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Using wavelet transform to evaluate single-shot phase measuring deflectometry data 43

Using wavelet transform to evaluate single-shot phase measur...

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conference on applications of Digital image processing XLiiI

作者： Liang, Hanning Sauer, Tomas Faber, Christian Landshut Univ Appl Sci Lurzenhof 1 Landshut Germany Univ Passau Innstr 41 Passau Germany

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

Phase measuring deflectometry (PMD) is a well-established method for determining the topography of specular freeform surfaces. A disadvantage of the classical PMD method, however, is the sequential measurement process it requires at least six camera images of phase-shifted sinusoidal fringe patterns for one measurement. Therefore, for moving objects in industrial production, as well as for non-fixable objects such as the human cornea, the classical PMD evaluation is not suitable any more. To overcome this problem, single-shot methods using single-sideband demodulation have been presented, which allow for a deflectometric measurement based on just one single image capture. However, this kind of evaluation does not work for complex surface geometries that result in broadband fringe patterns, since the phase is only considered globally in the Fourier space. A new single-shot evaluation method for the phase determination using the Continuous wavelet Transform (CWT) is presented. The advantage of the wavelet transform is that the signal can be evaluated locally in both spatial and frequency space, making it possible to measure even complex reflective surfaces in motion. First measurement results are shown and compared to the classic phase-shifting evaluation for a non-moving object. Furthermore, limits and possible enhancements of this new method are discussed.

关键词： Deflectometry Industrial Metrology 3D Metrology Specular Surfaces Single-Shot Measurement

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A color image watermarking in the frequency domain using a teaching-learning optimization algorithm

A color image watermarking in the frequency domain using a t...

引用

International conference on image Information processing

作者： Nikhlesh Kumar Badoga Raman Kumar Goyal Rajesh Mehta Thapar Institute of Engineering & Technology Patiala India

ISBN: (纸本)9781665433624

This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.

关键词： image coding PSNR Transform coding Watermarking Color Robustness Discrete wavelet transforms

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Quadratic Segmentation Algorithm Based on image Enhancement

Quadratic Segmentation Algorithm Based on Image Enhancement

引用

7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Jiang, Ying Dong, Heng Fan, Yaping Wang, Yu Gui, Guan Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Networ Nanjing 210003 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

Due to the indefinite position of the characters in the invoice and the difference of the color shades, which greatly increases the difficulty of intelligent identification and thus it is difficult to meet practical applications. In order to solve this problem, this paper proposes a quadratic segmentation algorithm based on image enhancement. Specifically, we firstly enhance the color of the image based on gamma transformation and then separate the machine-printing character from the blank invoice based on the color analysis of the machine-printing character. Then according to the open operation in the image processing field and the boundingRect algorithm, the pixel information of the machine-playing character is obtained, which is convenient for getting the character information. The algorithm can achieve effective extraction of machine-playing characters and also reduce the difficulty of invoice identification and improving the accuracy of invoice identification. Simulation results are given to confirm the proposed algorithm.

关键词： Quadratic segmentation algorithm image processing Invoice identification Color determination image processing

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A survey of various types of adaptive filters based on the signal and image processing applications

A survey of various types of adaptive filters based on the s...

引用

2020 International conference on System, Computation, Automation and Networking, ICSCAN 2020

作者： Elizabeth Caroline, B. Vidhya, J. Ragasudha, I. Electronic and Communication Engineering IFET College of Engineering Villupuram India

ISBN: (数字)9781728162027

ISBN: (纸本)9781728162027

Now a days, optimization filters are the major technique to decrease the noise in the signal, to eliminate the PLI from the signal processing, to produce the quality signal with maximum accuracy and for system identification. This paper deals with the survey on Finite Impulse Response (FIR), Infinite Impulse Response (iiR), Discrete wavelet Transform (DWT), Normalized Least Mean Square (NLMS) adaptive filter. However the performance of the filter is mandatory for analyzing the noise effect in all filters. The studies of the filter parameter like signal to Noise Ratio (SNR), Peak signal to Noise Ratio (PSNR), Mean Square Error (MAE), Mean Square Error (MSE) etc., were compared with all the types of above mentioned filter. The major aim of this review paper is to study the different adaptive filter's performance and also analyses the corresponding parameters to improve the quality and to eradicate the noise or unwanted features present in the signals such as Power Line Interference (PLI) in ECG signal, touch error in the PhotoPlethysmoGraphy (PPG) signal and soon on. © 2020 IEEE.

