ROOM MATERIAL IDENTIFICATION SYSTEM FROM PHOTO IMAGES USING GLCM, MODIFIED ZERNIKE MOMENTS, AND PSO-BP APPLICATION

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Fathin Liyana Zainudin ◽  
Abd Kadir Mahamad ◽  
Sharifah Saon ◽  
Musli Nizam Yahya
Keyword(s):  
Back Propagation ◽  
Image Data ◽  
Back Propagation Neural Network ◽  
Zernike Moments ◽  
Bp Algorithm ◽  
Material Surface ◽  
Identification System ◽  
Hybrid Particle Swarm Optimization ◽  
System Input ◽  
Occurrence Matrix

In acoustic engineering, the types of material used in a room are basically one of the fundamental features that are essential in some of room acoustic parameters computation. This paper proposed an improved system to identify room material type from its surface photographic image. Data images of several room surfaces were collected for the system input. This improved system implements Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments for image extraction and hybrid Particle Swarm Optimization and back-propagation (PSO-BP) algorithm for classification. For comparison purpose, experiments using variations combination of GLCM and modified Zernike moments extraction as well as Levenberg-Marquardt, back-propagation neural network (BPNN), and PSO-BP algorithm were executed. By applying the proposed methods, the system accuracy increased around 30% compared to previous research. Moreover, the convergence attained during training was three times faster compared to BP algorithm. Thus using the new methods in identifying material surface images had positively improved the system in becoming more efficient and reliable.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

scholarly journals The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

ICT Express ◽  
2021 ◽  
Author(s):  
Fitri Utaminingrum ◽  
Syam Julio A. Sarosa ◽  
Corina Karim ◽  
Femiana Gapsari ◽  
Randy Cahya Wihandika
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Gray Level ◽  
Occurrence Matrix

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

A New Back-Propagation Neural Network Algorithm for a Big Data Environment Based on Punishing Characterized Active Learning Strategy

2013 ◽  
Vol 4 (4) ◽  
pp. 32-45 ◽  
Author(s):  
Qiuhong Zhao ◽  
Feng Ye ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Active Learning ◽  
Learning Strategy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Active Learning Strategy

This paper introduces the active learning strategy to the classical back-propagation neural network algorithm and proposes punishing-characterized active learning Back-Propagation (BP) Algorithm (PCAL-BP) to adapt to big data conditions. The PCAL-BP algorithm selects samples and punishments based on the absolute value of the prediction error to improve the efficiency of learning complex data. This approach involves reducing learning time and provides high precision. Numerical analysis shows that the PCAL-BP algorithm is superior to the classical BP neural network algorithm in both learning efficiency and precision. This advantage is more prominent in the case of extensive sample data. In addition, the PCAL-BP algorithm is compared with 16 types of classical classification algorithms. It performs better than 14 types of algorithms in the classification experiment used here. The experimental results also indicate that the prediction accuracy of the PCAL-BP algorithm can continue to increase with an increase in sample size.

Intelligent Restoration of Historical Parametric Geometric Patterns by Zernike Moments and Neural Networks

2021 ◽  
Vol 14 (4) ◽  
pp. 1-27
Author(s):  
Bita Hajebi
Keyword(s):  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Zernike Moments ◽  
Individual Element ◽  
Periodic Patterns ◽  
Geometric Patterns ◽  
Efficient Performance ◽  
Mathematical Algorithms ◽  
Dominant Structure

Historical Islamic ornaments include a fantastic treasury of geometric and mathematical algorithms. Inevitably, restoration of these ornaments in periodic patterns consisting of repeated elements has been faced following and substituting the other available similar ingredients instead of vanished parts. Still, the prediction of parametric, quasi, or non-periodic patterns, where components are not identical, needs to be carried out in a more challenging process than the periodic ones due to shape, scale, or angle of rotation alteration. Intelligent restoration could facilitate the forecasting of damaged parts in such geometric patterns that an algorithm has changed their geometric characteristics. In some architectural heritage, geometric patterns include a parametric algorithm like parametric patterns in the ceiling of Sheikh Lotfollahmosque in Isfahan, Iran, and the dominant structure of Persian domes Karbandi. In this article, the aim is to propose a new method for the smart restoration of the parametric geometric patterns in which, by having access to the image of the existing patterns, the vanished parts could be reconstructed spontaneously. Our approach is based on image processing by detecting boundaries of deterioration, finding every individual element, and extracting features of detected individual patterns via Zernike moments. The order of individual patterns starts from the farthest pattern to detected deterioration. Then by creating a time series, the Back-propagation neural network would be trained by extracted features, and the vanished patterns’ features could be predicted and reconstructed. Eventually, the reconstructed and real patterns are compared to determine differences between them by mean-squared error and to evaluate the performance of our method. To validate the process, a parametric geometric pattern is designed by the assumption that some parts are disappeared. The proposed method’s results, in this case, hold an efficient performance with the accuracy of 92.99%. Furthermore, Sheikh Lotfollah’s patterns and Naseredin Mirza mansion’s patterns as two real cases are tested by the proposed method, representing reliable and suitable performance results.

