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Parametric Analysis of CT-image-Preprocessing for Improved Performance of Post-processing Operation 10

Parametric Analysis of CT-image-Preprocessing for Improved P...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Shivwanshi, Resham Raj Nirala, Neelamshobha National Institute of Technology Raipur Department of Biomedical Engineering India

ISBN: (纸本)9781665439701

advanced technological tools in medical image analysis for disease detection and diagnosis are progressively coming into the utility of doctors and academicians due to various methodological evolution. Since the last three decades, various studies have been performed to achieve the state of the art predictive ability through early warning disease detection systems. After going through existing research work, it has been found that there is a lack of credibility in CT (computed tomography) image disease detection algorithms, which can be overcome by applying certain image processing and statistical analysis techniques. This article is made to describe a disparate approach in order to attain eminence in terms of lung disease diagnosis and detection. There are a huge amount of databases available online, but most of them encounter the issues of image noise and quality deterioration that further becomes the cause of irregularity and erroneous outcomes. The notion of this paper is to delineate an approach to pre-process input images and measure the quality of the given technique in order to choose better image operations and improve their visual information before analyzing them through a meticulous algorithm. An amalgamation of appropriate filters and image enhancement operations are also utilized to make clear insights of abnormality present inside of lung parenchyma. Furthermore, This study shows that the application of a high pass filter in the spatial domain improves the input image quality that is clearly identified by performing statistical analysis of output parameters. It is also observed that the otsu filtered image is more suitable to prepare the image for an efficient segmentation procedure. At last, it has been discussed that the overall approach in the form of pre-processing and its parameter estimation would not only help to assure quality enhancement of input image but also assist to run disease detection precisely in order to obtain reliable outcomes. ©

关键词： image enhancement

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1st international conference on Advances in signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st international conference on Advances in signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression Techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning algorithms for image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded Systems;image Segmentation Techniques and Optimization algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-II: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power Applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in Distributed Cyber Physical System;Classification of LPI Radar signals Using Multilayer Perceptron (MLP) Neural Networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP Applications;air Quality Monitoring System Based on Artificial Intelligence;an Improved Technique for Identification of Forgery image Detection Using Clustering Method;EEG signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control Methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor Networks Against Malicious Attacks Using Butterfly Method.

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2021 IEEE 8th Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2021

2021 IEEE 8th Uttar Pradesh Section International Conference...

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8th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2021

ISBN: (纸本)9781665409629

The proceedings contain 110 papers. The topics discussed include: a bottom-up approach applied to dependency parsing in Malayalam language;probabilistic modeling of human locomotion for biped robot trajectory generation;expert monitoring system for online sessions employing ResNet;multimodal meme sentiment analysis with image inpainting;advanced signal processing based condition monitoring of PMSM for stator-inter turn fault;coordinate-assisted routing in a biomedical sensor network with schedule-based dynamic sink;a review of neural machine translation based on deep learning techniques;and computation of secrecy and outages for hybrid FSO/RF system with modified switching.

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A Double-stream Exchange Transformer Network for Intrinsic image Decomposition

A Double-stream Exchange Transformer Network for Intrinsic I...

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2022 international conference on image processing and Media Computing, ICIPMC 2022

作者： Zhang, Feng Jiang, Xiaoyue Xia, Zhaoqiang Gabbouj, Moncef Peng, Jinye Feng, Xiaoyi Northwestern Polytechnical University School of Electronics and Information Xi'an China Tampere University Department of Computing Sciences Tampere Finland Northwestern University School of Information Science and Technology Xi'an China

ISBN: (数字)9781665468725

ISBN: (纸本)9781665468725

Intrinsic image decomposition separates the input image into several layers which reflect the attributes of the scene. In this paper, we present a double-stream exchange transformer network for intrinsic image decomposition, in which an independent and interrelated relationship is built between reflectance and shading. There are two core designs in the double-stream exchange transformer network (DSETNet). First, we propose a novel exchange transformer block, which performs the information exchange and reconstruction between reflectance and shading components in a window-based self-attention. Second, a residual structure is added into the exchange transformer block to form a residual exchange transformer block to eliminate artifacts caused by local window areas. We predict reflectance and shading constraint relationship between reflectance and shading is established through residual exchange transformer block. The evaluation results on two real and synthetic public datasets BOLD and ShapeNet show that the DSETNet achieves competitive results with other advanced algorithms. © 2022 IEEE.

