Detection of Wood Features Extraction Region using Convolutional Neural Network
Pages 136 - 141
Abstract
Wood is applied with different industries such as building houses, bridges and depending on their strength. According to the research [12], the strength of wood can be inferred by wood features. Although [3] can extract wood features, [3] fails to extract wood features accurately on some wood images such as the indoor wood images of the unprocessed high-noise logs. If the region of the indoor wood images of the unprocessed high-noise logs can be removed as the same wood features appear in the places where are low-noise region, wood features can be extracted more effectively. This paper proposes a simple Convolutional Neural Network model to detect the region of the unprocessed low-noise logs in the indoor wood images for wood features extraction. Experimental results show that wood features can more effectively be extracted on the indoor wood images of the unprocessed high-noise logs.
References
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- Detection of Wood Features Extraction Region using Convolutional Neural Network
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Published: 22 October 2021
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ACIT 2021
ACIT 2021: The 8th International Virtual Conference on Applied Computing & Information Technology
June 20 - 22, 2021
Kanazawa, Japan
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