A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

A Coded Digital Watermarking Recognition Using Neural Network Method

2013 ◽  
Vol 284-287 ◽  
pp. 2961-2964
Author(s):  
Chih Ta Yen ◽  
Ing Jr Ding ◽  
Zong Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. Although watermarks possess advantageous secrecy and robustness, environmental interference in the image propagation through the Internet is inevitable and, certainly, human-based image modification can also destroy the watermark. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh-Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First, we used a low-pass filter and median filter to remove noise interferences. Although these filters can suppress noises, watermarked images remain unidentifiable when the noise interferences strongly. Finally, we used a back-propagation neural network algorithm to filter noises, obtaining results that exceeded our expectations. We removed nearly all noise and recovered the originally embedded watermarks of Walsh-Hadmard codes.

Design and Realization of the SMT Product Character Recognition System

2010 ◽  
Vol 139-141 ◽  
pp. 1736-1739
Author(s):  
Hui Huang Zhao ◽  
De Jian Zhou ◽  
Zhao Hua Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Median Filter ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Network Module ◽  
Pass Filter ◽  
Low Pass Filter

We present an approach to recognizing characters in surface mount technology (SMT) product. An improved SMT product character recognition method is proposed which can obtain a good recognition rate. Some appropriate image processing algorithms, such as Gray processing, Low-pass Filter, Median Filter, and so on, are used to eliminate the noise. Then, Character image is obtained after character segmentation and character normalization. Finally, a three-layer back propagation (BP) neural network module is constructed. In order to improve the convergence rate of the network and avoid oscillation and divergence, the BP algorithm with momentum item is used. As a result, the SMT product character recognition system is developed. Experimental results indicate that the proposed character recognition can obtain satisfactory character-recognition rate and the recognition rate reached over by 98.6% when the hidden layer of BP neural network module has 20 nodes.

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

Implication of Parameter Values on Low-Pass Filter Assisted Digital Back Propagation for DP 16-QAM

2013 ◽  
Vol 25 (10) ◽  
pp. 917-920 ◽  
Author(s):  
Ying Gao ◽  
Jian Hong Ke ◽  
John C. Cartledge ◽  
Kang Ping Zhong ◽  
Scott S.-H. Yam
Keyword(s):  
Back Propagation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Parameter Values

LICENSE-PLATE RECOGNITION USING DWT AND NEURAL NETWORK

2006 ◽  
Vol 04 (04) ◽  
pp. 601-615 ◽  
Author(s):  
TIAN-DING CHEN
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Structure Modification ◽  
New Approach ◽  
Low Pass

This paper presents a new approach for license-plate recognition using Discrete Wavelet Transform (DWT) and Plastic Perception Neural Network (PPNN). It accomplishes the preliminary license-plate localization by applying low-pass wavelet coefficients. Since the amount of data reduces to 1/4, this approach saves a lot of running time, simplifies computational complexity, and economizes memory usage. It adopts the LL and HH sub-bands, which come from a two-dimensional Haar DWT to implement the localization and segmentation for license plates. The proposed methodology provides high accuracy for locating a license plate from an image, and has a high tolerance for license plate displacement in the images. Back-Propagation Neural Network (BPNN) has the advantage of anti-noise and anti-distortion, but the problems of traditional BPNN are a longer learning period, iterations are not prone to convergence, and local minimum. The proposed methods combine the parallel distributive process concept with the BPNN structure modification to solve the above problems. This paper also utilizes PPNN to solve taking position, scale and rotation of the license-plate recognition.

scholarly journals A Modified Conjugate Gradient Formula for Back Propagation Neural Network Algorithm

2009 ◽  
Vol 5 (11) ◽  
pp. 849-856 ◽  
Author(s):  
Abbas Y. Al Bayati ◽  
Najmaddin A. Sulaiman ◽  
Gulnar W. Sadiq
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Klasifikasi Kanker Paru Paru menggunakan Naïve Bayes dengan Variasi Filter dan Ekstraksi Ciri GLCM

2021 ◽  
Vol 11 (2) ◽  
pp. 256
Author(s):  
Mohtar Yunianto ◽  
Soeparmi Soeparmi ◽  
Cari Cari ◽  
Fuad Anwar ◽  
Delta Nur Septianingsih ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Median Filter ◽  
Naïve Bayes ◽  
Gray Level ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Otsu Thresholding ◽  
Low Pass ◽  
Occurrence Matrix ◽  
High Pass

<p class="AbstractText">Telah berhasil dilakukan klasifikasi kanker paru-paru dari 120 data citra CT Scan. Pada penelitian, proses preposisi dimulai dengan variasi filtering yaitu low pass filter, median filter, dan high pass filter. Segmentasi yang digunakan yaitu Otsu Thresholding yang kemudian teksturnya akan diekstraksi menggunakan fitur Gray Level Co-occurrence Matrix (GLCM) dengan variasi arah sudut. Hasil dari ekstraksi GLCM dijadikan database yang akan menjadi dataset untuk pengklasifikasian citra menggunakan klasifikasi naïve bayes. Hasil dari penelitian dengan 12 buah variasi diperoleh hasil variasi terbaik adalah median filter dengan arah sudut GLCM 0° menunjukkan tingkat akurasi yang paling tinggi sebesar 88,33 %.</p>

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