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19th Pacific Rim International conference on Artificial Intelligence (PRICAI)

作者： Jin, Tian-Hu Tao, Yan-Yun Guo, Jia-Ren Huang, Zi-Hao Zheng, Jian-Yin Soochow Univ Sch Rail Transportat Suzhou 215000 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9783031208676;9783031208683

Single image dehazing is a challenging ill-posed problem. The key to achieve haze removal is to estimate an accurate medium transmission map. By redefining the atmospheric scattering model, we obtain a new transmittance map scattering model, haze image and haze-free image, derive the medium transmission as a function of the scene intensity only, also deduce a priori condition that the intensity of hazy image is higher than that of haze-free image, and propose a lightweight image dehazing neural network (Intensity neural network, I-Net) based on estimating medium transmission map by intensity. I-Net uses a convolutional neural network (CNN) as the backbone, and takes the intensity of hazy image as the input, and outputs the intensity of haze-free image, the medium transmission map and the original haze-free image, also joints the priori condition derived previously to obtain a more accurate medium transmittance map. In this paper, the dehazing algorithm obtains the intensity of haze-free image through I-Net, derives the transmittance map using the functional relationship between transmittance and scene intensity, and finally recovers the original haze-free image through the transmittance map scattering model. The experimental results show that our dehazing results are clearer and more natural. The subjective and objective evaluations show that our image dehazing algorithm can achieve better dehazing results than traditional algorithms, and outperforms current advanced algorithms in terms of Peak Signal to Noise Rate (PSNR) and Structure Similarity Index Measurement (SSIM).

关键词： Single image dehazing Atmospheric scattering model Medium transmission map Deep CNN

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Comparison of image Feature Detection algorithms 9

Comparison of Image Feature Detection Algorithms

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9th International conference on Dependable systems and Their Applications, DSA 2022

作者： Xu, Fan Liu, Xia Cui, Yanli Yan, Mingdie Lai, Zhongyuan Intelligent Manufacturing of JianghanUniversity Hubei Wuhan China Artificial Intelligence of JianghanUniversity Hubei Wuhan China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

The key of image processing is to extract feature points and feature vectors by appropriate methods. In order to analyze the application effect of common feature extraction methods in different scenarios, this paper adopts SIFT, BRISK, ORB, KAZE and AKAZE methods to rotate, adjust brightness, blur and zoom images based on two data sets. The operating efficiency, the ratio of feature points before and after operation, the best matching rate and accuracy rate of feature points pair of the five methods are analyzed, and the suggestions for selecting reasonable feature detection methods for different application scenarios are given. © 2022 IEEE.

关键词： Feature extraction

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Sign Language Translation Using image processing to Audio Conversion

Sign Language Translation Using Image Processing to Audio Co...

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IEEE International conference on Intelligent Techniques in Control, Optimization and Signal processing (INCOS)

作者： V Subashini B Someshwaran S Sowmya S Ashwin Kumar Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals outside of these communities struggle to comprehend and utilize sign language effectively. A ground-breaking system known as hand gesture recognition using image processing to audio conversion has emerged to address this issue. This innovative system aims to develop an application capable of translating sign language and hand gestures into text and audio, thereby facilitating communication between deaf and mute individuals and the wider society. This system enables the detection and classification of hand gestures by employing computer vision techniques, such as CNN algorithms for image processing and the MediaPipe framework for hand gesture identification in real-time video streams. Subsequently, the system utilizes audio signals to provide immediate feedback by converting the detected gestures into corresponding sounds. The feature extraction CNN algorithm is implemented in Python, while the execution takes place on a Raspberry Pi connected to an external camera utilizing OpenCV libraries. Through this comprehensive approach, the system endeavours to bridge the communication gap and enhance the inclusion of deaf and mute individuals in various social settings.

关键词： Sign language image recognition Signal processing algorithms Streaming media Signal processing Libraries Real-time systems

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Through-the-Wall Synthetic Aperture Radar Imaging Using 2-D Spectral Estimation Methods

Through-the-Wall Synthetic Aperture Radar Imaging Using 2-D ...

