Evaluation of texture feature based on basic local binary pattern for wood defect classification

Ibrahim, Eihab Abdelkariem Bashir; Hashim, Ummi Rabaah; Salahuddin, Lizawati; Ismail, Nor Haslinda; Choon, Ngo Hea; Kanchymalay, Kasturi; Zabri, Siti Normi

doi:10.26555/ijain.v7i1.393

Cited by 6 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) Timber trade [38,39]: measurement detection of construction timber can help timber traders evaluate, classify, and improve timber quality and market value. (4) Environmental protection [40,41]: building wood measurement detection can help reduce wood waste and loss, improve wood utilization efficiency and sustainability, and thus promote environmental protection. (5) Construction insurance [42,43]: measurement detection of construction wood can provide quality and structural information of wood, providing more accurate risk assessment and pricing for construction insurance companies.…”

Section: Application Fields and Future Prospectsmentioning

confidence: 99%

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Wang,

Xu,

Gui

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Automatic measurement detection in the process of wood processing can effectively improve the production efficiency and yield of promote the upgrading of wood processing industry. Therefore, this article conducts research on visual detection technology for wood surface measurements and proposes a construction wood measurement detection method based on interval type II fuzzy theory. This method overcomes the problem of increasing heterogeneity and similarity of homogeneous areas on the wood surface, which increases the difficulty of measurement detection. Firstly, the Gaussian model is improved by establishing a wood measurement image feature model that considers rate neighborhood relationships as the basic model. Then, the basic model is fuzzified to construct an interval Type-2 fuzzy model (IT2FM) to characterize the uncertainty features of the modeling and achieve measurement detection of building wood. We conducted experiments using a wood measurement detection dataset to study the influence of the construction form of IT2FM on wood measurement detection, and evaluated the results using kappa values. The detection accuracy of this method is improved by about 10% compared to traditional machine learning and deep learning methods, and it only takes about 3-5 seconds to run the program. The experimental results show that the proposed method can effectively suppress the noise generated by wood measurement detection and prevent missed detection. It can achieve high-quality measurement detection of wood dead knots, loose knots, and cracked parts.

show abstract

Section: Application Fields and Future Prospectsmentioning

confidence: 99%

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Wang,

Xu,

Gui

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…These parameters were determined using the multi-class confusion matrix. A confusion matrix is a tool for demonstrating the performance of classification methods by displaying the specifics of successfully classified and incorrectly classified data [25]. The confusion matrix for five courses in this study is presented in Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Winarno

Hadikurniawati

Septiarini

et al. 2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.

show abstract

“…Ondrejka et al [26] developed an algorithm framework based on deep learning, established a convolutional neural network composed of eight layers of networks for feature extraction, and extracted wood surface defect features from a deep learning network model, thus improving the detection efficiency and classification accuracy of wood defects. Reference [27] used histogram threshold method to segment images, extracted wood defect characteristic values by principal component analysis, and improved classifier by extreme learning machine algorithm combined with AdaBoost algorithm to improve the detection accuracy of wood defects and effectively classify wood defects. e authors of [28] were the first to put forward the theoretical formula of log axial stress wave propagation, namely, displacement, velocity, stress, and strain equations.…”

Section: Research Status Of Wood Nondestructive Testingmentioning

confidence: 99%

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Ping-an

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Traditional nondestructive testing technology for wood defects has a series of problems such as low identification accuracy, high cost, and cumbersome operation, and traditional testing methods cannot accurately show the specific location and size of wood internal defects; it is urgent to explore a new nondestructive testing scheme for wood defects. Aiming at this problem, this paper designs and develops an automatic detection method for wood surface defects based on deep learning algorithm and multicriteria framework. By comparing the performance of different deep learning detection methods on the data set, the advantages and disadvantages of the detection method in this paper are proved. After a series of works, such as the development and optimization of the experimental algorithm, the algorithm proposed meets the requirements in both the detection accuracy and training time.

show abstract

Evaluation of texture feature based on basic local binary pattern for wood defect classification

Cited by 6 publications

References 0 publications

A fast detection method for building wood measurement based on interval type-2 fuzzy model

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Contact Info

Product

Resources

About