Wood Strength Classification Based on RGB Color and Image Texture Using KNN Method

Sukrisdyanto, Okta Dhirga; Purnama, I Ketut Eddy; Nugroho, Supeno Mardi Susiki

doi:10.1109/isitia.2019.8937239

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…In [6], the authors studied the supervised machine learning methods for wood classification based on the main color characteristic obtained through the HSV color model and co-occurrence matrix characteristic. In [12], the average RGB histogram value for each color channel and the static characteristics of the gray-level co-occurrence matrix were used as features to identify the strength of wood. In [13], the SVM (support vector machine) model achieved an accuracy of 0.960 for classification of thermally-modified wood using the color lightness parameter as a feature.…”

Section: Related Workmentioning

confidence: 99%

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

et al. 2020

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Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.

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Section: Related Workmentioning

confidence: 99%

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

et al. 2020

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Indonesian wood feature texture extraction method using LBP-histogram and gray level co-occurance matrices

Fahrurozi¹,

Kosasih²

2023

Toward Adaptive Research and Technology Development for Future Life

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The implementation of k-means algorithm to determine the quality of teak wood in image based on the texture

Darmawan

Novantara

Suwarto

et al. 2021

J. Phys.: Conf. Ser.

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Teak is a type of tree that produces high quality wood. Large tree, straight trunk, can grow to a height of 30-40 m. In the developing teak parquet industry, the process of selecting raw materials is very important. This is due to the many types, types and classes of wood texture. The choice of wood texture is still widely used by human workers, most of which require a long time and the results obtained are still inaccurate. This study aims to solve the problem of quality teak grouping based on image texture by creating a computerized system or application using the K-means algorithm. With this method, it is hoped that the classification of the quality of teak can be described more easily based on the texture of the image. Based on the results of system design and testing, the application can group the quality of teak wood based on the texture and color of the image

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Wood Strength Classification Based on RGB Color and Image Texture Using KNN Method

Cited by 5 publications

References 7 publications

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Indonesian wood feature texture extraction method using LBP-histogram and gray level co-occurance matrices

The implementation of k-means algorithm to determine the quality of teak wood in image based on the texture

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