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2022 Innovations in Intelligent systems and Applications conference, ASYU 2022

作者： Dasbasi, Melek Hocaoglu, A. Koksal Savunma Teknolojileri Enstitüsü Gebze Teknik Üniversitesi Kocaeli Turkey Elektronik Mühendisliǧi Gebze Teknik Üniversitesi Kocaeli Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

In this study, adaptive signal processing algorithms are investigated to obtain high-resolution radar images of concealed targets. For this purpose, a vector network analyzer is used as a stepped frequency continuous wave-based radar system, and B-scan radar data is collected from concealed targets. synthetic aperture radar algorithm is applied to raw data to improve the azimuth resolution. Then, 2D Capon and MUSIC spectral estimation methods are implemented to the data obtained by the synthetic aperture radar algorithm for through-the-wall imaging applications. Fusion of synthetic aperture radar and spectral estimation methods are represented to compare enhanced image quality for better target detection. © 2022 IEEE.

关键词： image enhancement

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Relative Gradient Matching Cost for Mitigating Feature Discrepancies in Stereo Matching 5

Relative Gradient Matching Cost for Mitigating Feature Discr...

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5th International conference on High Performance Big Data and Intelligent systems, HDIS 2023

作者： Liu, Changlin Zhang, Yuan Sun, Linjun Zhang, Kaijie Cao, Bing Li, Zhiwei School of Semiconductor Science and Technology South China Normal University Guangzhou China School of Information Science and Technology Beijing Forestry University Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350394160

In stereo matching, perspective differences in images can cause feature inconsistencies. Traditional stereo matching algorithms compare pixel disparities using local parallel windows transformed by matching costs. However, this method under the fronto-parallel windows assumption may produce incorrect matching results when there are perspective differences in the feature scale. To address this issue, we propose a new cost computation method called relative gradient matching cost (RGC). RGC effectively handles feature perspective differences by robustly constraining feature matching relationships across varying object surface depths, simulating the effect of slanted support windows. We propose different improvement strategies for common traditional methods and DNN-based methods combined with RGC. In experiments, we compare the performance of RGC with some SOTA models. The experiment results demonstrate that RGC significantly improves the accuracy and stability of stereo matching, especially in cases with edge and discontinuity areas. ©2023 IEEE.

关键词： Stereo image processing

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Optimal Digital Algorithm of a Cognitive Synthetic Aperture Radar Operation 12

Optimal Digital Algorithm of a Cognitive Synthetic Aperture ...

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12th IEEE International Scientific Technical conference on Dependable systems, Services and Technologies (DESSERT) - Dependable and Trustworthy AI, IoE, Big Data Analytics for a Safe and Secure World

作者： Zhyla, Semen Volosyuk, Valeriy Pavlikov, Vladimir Vlasenko, Dmytro Mazurenko, Oleksandr Lisohorskyi, Bohdan Natl Aerosp Univ Aerosp Radioelect Syst Dept Kharkiv Aviat Inst Kharkiv Ukraine Kharkiv Natl Air Force Univ Chief Marshal Aviat I Fac Radiotech Air Def Forces Kharkiv Ukraine

ISBN: (纸本)9798350333046

The problem of optimal estimation of the complex scattering coefficient of the underlying surface in a digital radar with cognitive signal processing is considered. It is shown that it is possible to develop new algorithms not only in a heuristic way by combining together all the necessary structural components of a cognitive radar. To test the new approach the formation of an optimization problem for the synthesis of the optimal algorithm for the secondary filtering of the radar image of the surface was performed, and a solution was given. On the one hand the obtained algorithm corresponds to the operation of the extended Kalman-Bucy filter, and on the other hand, it has implementation features specifically for the task of terrain radar imaging. Based on the results of the analysis of the obtained expressions a structural diagram of the cognitive radar was synthesized.

关键词： cognitive radars optimal signal processing secondary processing adaptive receiver

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Algorithm of Palm Vein image Selection for Biometric Authentication systems Based on Multidimensional Markov Chain

Algorithm of Palm Vein Image Selection for Biometric Authent...

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Digital Signal processing and its Applications (DSPA), International conference on

作者： Natalya Kharina Anton Zemtsov Sergei Chernyadyev Department of the Radio Electronic Means Vyatka State University Kirov Russia Institute of Mathematics and Information Systems Vyatka State University Kirov Russia

Biometric palm vein authentication becomes more popular in the industry. This paper proposes an algorithm for palm vein image selection. The main idea of this method is an approximation of biometric template image through discrete Markov process and applying the theory of conditional Markov process to develop the algorithm, which allows to fully utilize the statistic redundancy of the biometric template. Given method can be effectively used after performing a thresholding process on an image, using the dynamic threshold value. CASIA MS Palmprint V1 Database dataset was used to present the examples of algorithm implementation. Morphological dilation and Zhang-Suen algorithms were used to evaluate and compare the effectiveness of the proposed palm vein image thinning algorithm. The results showed that the proposed method makes it possible to compute an image that preserves the vein thickness and closely matches the real vein pattern and is effective when using a template matching algorithm based on perceptual hash functions.

