scholarly journals Deteksi Api Pada Video Menggunakan Metode Artificial Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 96-107
Author(s):  
Budiman Rabbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Validation Process ◽  
Feature Extraction Method ◽  
Time Value ◽  
Occurrence Matrix

Abstract The camera is one of the tools used to collect images. Cameras are often used for the safety of homes, highways and others. Then in this study camera captures are used to support fire objects because fire is one of the causes of safety that can be controlled. Therefore, by utilizing a capture camera will see the best model of backpropagation neural network by combining the local binary patern (LBP) feature extraction method and the Gray Level Co-occurrence Matrix (GLCM) to access the fire. Then to evaluate the performance of the model created using three parameters that contain accuracy, recall, precision. The data in this study consisted of videos with variations of fire and non-fire videos. Based on the final results of the study, accuracy, remember, the best precision obtained simultaneously 96%, 97%, 97%. Then the validation process was done using 30 videos with a variation of 15 fire videos and 15 non-fire videos and obtained an accuracy of 86.6% with an average time value of 6.029 minutes.

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

On-Line Quality Detection of Ultrasonic Wire Bonding via Refining Analysis of Electrical Signal From Ultrasonic Generator

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Wuwei Feng ◽  
Qingfeng Meng ◽  
Youbo Xie ◽  
Hong Fan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Wire Bonding ◽  
Bond Quality ◽  
Feature Extraction Method ◽  
Quality Detection ◽  
On Line ◽  
Ultrasonic Wire Bonding

A technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

Digital Image Technology Mineral Feature Extraction Method Research

2014 ◽  
Vol 989-994 ◽  
pp. 3906-3909
Author(s):  
Jian Peng ◽  
Dong Bo Li
Keyword(s):  
Feature Extraction ◽  
Traditional Method ◽  
Extraction Method ◽  
Texture Classification ◽  
Gabor Wavelet ◽  
Gabor Transform ◽  
Support Vector ◽  
Gray Level ◽  
Feature Extraction Method ◽  
Occurrence Matrix

This paper presents a texture classification algorithm using Gabor wavelet and Gray Level Co-occurrence Matrix as feature extraction method and Support Vector Machine as classifier. Gabor transform and Gray Level Co-occurrence Matrix are used to get the features of the digital images, SVM classifiers are followed to build image and realize classification. The results of the experiments have shown that the methods described in this paper can improve the rate of correct classification effectively than traditional method of classification.

scholarly journals THE IMPLEMENTATION OF EXTRACTION FEATURE USING GLCM AND BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK TO CLASIFY LOMBOK SONGKET WOVEN CLOTH

2020 ◽  
Vol 17 (2) ◽  
pp. 131-136
Author(s):  
Bahtiar Imran ◽  
Muhamad Masjun Efendi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Back Propagation ◽  
Training Data ◽  
Feature Extraction Method ◽  
Good Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The aimed of this study was to apply the feature extraction method of GLCM and Back-propagation Artificial Neural Network (ANN) to classify Lombok's typical Songket woven cloth by classifying based on the texture of the Songket woven cloth. Songket woven cloth in Lombok in terms of weaving and texture are vary from region to region. For example the songket woven cloth in Pringgasela Village, Sukarara Village and Sade Village has differences in texture and motifs. For this reason, this study focuses on classifying Lombok's typical Songket woven cloth by performing feature extraction on woven cloth using the GLCM method and the classification method uses Back-propagation Artificial Neural Network (ANN). For data collection, the data was taken directly from the Songket weaving centers in Pringgasela, Sade and Sukarara. In the classification stage the training data used were 64 data and 11 test data. Then the epoch used was 41 iterations with a time of 0:00:04, with neurons 80 and 100. The use of neurons 80 generated 18% which was successful in the classification. While using 100 neurons generated 100% successful which was can be classified. Based on the classification results obtained, the use of 100 neurons gained good classification results.

scholarly journals Banten Batik Classification with Backpropagation Neural Network

2018 ◽  
Vol 218 ◽  
pp. 03012
Author(s):  
Rian Fahrizal ◽  
Ridwan Prasetya Parlindungan Siahaan ◽  
Romi Wiryadinata
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Public Awareness ◽  
The Other ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Gray Scale ◽  
Indigenous Cultures ◽  
Artificial Neural ◽  
Occurrence Matrix

Batik Cloth is Indonesia heritage of the indigenous cultures. However, the lack of public awareness could made the other nations has claimed that Batik cloth as their heritage. Therefore, We will need serious attention to take the problems. The classification Banten pattern batik cloth had been made to protect the Batik pattern. The classification had been made with Artificial Neural Network with Lavender-Marquant methods. Gray-Level Co-Occurrence Matrix (GLCM) has been used to preprocessing the pattern from Gray scale images. The results showed that the Batik patterns had 97% accuracy and 2% error.

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