关键词： Finite impulse response filters iiR filters Adaptive filters Filtering algorithms Band-pass filters Electrocardiography Discrete wavelet transforms

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CCTV based system for detection of anti-virus masks 24

CCTV based system for detection of anti-virus masks

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24th IEEE conference on signal processing: Algorithms, Architectures, Arrangements, and applications (IEEE SPA)

作者： Podbucki, Kacper Suder, Jakub Marciniak, Tomasz Dabrowski, Adam Poznan Univ Tech Fac Control Robot & Elect Engn Inst Automat Control & Robot Div Elect Syst & Signal Proc Jana Pawla II 24 PL-60965 Poznan Poland

ISBN: (纸本)9788362065394

This paper presents the use of neural networks to detect people with anti-virus masks. The algorithm allows to determine if a person has a correctly worn mask (covered nose and mouth). The use of neural networks has been compared with typical Haar cascade frontal face solutions available in the OpenCV library. The proposed solution has been checked for efficiency and precision, as well as for the minimum resolution requirements of the resulting facial image. The software works on still images as well as on video sequences from computer webcams and CCTV cameras.

关键词： COVID-19 neural networks convolutional neural networks anti-virus masks mask detection vision system OpenCV Python programming

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An Attention Based wavelet Convolutional Model for Visual Saliency Detection

An Attention Based Wavelet Convolutional Model for Visual Sa...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Reshmi S. Bhooshan Suresh K College of Engineering Trivandrum Thiruvananthapuram India Govt. Engineering College Thiruvananthapuram India

ISBN: (纸本)9781728176055;9781728176062

The emergence of deep neural architectures greatly enhanced the accuracy of salient region detection algorithms that plays a vital role in computer vision applications. However, the accurate extraction of regions with fine boundaries still remains as a challenge. In this work, an attention based wavelet Convolutional Neural Network (WCNN) is implemented that efficiently extracts the spatial, spectral and semantic features of the image in multiple resolution and it turns out to be suitable for locating the visually salient regions. Further enhancement of the fine boundaries of the predicted map is made possible by the inclusion of a combinational loss function of balanced cross entropy loss, SSIM loss and edge loss. The effectiveness of the method is evaluated using three benchmark datasets and the results shows better performance achieving a minimum Mean Absolute Error (MAE) of 0.032 and maximum F-measure of 0.938.

关键词： Visualization Convolution image edge detection Semantics Benchmark testing Feature extraction Entropy

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Detection of White Gaussian Noise and Its Degree in image processing Using Generative Adversarial Nets

Detection of White Gaussian Noise and Its Degree in Image Pr...

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7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Hua, Wentao Xiong, Jian Yang, Jie Gui, Guan Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

Since the theory of generative adversarial nets (GANs) put forward in 2014, various applications based on GANs have been developed. Most of the applications focused on generator network (G) of GANs to solve the daily challenges. However, rare of them had been aware of the great value of the discriminator network (D). In this paper, we propose a new method of detecting white Gaussian noise and its degree by the discriminator of generative adversarial nets. The results of our experiments show the feasibility of detecting white Gaussian noise (WGN) and evaluating its degree through generative adversarial nets.

关键词： Generative adversarial nets Gaussian white noise detection Deep learning

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A Framework For Single image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

A Framework For Single Image Dehazing Using DWT Based Cross ...

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International conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Sanjay K. Dwivedi Manoj Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (纸本)9781665474177

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.

关键词： Training Measurement Learning systems PSNR Benchmark testing Discrete wavelet transforms image restoration

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An Adaptive Iteratively Weighted Half Thresholding Algorithm for image Compressive Sensing Reconstruction

An Adaptive Iteratively Weighted Half Thresholding Algorithm...

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7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Peng, Qiwei Yu, Tongwei Luo, Wang Li, Tong Zhao, Gaofeng Fan, Qiang Hao, Xiaolong Wang, Peng Li, Zhiguo Zhong, Qilei Feng, Min Yu, Lei Yan, Tingliang Liu, Shaowei Xia, Yuan Han, Bin Dai, Qibin Li, Yunyi Zhang, Zhenyue Gui, Guan NARI Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (纸本)9789811365041;9789811365034

The L-1/2 regularization has been considered as a more effective relaxation method to approximate the optimal L-0 sparse solution than L-1 in CS. To improve the recovery performance of L-1/2 regularization, this study proposes a multiple sub-wavelet-dictionaries-based adaptive iteratively weighted L-1/2 regularization algorithm (called MUSAI-L-1/2), and considering the key rule of the weighted parameter (or regularization parameter) in optimization progress, we propose the adaptive scheme for parameter lambda(d) to weight the regularization term which is a composition of the sub-dictionaries. Numerical experiments confirm that the proposed MUSAI-L-1/2 can significantly improve the recovery performance than the previous works.

关键词： L-1/2 regularization Multiple sub-wavelet-dictionaries Enhancing sparsity Adaptive Iteratively weighted

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A Hybrid Multi-focus image Fusion Technique using SWT and PCA 10

A Hybrid Multi-focus Image Fusion Technique using SWT and PC...

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10th International conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Tyagi, Tushar Gupta, Parth Singh, Prabhishek Amity Univ Uttar Pradesh Amity Sch Engn & Technol Dept CSE Noida India

ISBN: (纸本)9781728127910

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. it is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal image Quality Index), and PSNR (Peak signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

关键词： image fusion SWT PCA VSN

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