关键词： Reflection

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Fast architecture for low level vision and image enhancement for reconfigurable platform 1

Fast architecture for low level vision and image enhancement...

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1st IEEE international conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Narasimhamurthy, Chiranjeevi Gubbi Kulkarni, Subhash Bangalore India

ISBN: (纸本)9781728157900

image processing architecture and algorithms are used for the purpose of improving the image quality and to extract complete information in more precise manner. The need to process in real time has led to implement them on the target device with real time constraints like power and area. To implement image processing algorithms and architecture with the help of high level languages needs more blocks of code apart from inefficient in terms of time constraint. The easiest way to deal with facilitating usage of Xilinx System Generator/XSG blocks, XSG is a tool where we can build our design using pre existing block level components which are captured by using Xilinx block set from library environment and implement as per user requirement. The main responsibility of system generator is Xilinx block sets that provides the package integration with MATLAB Simulink and helps in co- simulating FPGA hardware module. As per part application category, edge detection is a major essential application in medical field for proper diagnosis and correct treatments. Field-Programmable Gate Arrays (FPGAs) have become more popular computing platforms as target device for digital signal processing. The Zynq System on Chip (SOC) is a dual-processor platform with shared memory. This paper describes a fast architecture implementation of Sobel edge detection using the Zynq as a reconfigurable device. Our implementation is to merge both software and hardware using the Vivado and Zynq (SoC). As a result, our implementation is fast in terms of performance compare to the pre existing models. We make a comparison with other conventional edge detection techniques and show that the speed of operation of this design is much faster. This approach is tested over hardware or different gradient test vectors. Finally the algorithms are evaluated for the major essential images from the medical field to extract lasting effect in detecting the edges using gradient factor. The area and power factors are evalu

关键词： Simulink

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Design and implementation of personal online disk system based on DBN image recognition algorithm 3

Design and implementation of personal online disk system bas...

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3rd international conference on signal image processing and Communication, ICSIPC 2023

作者： Wen, Mingxing Chen, Tongfei Tang, Wenyang Chen, Elaine Pan, Zhihong School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China School of Art Media Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9781510670945

With the growth of the Internet and the age of information explosion, more and more people need network drives to store and share the resources they find online. While downloading files to a local hard drive can ensure data security, this also brings with it many inconveniences, such as the need to share with others using third-party websites or communication software, or the need to transfer files using a removable hard drive. As a result, personal network drives have emerged on the market to make data sharing easier by transferring files to a server. This project will retain the basic functions of a traditional network drive system, such as file uploading, downloading, and deletion, while taking into account the security of the data and using deep learning-based DBN algorithms for image recognition, using artificial intelligence and image recognition techniques to detect pirated and illegal image material. Through the combined application of the above technologies, an efficient personal web-disk system is designed and implemented. © 2023 SPIE.

关键词： image recognition

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Voice Command Recognition for Drone Control by Deep Neural Networks on Embedded System 8

Voice Command Recognition for Drone Control by Deep Neural N...

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8th international conference on Electrical and Electronics Engineering (ICEEE)

作者： Yapicioglu, Cengizhan Dokur, Zumray Olmez, Tamer Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781665440714

Speech recognition and its applications for controlling a system has been an important and attractive issue over the last few decades. Controlling electronic devices by speech commands allows us to manage systems quickly and easily since users would not need any additional information or remote controller. Being able to communicate a system by using speech commands also brings with the requirements of fast and accurate response. So, at the present, speech recognition algorithms are extensively performing on high performance computers. However, the improvements of system on a chip (SoC) board and deep neural network based algorithms, make it possible to execute such kind of programs on them. The proposed study defines a model for controlling a drone system by using Turkish speech directional commands in real time which is based on deep learning approaches using spectrogram images. At first, speech commands are detected in real time with the help of signal energy and zero crossing rate and these are converted to log spectrogram images. A CNN (three convolutional layers and a fully connected layer) structure is created and trained by feeding those images. Then, the trained model is moved to embedded board to achieve real time, low-cost performance. Speech commands are provided by the user instantaneously and transferred to the model as an input for decision. Then, algorithm decides which directional command is given by the user and desired operation is performed on the drone system. It is observed that, by using the proposed model, accuracies of 95.72% for offline dataset and 92,88% for real time classification are obtained.

