contours pattern metric sets so that each metric created has a representation of one level of contour presentation, at each iteration of the learning contour identification system defined herein. This transformation of data instance to contour metrics permits a user to take relevant data of a data set, as determined by a learning contour identification system, to machines of other types and function, for the purpose of further analysis of the patterns found and labeled by said system. The invention performs with data format representations, not limited to, signals, images, or waveform embodiments so as to identify, track, or detect patterns of, amplitudes, frequencies, phases, and density functions, within the data case and then by way of using combinations of statistical, feedback adaptive, classification, training algorithm metrics stored in hardware, identifies patterns in past data cases that repeat in future, or present data cases, so that high-percentage labeling and identification is a achieved.
机译:一种系统和方法,其在本文定义的学习轮廓识别系统的每次迭代中,将数据格式转换为轮廓度量,并进一步将该映射的每个轮廓转换为轮廓模式度量集,以使创建的每个度量都具有一个轮廓表示级别的表示。 。数据实例到轮廓量度的这种转换允许用户将由学习轮廓识别系统确定的数据集的相关数据用于其他类型和功能的机器,以进一步分析所发现和标记的模式。说系统。本发明以不限于信号,图像或波形实施例的数据格式表示来执行,以便识别,跟踪或检测在数据情况下然后通过以下方式的幅度,频率,相位和密度函数的模式。使用存储在硬件中的统计,反馈自适应,分类,训练算法指标的组合,识别将来重复的过去数据案例或当前数据案例中的模式,从而实现高百分比的标记和标识。

著录项

  • 公开/公告号CA2965739A1

    专利类型

  • 公开/公告日2016-06-02

    原文格式PDF

  • 申请/专利权人 PADUBRIN HARRY FRIEDBERT;

    申请/专利号CA20152965739

  • 发明设计人 PADUBRIN HARRY FRIEDBERT;

    申请日2015-11-24

  • 分类号G06K9/80;G06K9/46;G06K9/62;

  • 国家 CA

  • 入库时间 2022-08-21 14:21:15

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