scholarly journals Classification of wood defect images using local binary pattern variants

2020 ◽  
Vol 6 (1) ◽  
pp. 36
Author(s):  
Rahillda Nadhirah Norizzaty Rahiddin ◽  
Ummi Rabaah Hashim ◽  
Nor Haslinda Ismail ◽  
Lizawati Salahuddin ◽  
Ngo Hea Choon ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wood Species ◽  
Local Binary Pattern ◽  
Rotation Invariant ◽  
Texture Representation ◽  
Accuracy Level ◽  
Extraction Processes ◽  
Uniform Lbp

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

scholarly journals A High Performace of Local Binary Pattern on Classify Javanese Character Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Ajib Susanto ◽  
Daurat Sinaga ◽  
Christy Atika Sari ◽  
Eko Hari Rachmawanto ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Characteristic Extraction ◽  
Research Objects ◽  
Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

1D-local binary pattern based feature extraction for classification of epileptic EEG signals

2014 ◽  
Vol 243 ◽  
pp. 209-219 ◽  
Author(s):  
Yılmaz Kaya ◽  
Murat Uyar ◽  
Ramazan Tekin ◽  
Selçuk Yıldırım
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Eeg Signals

scholarly journals Robust Iris Classification Based on Deep Neural Network (DNN) and Stationary Wavelet Transform (SWT)

2019 ◽  
pp. 198-204
Author(s):  
Priyanka S ◽  
Pavithra V ◽  
Pavithra M ◽  
S. Bhuvana
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Major Part ◽  
Deep Neural Network ◽  
Local Binary Pattern ◽  
Vital Role ◽  
Stationary Wavelet Transform ◽  
Huge Amount

The eye is a vital part of our body. It consists of several layers like sclera, retina, tunica, and iris. Among these several layers, Iris plays a vital role in human visionary. There are various infections which affect the Iris functioning. The sign, symptoms, and diagnosis of this is still a challenge for doctors. To overcome this many techniques and technologies have been introduced. But still, the existing system has several drawbacks in recognition like a huge amount of dataset, classification, extraction, etc. To overcome this we propose a system where Deep Neural Network plays a major part. It classifies the iris disease in our eyes in a more clear and precise manner. In additional to Deep Neural Network several other algorithms have been used like Stationary Wavelet Transform, for image selection and recognition, Local Binary Pattern, for Feature extraction and at a final stage Deep Neural Network for classification of Iris images.

scholarly journals Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

2019 ◽  
Vol 9 (11) ◽  
pp. 2211
Author(s):  
Qinghe Feng ◽  
Ying Wei ◽  
Yugen Yi ◽  
Qiaohong Hao ◽  
Jiangyan Dai
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Computational Cost ◽  
Natural Scene ◽  
Color Quantization ◽  
Rotation Invariant ◽  
Structure Pattern ◽  
Depth Analysis ◽  
Earth Observation Satellites ◽  
Cross Structure

With the advent of medical endoscopes, earth observation satellites and personal phones, content-based image retrieval (CBIR) has attracted considerable attention, triggered by its wide applications, e.g., medical image analytics, remote sensing, and person re-identification. However, constructing effective feature extraction is still recognized as a challenging problem. To tackle this problem, we first propose the five-level color quantizer (FLCQ) to acquire a color quantization map (CQM). Secondly, according to the anatomical structure of the human visual system, the color quantization map (CQM) is amalgamated with a local binary pattern (LBP) map to construct a local ternary cross structure pattern (LTCSP). Third, the LTCSP is further converted into the uniform local ternary cross structure pattern (LTCSPuni) and the rotation-invariant local ternary cross structure pattern (LTCSPri) in order to cut down the computational cost and improve the robustness, respectively. Finally, through quantitative and qualitative evaluations on face, objects, landmark, textural and natural scene datasets, the experimental results illustrate that the proposed descriptors are effective, robust and practical in terms of CBIR application. In addition, the computational complexity is further evaluated to produce an in-depth analysis.

Illumination and Rotation Invariant Texture Representation for Face Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 58-69
Author(s):  
Medha Kudari ◽  
Shivashankar S. ◽  
Prakash S. Hiremath
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Galois Field ◽  
Rotation Invariance ◽  
Invariance Property ◽  
Rotation Invariant ◽  
Texture Representation ◽  
Novel Approach ◽  
Local Gradient

This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.

scholarly journals Image Classification with Shell Texture Feature Extraction Using Local Binary Pattern (LBP) Method

2020 ◽  
Vol 3 (1) ◽  
pp. 48-57
Author(s):  
Putri Aisyiyah Rakhma Devi ◽  
Rizqi Putri Nourma Budiarti
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Extraction Process ◽  
Input Image ◽  
Texture Feature Extraction ◽  
Classification Procedure ◽  
Svm Classification ◽  
Texture Characteristics

Classification procedure that is usually done manually by way of separation based on the texture of the shell shell. Classification is done by looking at objects based on inherent characteristics usually referred to as features / characteristics. Classification by hand can cause accuracy problems. In the image of the shells, texture characteristics are needed to distinguish one type of shell from another. The purpose of this study is to develop a texture feature extraction system for the classification of shell images. The input image is carried out preprocessing and segmenting to separate objects from the background and the image of the separated object is transformed into a grayscale image for the feature extraction process using the Local Binary Pattern method. Based on trials that have been done, the accuracy is quite good, the highest accuracy value occurs in shellfish blood cockles with RBF kernels. While the lowest accuracy is on testing the feather shell image where the accuracy value is 86.6% this result can show that the LBP method with SVM classification is quite reliable in calculating the accuracy for the classification process of shellfish types.

AUTOMATIC DEFECT CLASSIFICATION OF TFT-LCD PANELS WITH SHAPE, HISTOGRAM AND COLOR FEATURES

2013 ◽  
Vol 13 (03) ◽  
pp. 1350011 ◽  
Author(s):  
WEI HUANG ◽  
HONGTAO LU
Keyword(s):  
Local Binary Pattern ◽  
Liquid Crystal Display ◽  
Thin Film Transistor ◽  
Defect Classification ◽  
Rotation Invariant ◽  
Color Features ◽  
Low Dimension ◽  
Linear Svm ◽  
Dimension Number

In this paper, an automatic defect classification algorithm for thin film transistor liquid crystal display (TFT-LCD) manufacturing is proposed. Each sample of defect data contains three images: the original image, the defect shape image and the circuit zone image. A set of features including shape, histogram and color is extracted. Some common classifiers were tested in the experiments and Linear-SVM (Linear Surport Vector Machine) was chosen in practical manufacturing. A novel LBP-E feature considering intensity equality proposed in this paper is compared to other original rotation invariant LBP (Local Binary Pattern) features. The experimental results show that our method can generate a better result with a relatively low dimension number.

Sign in / Sign up

Export Citation Format

Share Document