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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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Sparse noise minimization in image classification using Genetic Algorithm and DenseNet 5

Sparse noise minimization in image classification using Gene...

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5th IEEE Annual international conference on Information Communications Technology and Society (ICTAS)

作者： Mienye, Ibomoiye Domor Ainah, Priye Kenneth Emmanuel, Ikiomoye Douglas Esenogho, Ebenezer Univ Johannesburg Dept Elect & Elect Engn Johannesburg South Africa Niger Delta Univ Dept Elect & Elect Engn Wilberforce Isl Nigeria Univ Salford Sch Comp Sci & Engn Salford Lancs England Univ Johannesburg Ctr Telecommun Dept Elect & Elect Engn Johannesburg South Africa

ISBN: (纸本)9781728180816

Noise handling is a critical aspect of image processing, which can significantly affect the accuracy of classification and recognition algorithms. In this paper, we propose a technique for improved noise handling in sparse input feature maps where the noise signal is also sparse. The signal-noise relationship is formulated as an optimization problem which is solved by a genetic algorithm. The genetic algorithm is applied to optimize the setting of a non-convexity parameter which yields a more accurate image sparse matrix. The resulting feature map is then classified using a densely connected convolutional network (DenseNet). Lung computed tomography images were used for the experiments. The proposed approach achieves better performance when the classification results are compared with a case in which the input signal has not been denoised using the proposed approach.

关键词： ADMM DenseNet genetic algorithm lung cancer sparse noise

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Face and Face Mask Detection Using Convolutional Neural Network 1

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13th international conference on Intelligent Human Computer Interaction (IHCI)

作者： Zainal, Muhammad Mustaqim Ambar, Radzi Abd Wahab, Mohd Helmy Poad, Hazwaj Mhd Abd Jamil, Muhammad Mahadi Choon, Chew Chang Univ Tun Hussein Onn Malaysia Fac Elect & Elect Engn Dept Elect Engn Batu Pahat 86400 Johor Malaysia Univ Tun Hussein Onn Malaysia Computat Signal Image & Intelligence Res Focus Gr Batu Pahat 86400 Johor Malaysia

ISBN: (数字)9783030984045

ISBN: (纸本)9783030984045;9783030984038

The COVID-19 outbreak has posed a severe healthcare concern in Malaysia. Wearing a mask is the most effective way to prevent infections. However, some Malaysians refuse to wear a face mask for a variety of reasons. This work proposes a real-time face and face mask detection method using image processing technique to promote wearing face mask. Haar Cascade is used for the face detection to extract the features of the human faces as a method of approach. On the other hand, the face mask detection utilizes convolutional neural network (CNN) to train a model using the MobileNetV2 training model designed using Python, Keras and Tensorflow. OpenCV package was used as the interface for the algorithms to be connected to a web camera. Based on the performance metric calculation of detection rate analysis of the experimental results, the face detection rate is at 90% true and 10% false detection, which shows very good detection rate. Furthermore, the training accuracy and validation accuracy for the face mask detector are efficiently near to 1.0, proving a steady accuracy over the time. Training loss and validation loss are almost near to zero and decreasing over time, reassuring the algorithm performance is accurate and efficient for a datasets of 4000 images.

关键词： Face mask detection Face detection image processing Convolutional neural network

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image super-resolution method based on generative adversarial network 4

Image super-resolution method based on generative adversaria...

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4th international conference on advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Lijun, Yao Xiaoming, Zhu Fan, Luo Gang, Sun Zhou, Chen Jing, Yan Min, Zhou Yongchang, Cai Lingling, Wang Zelong, Cao Lan, Peng Fengqing, Bai Zifang, You Hongqiu, Xiao Pengjian, Li Zhisheng, Lv Qiang, Tang Changzhutan Tobacco Logistics Co.Ltd Ltd. of Hunan Province ChangSha410000 China Hunan Tobacco Company Chagnsha410000 China Hunan Tobacco Workers Training Center Xiangtan411100 China Hunan Normal University College of Information Science and Engineering ChangSha410081 China Hunan Normal University College of Engineering and Design ChangSha410081 China

ISBN: (纸本)9781665415965

Although deep convolutional neural networks have made breakthroughs in the accuracy and speed of single-image super-resolution, there are still many unsolved problems: firstly, How to refine the texture problem when performing super-resolution processing at a larger magnification ratio. Secondly, the existing convolutional neural network image super-resolution algorithms are prone to overfitting and insufficient convergence of the loss function. Aiming at two problems, an image super-resolution method based on generative adversarial network is proposed. The feature map is spatially transformed on the network to solve the problem of refined texture, combined with CycleGAN and SRGAN, the network structure is improved and the loss function is optimized, and the SRCICGAN algorithm is proposed to restore the four times down-sampled image to solve the loss function problem. The experiment is compared with the latest six methods on three data sets. The PSNR and SSIM indicators are 1.92% and 5.49% higher in the Flickr2K data set, respectively, and better visual effects can be obtained in terms of detailed texture. © 2021 IEEE.