关键词： speech recognition spectrogram convolutional neural networks deep learning speech processing image processing embedded systems

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A Deep Optimization image Deblurring Model Based on Internal and External Information 9

A Deep Optimization Image Deblurring Model Based on Internal...

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9th international conference on Communication, image and signal processing, CCISP 2024

作者： Tan, Yongqun Zhang, Lingli School of Mathematics and Statistics Chongqing Jiaotong University Chongqing400074 China Chongqing University of Arts and Sciences Chongqing Key Laboratory of Statistical Optimization and Complex Data Chongqing402160 China Chongqing University of Arts and Sciences Chongqing Key Laboratory of Group & Graph Theories and Applications Chongqing402160 China

ISBN: (纸本)9798350356656

In recent decades, there has been a notable focus on addressing image deblurring problem. Different deblurring algorithms integrate prior information into the image deblurring model, and the effectiveness of the prior information directly determines the performance of the deblurring. This paper presents a deep optimization image deblurring model based on internal and external information, which is joint with total variation, non-local self-similarity and convolutional neural network. The corresponding algorithm of the presented model utilizes half quadratic splitting method to decouple the fidelity and regularization terms, which can be solved through the sub-problems, respectively. Numerical experiments show that the presented algorithm can outperform the existing algorithms both in preserving the edges and assessment indicators (PSNR, RMSE, and SSIM). © 2024 IEEE.

关键词： Optimization algorithms

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image defogging system and algorithm research based on MATLAB platform

Image defogging system and algorithm research based on MATLA...

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2021 international conference on advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Chen, Yongchang Sun, Xiangrui Qiu, Xiaofeng Wang, Xinyan Department of Telecommunications Tibet University Tibet Lhasa850000 China Department of Civil Engineering and Water Conservancy Tibet University Tibet Lhasa850000 China

Since the Reform and Opening up, the characteristics of China's high-quality economic development have become more distinctive, the adjustment of economic and social structure has been continuously optimized, the kinetic energy has been continuously enhanced, and the degree of industrialization has been significantly improved. However, it has also caused some environmental problems, and haze pollution is one of them. The haze weather with low visibility has a certain impact on people's daily travel, transportation and navigation. Because the images taken under the haze condition will be degraded to varying degrees and the image details will be lost, the effect of identifying and processing the degraded images in the later stage will be reduced. Therefore, in order to clear and restore the degraded image, we developed a defogging system based on MATLAB software. The system mainly uses histogram equalization, MSR and dark channel a priori defogging algorithms to restore fog degraded images. The results indicate that various defogging algorithms are able to enhance the clarity of the picture to varying degrees. The successful implementation of the defogging system lays a theoretical foundation for the in-depth research and application of fog image defogging processing technology, and has good technical operability. © Published under licence by IOP Publishing Ltd.

关键词： Restoration

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Moment Transform-Based Compressive Sensing in image processing 28th

Moment Transform-Based Compressive Sensing in Image Processi...

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28th international conference on Systems, signals and image processing (IWSSIP)

作者： Kalampokas, Theofanis Papakostas, George A. Int Hellen Univ Dept Comp Sci MLV Res Grp Kavala Greece

ISBN: (纸本)9783030968786;9783030968779

Over the last decades, images have become an important source of information in many domains, thus their high quality has become necessary to acquire better information. One of the important issues that arise is image denoising, which means recovering a signal from inaccurately and/or partially measured samples. This interpretation is highly correlated to the compressive sensing theory, which is a revolutionary technology and implies that if a signal is sparse then the original signal can be obtained from a few measured values, which are much less, than the ones suggested by other used theories like Shannon's sampling theories. A strong factor in Compressive Sensing (CS) theory to achieve the sparsest solution and the noise removal from the corrupted image is the selection of the basis dictionary. In this paper, Discrete Cosine Transform (DCT) and moment transform (Tchebichef, Krawtchouk) are compared in order to achieve image denoising of Gaussian additive white noise based on compressive sensing and sparse approximation theory. The experimental results revealed that the basis dictionaries constructed by the moment transform perform competitively to the traditional DCT. The latter transform shows a higher PSNR of 30.82 dB and the same 0.91 SSIM value as the Tchebichef transform. Moreover, from the sparsity point of view, Krawtchouk moments provide approximately 20-30% more sparse results than DCT.

关键词： Compressive sensing image moments Denoising Tchebichef moments Krawtchouk moments DCT

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