Image Extraction in OpenCV Using the Local Binary Pattern Method

This research aims to implement the Local Binary Pattern (LBP) method for image extraction in OpenCV software. This method separates the objects' characteristics in the image to be recognized accurately. The research process involves stages of device configuration, image data preprocessing, and implementation and evaluation of extraction results. The research results show that the LBP method produces images with various patterns, such as grayscale, the original LBP shape, and other variations. This study also discusses the application of edge detection, histogram, thresholding, and equalization for image processing. Implementation of this method opens up opportunities for further development in various areas, including object identification, video, and wood imagery.

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Publication Details

Journal
Journal of Software Engineering Information and Communication Technology (SEICT)
Published
2024-12-01
DOI
https://doi.org/10.17509/seict.v5i2.76251
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

Image Extraction in OpenCV Using the Local Binary Pattern Method

Yenni Fatman, Nani Sarah Hapsari, Bambang Sugiarto
Journal of Software Engineering Information and Communication Technology (SEICT)
Industrial Vision Systems and Defect Detection
article

Image Extraction in OpenCV Using the Local Binary Pattern Method

Yenni Fatman, Nani Sarah Hapsari, Bambang Sugiarto
article en

Abstract

This research aims to implement the Local Binary Pattern (LBP) method for image extraction in OpenCV software. This method separates the objects' characteristics in the image to be recognized accurately. The research process involves stages of device configuration, image data preprocessing, and implementation and evaluation of extraction results. The research results show that the LBP method produces images with various patterns, such as grayscale, the original LBP shape, and other variations. This study also discusses the application of edge detection, histogram, thresholding, and equalization for image processing. Implementation of this method opens up opportunities for further development in various areas, including object identification, video, and wood imagery.

Journal of Software Engineering Information and Communication Technology (SEICT)Vol. 5(2)
Universitas Islam Nusantara (ID), National Research and Innovation Agency (ID)
Openalex Percentile: Top 55%
Industrial Vision Systems and Defect Detection
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