关键词： Generative adversarial networks

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Satellite Attitude Estimation Based on ISAR image Sequence Interpretation

Satellite Attitude Estimation Based on ISAR Image Sequence I...

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2021 CIE international conference on Radar, Radar 2021

作者： Wang, Jiadong Li, Yachao Song, Ming Academy of Advanced Interdisciplinary Research Xidian University Xi'an China Xidian University National Laboratory of Radar Signal Processing Xi'an China Beijing Institute of Space Long March Vehicle Beijing China

ISBN: (纸本)9781665498142

This paper presents a novel approach for the estimation of satellite attitude based on ISAR image interpretation. The solar panel of the satellite is segmented from the initial ISAR image using Pix2pixGAN network, and morphological method is used for edge extraction according to the parallelogram characteristics of the solar panel. According to the projection process of ISAR imaging, the optimization function for the 3D absolute attitude estimation of the satellite solar panel is established, and the attitude of the satellite is solved by iteration. The experimental results show the effectiveness of the proposed method. © 2021 IEEE.

关键词： attitude estimation inverse synthetic aperture radar (ISAR) ISAR image interpretation Satellite targets

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A Sensitive Nanothermometer Based on DNA Triplex Structure 1

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17th international conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Chen, Zhekun Hu, Yingxin Xie, Chun Chen, Kuiting Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA has been used to construct a variety of nanoscale thermometers due to its unique thermodynamic properties. It has proven to be one of the most promising, yet challenging aspects of DNA-based thermometers, to obtain significant signal changes when the temperature changes slightly. Here, we propose a strategy to construct nanoscale thermometers with sensitive temperature responses based on DNA triplex. The thermometers consist of a CT-rich DNA strand and stabilizing strands, whose conformations vary with temperature. By adjusting the sequence design or introducing mismatch bases in stabilizing strands, the temperature response interval of the thermometer can be reduced to 7 ∘C, which lead to higher sensitivity compared to the interval of 12–15 ∘C for conventional designs. The temperature responses of the thermometer are characterized by fluorescence experiments. The fluorescence kinetics experiments demonstrate the good repeatability. The design of the nanothermometer will be helpful for constructing advanced nanosystem with sensitive temperature responses as well as long service times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermometers

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image Electronic Evidence Screening Based on Improved SSD 6

Image Electronic Evidence Screening Based on Improved SSD

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Liu, Yafei Jiang, Liehui Liu, Tieming Zhang, Youwei Zhengzhou University Zhongyuan Network Security Research Institute Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738143705

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm. © 2021 IEEE.

关键词： Multilayer neural networks

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Hexagonal image Compressionusing Singular Value Decomposition in Python 2

Hexagonal Image Compressionusing Singular Value Decompositio...

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2nd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2021

作者： Varghese, Prathibha Selva Saroja, G. Arockia Electronics and Communication Department Noorul Islam Centre for Higher Education Tamil Nadu India Dept. Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (纸本)9781728171364

With the advent of multimedia technologies in last two decades, there is a widespread need for efficient storage and transmission of data. Dealing with the vast information interchangein this digital era, image compression for reduction in byte size of graphics image file without loss of image quality to an acceptable level becomes the large interest area. Inspired from the biological models of human fovea, hexagonal image processing has gained a lot of attention in artificial intelligence era that deals with the application of image processing system that combines the benefits of biologically motivated structures. In this paper a singular value decomposition (SVD) over hexagonal image compression which is a missing stone in computer vision which provides higher packing density, higher angular symmetry and uniform connectivity. Due to lack of developments in hexagonal imaging devices, different resampling methods like alternate pixel shift method, half pixel shift method, pseudo hexagonal pixel method for sourcing hexagonal images. SVD is one of the powerful cutting-edge technology for image compression algorithms. SVD based image compression is performed on hexagonal grid and is compared with square grid using different parameters like compression ratio, compression size, PSNR and MSE using PYTHON’s SVD function. SVD based hexagonal image achieves the goal of compression by preserving good image quality at higher compression ratios, high computational efficiency, provides low mean square error (MSE), acceptable compression size depending on application and high peak to signal ratio (PSNR). © 2021 IEEE

关键词： Singular value decomposition

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ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Computing at the Edge 43

ApproxBioWear: Approximating Additions for Efficient Biomedi...

