Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern

Salma,; Gunawan, P. H.; Prakasa, Esa; Sugiarto, Bambang; Wardoyo, Riyo; Rianto, Yan; Damayanti, Ratih; Krisdianto, Krisdianto; Dewi, Listya Mustika

doi:10.1109/ic3ina.2018.8629529

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…The research resulted in crystal properties, vascular bundle, fiber length, vessel diameter, and parenchyma, as well as the length and width of the radial and tangential sections (Jeon et al, 2018). Salma et al (2018) proposed a wood identification algorithm by combining Daubechies Wavelet (DW) and Local Binary Pattern (LBP) methods (Salma et al, 2018) as the pattern extractor. The pattern was then classified by using support vector machine (SVM) classifier.…”

Section: Introductionmentioning

confidence: 99%

Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

Fathurahman¹,

Gunawan²,

Prakasa³

et al. 2021

Journal of the Korean Wood Science and Technology

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Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

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Section: Introductionmentioning

confidence: 99%

Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

Fathurahman¹,

Gunawan²,

Prakasa³

et al. 2021

Journal of the Korean Wood Science and Technology

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“…Kvist et al (2018) used the method of fluorescence recovery after photobleaching (FRAP) to perform diffusion measurements locally in the wood microstructure. Salma et al (2018) obtained results able to identify the microscopic image of wood as a wood species with average SVM accuracy of 85%. Fahey et.…”

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confidence: 99%

Sparse attention double-channel FCN network for numerical analysis tracheid features in larch

Zhang

Wang

et al. 2022

Front. Plant Sci.

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Understanding the macro-mechanical behavior of wood at the micro-scale is of great significance for the design of cell-wall-like composite materials and pulp papermaking. In order to predict tracheid mechanical properties and analyze its relationship with tracheid features, based on the FCN network model, a double-channel FCN network with sparse attention (D-SA-FCN) was designed by introducing the double-channel mechanism and the sparse attention mechanism. The features of tracheid of larch were extracted numerically and the data set was established by using the compression strength data, the gray level co-occurrence matrix, cell segmentation and geometric analysis. A feature analysis algorithm based on PCA and random forest was established to optimize the feature values. The training set accuracy of the D-SA-FCN network model reached 85.75% with the five-level mechanical property level according to the classification standard. The accuracy of the training model is 71.48% and 79.52% when the morphological and texture features are input respectively. The results show that texture features had a more significant impact on mechanics to a certain extent and the D-SA-FCN could reduce the computational complexity and improve the prediction accuracy.

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Identifying Wood Types Using Convolutional Neural Network

Rostina

2020

Software Engineering Perspectives in Intelligent Systems

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Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern

Cited by 6 publications

References 7 publications

Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

Sparse attention double-channel FCN network for numerical analysis tracheid features in larch

Identifying Wood Types Using Convolutional Neural Network

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