scholarly journals Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data

2019 ◽  
Vol 9 (21) ◽  
pp. 4620 ◽  
Author(s):  
Deng ◽  
Zhang ◽  
Cen
Keyword(s):  
Chemical Oxygen Demand ◽  
Back Propagation ◽  
Oxygen Demand ◽  
Back Propagation Neural Network ◽  
Hyperspectral Data ◽  
Bp Algorithm ◽  
A Value ◽  
Regression Relationship ◽  
Network Method ◽  
Regression Problems

This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results indicate there is a statistically significant correlation between COD and the hyperspectral data. The accuracy of the capsule network was compared with the results obtained from using a traditional back-propagation neural network (BP) method. The capsule network achieved superior accuracy with fewer iterations, compared with the BP algorithm. An R2 value of 0.78 was obtained against measured COD values retrieved using the capsule network method, compared with a value of 0.42 for the BP algorithm retrievals. This suggests the capsule network method has great potential to solve regression problems in the field of remote sensing.

Hybrid Bat-BP: A New Intelligent Tool for Diagnosing Noise-Induced Hearing Loss (NIHL) in Malaysian Industrial Workers

2013 ◽  
Vol 465-466 ◽  
pp. 652-656 ◽  
Author(s):  
Nazri Mohd Nawi ◽  
Muhammad Zubair Rehman ◽  
Mohd Imran Ghazali ◽  
Musli Nizam Yahya ◽  
Abdullah Khan
Keyword(s):  
Hearing Loss ◽  
High Frequency ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Bp Algorithm ◽  
Frequency Noise ◽  
Noise Induced Hearing Loss ◽  
Industrial Workers ◽  
Major Health

Noise-Induced Hearing Loss (NIHL) has become a major health threat to the Malaysian industrial workers in the recent era due to exposure to high frequency noise produced by the heavy machines. Recently, many studies have been conducted to diagnose the NIHL in industrial workers but unfortunately they neglected some factors that can play a major role in speeding-up NIHL. In this paper, a new Hybrid Bat-BP algorithm which is based on the trio combination of BAT based metaheuristic optimization, back-propagation neural network, and fuzzy logic is proposed to diagnose NIHL in Malaysian industrial workers. The proposed Hybrid Bat-BP will use heat, body mass index (BMI), diabetes, and smoking along with the century old audiometric variables (i.e. age, frequency, and duration of exposure) to better predict NIHL in Malaysian workers. The results obtained through Hybrid Bat-BP will be able to help us identify and reduce the NIHL rate in the workers with high accuracy.

scholarly journals Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn

2019 ◽  
Vol 7 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Isamadeen A. Khalifa ◽  
Subhi R.M. Zeebaree ◽  
Musa Ataş ◽  
Farhad M. Khalifa
Keyword(s):  
Frequency Domain ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Research Area ◽  
Input Image ◽  
Good Effect ◽  
Discrete Wavelet ◽  
Hidden Information ◽  
Left Behind ◽  
Occurrence Matrix

In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works.

scholarly journals A hybrid feature extraction approach for the detection of melanoma using neural network

2019 ◽  
Vol 10 (3) ◽  
pp. 1836-1840
Author(s):  
Asuntha A ◽  
Faizy ◽  
Rahul S ◽  
Akshay Menon ◽  
Pranjal ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Back Propagation ◽  
Texture Features ◽  
Back Propagation Neural Network ◽  
Number Of Patients ◽  
Detection Rule ◽  
Hybrid Feature Extraction ◽  
Dermoscopic Image ◽  
Occurrence Matrix

In spite of the gargantuan number of patients affected by melanoma every year, its detection at an early stage is still a challenging task. This paper illustrates a method which involves the combination of the existing ABCD (Involving symmetry, border, color, and diameter detection) rule and grey level co-occurrence matrix (GLCM) along with Local Binary Pattern (LBP) for identification of malignant melanoma skin lesion with greater accuracy. Several steps, such as image acquisition technique, pre-processing (RGB to HSV) techniques and segmentation processes are undertaken for the skin feature selection criteria to successfully determine the skin lesion's characteristic properties for classification. Texture features such as contrast, entropy, energy and homogeneity of the affected region is obtained using LBP and GLCM for discriminatory purposes of the two cases (melanoma and non-melanoma). Finally, the back propagation neural network (BPN) is used as the classifier to determine whether the dermoscopic image is benign or malignant.

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