关键词： Thresholding (Imaging) Biometrics (access control) Veins Heuristic algorithms Redundancy Signal processing algorithms Authentication

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Automated Dengue Recognition and Analysis through image processing and Machine Learning

Automated Dengue Recognition and Analysis through Image Proc...

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Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： Preethi N Patil Usha J Andhe Dharani Department of MCA RV College of Engineering Bangalore India

Detecting dengue fever using image processing techniques typically involves the analysis of medical images such as blood smears or tissue samples. Dengue is a viral disease transmitted by Aedes mosquitoes, and its diagnosis primarily relies on clinical symptoms, serological tests, and molecular assays. image processing can be used as a complementary tool to assist and identify the dengue virus or related disparities in the body. Dengue fever, alternatively known as break-bone fever, is a lifethreatening arboviral disease caused by the DENV virus. It is a major global health concern with an increasing incidence worldwide. The existing clinical methods for diagnosing dengue are manual, requiring significant time and labor. The present work aims to develop an automated system using machine learning and digital image analysis to detect dengue infection from blood smear images. The custom algorithms are designed to extract platelets, red blood cells, and white blood cells, and train a feature vector. By automating the analysis of blood smear images, aids healthcare professionals detect cases of dengue more efficiently, allowing for early treatment and potentially saving lives. However, it is crucial to address data quality, algorithm selection, validation, interpretability, and ethical considerations throughout the development process.

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image-Based Analysis of Plate Separation and Clearance Distribution in Wet Clutches

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9270页

作者： Pointner-Gabriel, Lukas Flamm, Simon Voelkel, Katharina Stahl, Karsten Tech Univ Munich Gear Res Ctr FZG TUM Sch Engn & Design Dept Mech Engn D-85748 Garching Germany

A fundamental understanding of wet clutches' drag loss behavior is essential for designing efficient clutch systems. It has been widely recognized that the separation behavior immediately after disengaging the clutch and the resulting clearance distribution influence the drag loss behavior. However, these influencing factors have yet to be systematically investigated. Therefore, this study aimed to experimentally investigate the effects of plate separation and clearance distribution on drag loss behavior under different operating conditions and modes. For this purpose, image series of an operating clutch system were recorded and subsequently analyzed using image processing algorithms to evaluate the movements of the plates. Based on this, the effects on drag loss behavior were analyzed. The investigations were carried out on a clutch system used in an industrial application. The measurements show that the axial movements of the plates comprise main and superimposed non-periodic movements of much a smaller amplitude. The separation of the plates is primarily driven by the applied differential speed so that the set total clearance is only utilized mainly in the higher differential speed range. The separation behavior, therefore, decisively influences the drag loss behavior. The plates can even remain in contact in the low differential speed range. The investigations also showed that the separation behavior and, thus, the drag loss behavior can be improved by using waved plates, especially in the low differential speed range. It was also found that a high plate number and a large set total clearance support a non-uniform clearance distribution. Based on the investigations conducted, it is possible to expand our fundamental knowledge of separation behavior and clearance distribution, allowing for a reduction in the drag losses of wet clutches. The findings can thus contribute to the development of low-loss and compact clutch systems.

关键词： applied image processing wet clutches drag losses plate separation

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Research on Smoke image Recognition: Multi-Scale Dilated Convolutional Neural Network and Entropy Function Threshold

Research on Smoke Image Recognition: Multi-Scale Dilated Con...

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Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Yongmei Xiao Xing Wang Officers College of PAP Chengdu China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

For the application of artificial intelligence in image-based smoke detection in emergency management, this paper proposes an image recognition algorithm based on a multi-scale dilated convolutional neural network and entropy threshold function to prevent the loss of small-scale features, low detection rate, and poor real-time performance of smoke image recognition. First, the smoke features are determined according to a multi-scale analysis model, and multi-scale information such as texture, color, and shape of the smoke image is obtained. Second, we propose a dilated convolutional neural network to solve the feature loss. Third, according to the characteristics of the entropy function, we build a model that comprehensively considers smoke features and recognition thresholds, which more accurately describes the image recognition process. Ultimately, this research develops a smoke detection system using the suggested algorithm and demonstrates its efficacy through simulation and experimental validation.

关键词： image recognition Shape image color analysis Engineering management Signal processing algorithms Signal processing Feature extraction Entropy Real-time systems Convolutional neural networks

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