A Line Fitting Algorithm: Linear Fitting on Locally Deflection (LFLD)

Mahmut Sami Yasak, Muhammed Said Bi̇lgehan
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Abstract

The main motivation of the study is to prevent and optimize the deviations in linear connections with complex calculations related to the previous and next steps. This purpose is used for more stable detection and therefore segmentation of object edge/corner regions in Quality Control Systems with Image Processing and Artificial Intelligence algorithms produced by authors within Alpplas Industrial Investments Inc. The dataset used in this area was originally obtained as a result of the edge approaches of the plastic panels manufactured by Alpplas Inc., extracted from the images taken from the AlpVision Quality Control Machine patented with this research. The data consists entirely of the pixel values of the edge points. Dispersed numeric data sets have quite changeable values, create high complexity and require the computation of formidable correlation. In this study, dispersed numeric data optimized by fitting to linearity. The LFLD (Linear Fitting on Locally Deflection) algorithm developed to solve the problem of linear fitting. Dispersed numeric data can be regulated and could be rendered linearly which is curved line smoothing, or line fitting by desired tolerance values. The LFLD algorithm organizes the data by creating a regular linear line (fitting) from the complex data according to the desired tolerance values.
一种线性拟合算法:局部偏转线性拟合(LFLD)
研究的主要动机是防止和优化与前一步和下一步相关的复杂计算的线性连接中的偏差。该目的用于更稳定的检测,从而在质量控制系统中使用由alplas Industrial Investments Inc.的作者制作的图像处理和人工智能算法分割对象边缘/角落区域。该领域使用的数据集最初是由Alpplas Inc.生产的塑料面板的边缘方法获得的,提取自本研究专利的AlpVision质量控制机器拍摄的图像。数据完全由边缘点的像素值组成。分散的数值数据集值变化大,复杂性高,需要计算强大的相关性。在本研究中,分散的数值数据通过拟合线性优化。为了解决线性拟合问题,提出了局部偏转线性拟合算法(LFLD)。分散的数值数据可以调节,并可以呈现线性,即曲线平滑,或线拟合所需的公差值。LFLD算法通过根据期望的公差值从复杂数据中创建规则的线性线(拟合)来组织数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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