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43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Kanani, Alish Bhattacharjya, Rajat Banerjee, Dip Sankar Indian Inst Technol Jodhpur Elect Engn Dept Karwar India Parallel Wireless Inc Nashua NH USA Indian Inst Technol Jodhpur Comp Sci & Engn Dept Karwar India

ISBN: (纸本)9781728111797

Wearables in the biomedical domain have been of extensive use in the current era. Given the importance of wearable computing, it has become necessary to innovate on enhancing hardware efficiency. The domain of approximate computing offers a conclusive method to lower area, power and delay in hardware in addition to a marginal loss in accuracy. In this paper, we investigate ApproxBioWear, a technique which enables the use of approximate computing for efficient biomedical wearable computing at the edge. The methodology involves approximating additions during the functional stages of an error-resilient biomedical signal processing algorithm and determining the application accuracy. Upon evaluating the Pan-Tompkins algorithm, which is used to detect QRS peaks in ECG signals, it is observed that the ApproxBioWear approach reduces the power consumption and chip area by 19.27% and 19.71% respectively on an average with a marginal loss in accuracy.

关键词： Power demand Filtering Wearable computers image edge detection Approximate computing signal processing algorithms Electrocardiography

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Incremental feedback learning mechanism for highly efficient automatic image segmentation with feature coupling

Incremental feedback learning mechanism for highly efficient...

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2021 IEEE international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Bhagwat, Manisha Gupta, Gyanendra Ambhaikar, Asha Kalinga University Dept.of CSE Raipur India

ISBN: (纸本)9781665435215

Segmentation of images is a pre-requisite for all modern high efficiency image processing system. In order to perform this task, various application specific algorithms are designed and deployed by image processing experts. These systems work on a context-specific mode, wherein all segmentation outputs are restricted by context of images for which the system is trained. In order to deploy these systems to other domains, complex tuning and optimization operations are needed. This reduces applicability of these system models for real time use cases, where general purpose segmentation methods are needed. These use cases include but are not limited to, scene classification, satellite image classification, yield prediction, traffic detection, etc. Moreover, general purpose image segmentation models work effectively only under a pre-set types of application scenarios, and need to be constantly trained in order to improve their applicability. Retraining these systems increases computational costs, and requires large training and testing delays. In order to remove these drawbacks, in this text an incremental feedback learning mechanism with feature coupling is proposed. The proposed model uses a wide variety of image segmentation methods that analyze colour, texture shape information;and map it with relevant image features. These features are traced along with segmentation quality metrics like peak signal to noise ratio (PSNR), figure of merit (FOM), minimum mean squared error (MMSE), and probabilistic random index (PRI) in order to evaluate the best segmentation algorithm. These features are classified using an ensemble classification model for selection of the most efficient segmentation method that maximizes PSNR, PRI while minimizing MMSE. Parametric evaluation suggests that the proposed model is able to improve segmentation accuracy by 8%, and reduce false alarm rate by 15% when compared with standard automatic segmentation models. © 2021 IEEE.

关键词： image segmentation

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Research on image enhancement algorithm based on deep neural network algorithm

Research on image enhancement algorithm based on deep neural...

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2021 international conference on Artificial Intelligence and Information Technology, ICAIIT 2021

作者： Lin, Qiang wen Li, Liu mei Academic affaires office Guangzhou College of Commerce Guangdong Guangzhou511363 China

image denoising and enhancement technology plays an important role in remote sensor image processing. Due to the low brightness, signal-to-noise ratio and contrast of image processing, the traditional remote sensor is limited in obtaining object information. Based on the principle of biological segmentation algorithm, neural network algorithm technology can carry the advantages of image evaluation algorithms such as PSNR, IFC and SSIM, through the end-to-end training of nonlinear infinite approximation, and carry out the research of image fitting low light level image enhancement and denoising. On this basis, the effectiveness of the research results is evaluated. The results show that compared with the traditional remote sensor imaging technology, the PSNR value of the image constructed by the depth neural network algorithm is significantly improved by about 15.2401, the IFC value is improved by about 0.4523, and the SSIM value is improved by about 0.4500, which improves the low light level imaging quality to a higher extent, and provides excellent technical guidance for the remote sensor to obtain the object information at night. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Deep neural networks

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