Wood Knot Classification using Bagging

Mohan, Shashank; Venkatachalapathy, K.

doi:10.5120/8146-1937

Cited by 10 publications

(16 citation statements)

References 10 publications

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“…The most commonly used 0  and 0  values are 2 and 1 [14], approach and wavelet function are created by using multiples of 2, can be described such as in (2) and 3respectively:…”

Section: Wavelet Moment (Wm) Feature Extraction Methodsmentioning

confidence: 99%

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Classification of Knot Defect Types Using Wavelets and KNN

Çetiner¹,

Var²,

ÇETİNER³

2016

ElAEE

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Automatic defect classification methods are important to increase the productivity of the forest industry. In this respect, classification is also an important component of a pattern recognition system. Well designated classification algorithm will make recognition process more efficient and productive. Quality control is one of the most important steps among the applications that use classification. There are various techniques which are available in order to check quality of wooden material. However, display based quality control of wooden materials is still stands as a hard process. Although there are quality control methods, there are quality control problems because of the classification depending on the person's eyestrain. We aim the reduction of labor costs. Also, we aim to achieve a more accurate and reliable way to make automatic quality classification. In this article, we proposed a method for quality control of wood material. In order to determine quality control of wooden material, knot detection algorithm which is developed using image processing techniques. This knot detection algorithm consists of several steps. These steps are morphological preprocesses in the knot preprocessing step, knot features obtained from Wavelet Moment (WM) in the feature extraction step, k nearest neighbor method (KNN) classification technique in the classification step. Index Terms-Approach coefficients; knot defect types; k nearest neighbour method; wavelet moment; wood.

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“…The most commonly used 0  and 0  values are 2 and 1 [14], approach and wavelet function are created by using multiples of 2, can be described such as in (2) and 3respectively:…”

Section: Wavelet Moment (Wm) Feature Extraction Methodsmentioning

confidence: 99%

“…Knot images were pre-processing using Hilbert Conversion and Gabor filters. Features obtained from preprocessed were compared by 'bagging' technique and were classified using data mining method [2].…”

Section: Related Workmentioning

confidence: 99%

Classification of Knot Defect Types Using Wavelets and KNN

Çetiner¹,

Var²,

ÇETİNER³

2016

ElAEE

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“…İLİŞKİLİ ÇALIŞMALAR [1], ahşabın el ile seçilmesi ve sınıflandırma sürecinin, sıkıcı ve zaman alan bir işlem olduğundan bahsetmektedir. Yapılan çalışmada ahşaptaki budakları tespit etmeye ve sınıflandırabilmeye çalışmışlardır.…”

Section: Gi̇ri̇ş (Başlik 1)unclassified

Classification of KNOT defect types

Cetiner

Var

ÇETİNER

2014

2014 22nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Bu çalışmada, ahşap budak tiplerini içeren bir veritabanı üzerinde deneysel çalışmalar gerçekleştirilmiştir. Veritabanındaki görüntüler üzerinde ön işlemler gerçekletirdikten sonra dalgacık moment öznitelikleri çıkartma algoritması kullanılarak budaklara özgü öznitelikler elde edilmiştir. Bu öznitelikleri üzerinde en ayırt edici olan yaklaşım katsayıları seçilerek KNN sınıflandırma algoritması ile tip tanımlaması yapılmaya çalışılmıştır. Çalışma sonucunda budak görüntüleri yedi farklı budak tipine %98'lik başarı oranı ile sınıflandırılabilmiştir. Anahtar Kelimeler -Budak Tipleri; Dalgacık Moment; KNN Sınıflandırma; Yaklaşım KatsayılarıAbstract-In this study, the experimental studies were carried out on a database containing the types of wood knot. After preprocessing on the images in the database, specific features to knot were obtained using wavelet moments feature extraction algorithm. Type description is carried out with KNN classification algorithm by selecting most distinguishing the approximation coefficients on these features. In conclusion, knot images could be classified with the success rate of 98%.

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“…These techniques are in use and becoming popular in the quality control of products such as textile [3][4][5][6], ceramic [7][8][9], steel [10,11], glass [12], electronic circuits [13], weld bead [14] , metal lids [15] and cylindrical parts [16] to keep up with high quality standards set by the competitive markets. There are also academic studies [17][18][19][20][21][22] on applying image processing techniques in the quality control of wooden surfaces. There are existing quality control systems in the wider wood industry.…”

Section: Introductionmentioning

confidence: 99%

A Quality Control System Prototype for Detecting Knot Defects in the Wooden Panel Manufacturing

Kılıç

Susuz²,

Süzek

2019

Mugla Journal of Science and Technology

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Product quality has become a necessary goal for all manufacturers in today's competitive market. Product defects, not detected, cause financial damages and reputation loss for the manufacturer. These defects can be due to quality of the inputs or misuse of the good quality inputs during the manufacturing process. This is also the case for wooden panel manufacturing where elements are the basic input. It is possible to reduce the loss of the manufacturer by using a method that minimizes the human error in the inspection of the elements. In this study, we, first, identified the quality control problems of the wooden panel manufacturers and basic steps in the automated element quality control. We then developed a prototype for the detection of knots, the most common defects in wooden panels. This prototype, with 80.0% true positive (with knot defect) and 82.0% true negative (without knot defect) rates, performs close to accuracy rates of a quality control inspector. The element image library created during the development of the system made publicly available for use in similar studies. This prototype is expected to be developed to detect other wood defects and to be applied in the wooden panel manufacturing.

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Wood Knot Classification using Bagging

Cited by 10 publications

References 10 publications

Classification of Knot Defect Types Using Wavelets and KNN

Classification of Knot Defect Types Using Wavelets and KNN

Classification of KNOT defect types

A Quality Control System Prototype for Detecting Knot Defects in the Wooden Panel Manufacturing

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