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

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.

Keywords

• Line Fitting
• Linear Smoothing
• Locally Deflection

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