Wood Texture Features Extraction by Using GLCM Combined With Various Edge Detection Methods

Fahrurozi, Achmad; Madenda, Sarifuddin; Ernastuti,; Kerami, Djati

doi:10.1088/1742-6596/725/1/012005

Cited by 16 publications

(17 citation statements)

References 3 publications

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“…Change the parameters of spatial distance and graylimits not provide a considerable influence on the accuration rate and precision, except on Canny operator. Thus, the best value for a parameter G that can be selected is 8, as mentioned in [19] with the intention of reducing the computation, because the size of co-occurrence matrix is smaller than the current G value greater than 8. …”

Section: Wood Classification Of Proposed Methodsmentioning

confidence: 99%

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi

Madenda

Ernastuti

et al. 2016

IJECE

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One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Cooccurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier. Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.

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Section: Wood Classification Of Proposed Methodsmentioning

confidence: 99%

Wood Classification Based on Edge Detections and Texture Features Selection

Fahrurozi

Madenda

Ernastuti

et al. 2016

IJECE

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“…GLCM produces features that describe the relationship of adjacency among pixels in a texture image effectively . These features are extracted from cooccurrence matrices . The determination of the adjacency relationship between the pixels of the cooccurrence matrix is illustrated in Figure .…”

Section: Background Knowledgementioning

confidence: 99%

“…Example of cooccurrence matrix construction . Left: matrix representation of grayscale image size 6 × 6 with gray levels 0 to 7 ( G = 8); Right: cooccurrence matrix M 1,0 ( i , j ), size 8 × 8 [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Background Knowledgementioning

confidence: 99%

“…29 These features are extracted from cooccurrence matrices. 30 The determination of the adjacency relationship between the pixels of the cooccurrence matrix is illustrated in Figure 1. The values of the angle parameters ( ), often used in studies on the analysis and classification of texture, are 0 • , 45 • , 90 • , and 135 • .…”

Section: Texture Feature Extractionmentioning

confidence: 99%

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Artificial bee colony algorithm for content‐based image retrieval

Banharnsakun

2020

Computational Intelligence

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Image retrieval is the process of searching for digital images from a large database. There exist two distinctive research groups, which employ the content‐based and description‐based approaches, respectively. However, research in the content‐based domain is currently dominant in the field, while the other approaches are not as widely utilized. Although there are a number of different techniques that are available for image retrieval, the development of more effective methods is still necessary. In recent years, previous research has shown that biologically inspired metaheuristic algorithms have great potential for use in solving problems in many science and engineering domains. The artificial bee colony (ABC) algorithm is one of the more promising biologically inspired metaheuristic approaches used to find optimal solutions as it has the advantages of convenient implementation and efficient performance. In this article, a new efficient method based on a combination of the gray‐level cooccurrence matrix (GLCM) with the ABC, referred to as “GLCM‐ABC,” is proposed for use in content‐based image retrieval (CBIR). The experimental results demonstrate that the proposed approach works well for CBIR and can classify specific types of material surfaces in images with a reasonably high level of accuracy as well as outperform other existing algorithms.

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“…Recently, researchers have attempted to recognize wood species by utilizing a growth ring boundary detection algorithm (Fahrurozi et al 2016) such as the Gray Level Co-occurrence Matrix (Xie & Wang 2015;Fahrurozi et al 2016), and the color histogram statistical method (Zhao 2013) to extract wood features. Subsequently, various techniques, including Support Vector Machine (SVM) (Sun et al 2015), K-nearest neighbor (KNN) (Gani & Mohamed 2013;Kobayashi et al 2015;Fuentealba et al 2005), and neural network (Zhao et al 2014;Yuce et al 2014), have been used to create many classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Computer-aided Method for Identifying the Presence of Softwood Growth Ring Boundaries

Lin

Sun

et al. 2020

Preprint

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The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, then every image is binarized as a gray image according to an automatic threshold value.After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If this was successful, the growth ring boundaries of the sub-image are distinct, otherwise they were indistinct or absent. If 10 of the 20 sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood are distinct, otherwise they are indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the .NET Framework. A sample of 100 micro-images (Supplementary Images) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involved image importing, image cropping, image reading, image grayscale, image binarization, data analysis.The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%.

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Wood Texture Features Extraction by Using GLCM Combined With Various Edge Detection Methods

Cited by 16 publications

References 3 publications

Wood Classification Based on Edge Detections and Texture Features Selection

Wood Classification Based on Edge Detections and Texture Features Selection

Artificial bee colony algorithm for content‐based image retrieval

A Computer-aided Method for Identifying the Presence of Softwood Growth Ring Boundaries

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