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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Realtime Scene Enhancement of Low Light image/Video

Realtime Scene Enhancement of Low Light Image/Video

引用

Intelligent Computing and Human-Computer Interaction (ICHCI), International conference on

作者： Huixia Zeng Ping xiao School of Electronic & Information Engineering Guangzhou City University of Technology Guangzhou Guangdong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

The quality of image and videos plays a vital role in case of real-time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973.

关键词： Runtime Neural networks Lighting Streaming media Real-time systems Stability analysis image enhancement Thermal stability Videos Noise level

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LPD Data Link in System-of-systems Simulation 7

LPD Data Link in System-of-Systems Simulation

引用

7th International conference on Signal and image processing, ICSIP 2022

作者： Zhang, Jie Cao, Wei Wu, xi Wu, Sicong National Defense University Graduate School Beijing China National Defense University Joint Operations College Beijing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Low probability of detection (LPD) data link is important to realize radio frequency (RF) stealth on the battlefield when stealth aircraft performs penetrating combat missions. However, in the System-of-systems (SoS) simulation, there is no research on how to model the LPD data link. In this paper, a LPD data link model and RF signal detection model are established. First, data link model in the SoS simulation system is described, and three antenna beam models, simple circular pencil beam, simple elliptical pencil beam and enhanced elliptical pencil beam, are introduced in detail. Moreover, the detection workflow of the passive radar model is described. Finally, experimental results show that: 1) with lower power, the LPD data link get better RF stealth performance;2) the enhanced elliptical pencil beam model is more suitable for simulating the LPD data link compared with simple circular pencil beam and simple elliptical pencil beam;3) it is an effective method to simulate the LPD data link using low sidelobe antenna technology in the SoS simulation system. © 2022 IEEE.

关键词： Signal detection

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AI-based Gender Identification using Facial Features 2

AI-based Gender Identification using Facial Features

引用

2nd International conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Venkateswarlu, B. Sunanda, N. Kumar, A Yaswanth Mani Reddy, A Naga Siva Charan Hyndav, B. Ram Gopal Koneru Lakshmaiah Education Foundation Department of Cse Guntur India Vnr Vignana Jyothi Institute of Engineering & Technology Department of CSE-DS CYS Ai&ds Hyderabad India

ISBN: (纸本)9781665456302

The identification of gender through facial images has gained significant importance in recent times. In the domain of human recognition through image processing, biometric features like facial structure, iris, voice, and other physical characteristics are employed to establish unique identification of individuals. As the quantity of image data on the internet continues to rise, the need for Artificial Intelligence (AI) algorithms capable of performing classification tasks like face and gender recognition has grown exponentially. With the increasing prevalence of machine-to-machine interaction, and the emphasis on ethical, efficient, and individual-centric systems becoming more pronounced, gender identification software has become an increasingly popular tool. Automated gender identification software has gained popularity in the era of exchange of information between two or more machines. The method for detecting the face involves using the Haar Cascade face detection technique, which employs the Viola-Jones face recognition algorithm. After detecting the face, the eyes, mouth, and nose can be identified as facial features using various computer vision techniques. Adaptive filters are utilized to decrease noise prior to detection process in order to increase gender identification accuracy. The gathered facial characteristics serve the neural network as input or test data, which is then trained on these features. The neural network is designed to learn gender-specific features and operate as a classifier for gender detection. © 2023 IEEE.

关键词： image enhancement

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High Dynamic Range image Tone Mapping Based on Layer Decomposition and image Fusion

High Dynamic Range Image Tone Mapping Based on Layer Decompo...

引用

IEEE International conference on image processing

作者： Xueyu Han Susanto Rahardja School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Infocomm Technology Cluster Singapore Institute of Technology Singapore Singapore

Common displays have limited dynamic range and therefore cannot support direct reproduction of high dynamic range (HDR) images. Generally, this gap can be effectively solved by image tone mapping. One of the typical tone mapping methods is known as layer decomposition based algorithms. They decompose an HDR image into base and detail layers, and reduce the dynamic range by employing gamma functions on the base layer. However, simple gamma curves are not sufficient to produce good local contrasts. This paper proposes a layer decomposition based method by utilizing image fusion techniques to enhance local contrasts. Our method constructs virtual images to stretch dark, normal and bright regions of the base layer respectively and fuses them together for contrast enhancement. Experimental results validate that the proposed method produces enhanced local contrasts and more natural colors in the tone-mapped images.

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Comparison of image Feature Detection algorithms 9

Comparison of Image Feature Detection Algorithms

引用

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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A Model robustness optimization method based on adversarial sample detection 5

A Model robustness optimization method based on adversarial ...

引用

5th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Sun, Jiaze Long, Siyuan Ma, xianyan Tang, Yanmei Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. The detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments. © 2022 ACM.

关键词： Deep neural networks

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Sign Language Translation Using image processing to Audio Conversion

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

引用

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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Analysis of Relationship between Point Cloud Just Noticeable Difference and Attribute Quantization Parameters

Analysis of Relationship between Point Cloud Just Noticeable...

引用

IEEE Visual Communications and image processing (VCIP)

作者： Zhang Chen Luqian Bai Mengting Yu Shuai Wan Hejie Yang School of Electronics and Information Northwestern Polytechnical University Xi’an China School of Telecommunication Engineering Xidian University Xi’an China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Utilizing just noticeable difference(JND) thresholds to describe distortion visibility enables efficient transmission and storage of point clouds while maintaining high quality. Therefore, describing the relationship between point cloud JND and attribute quantization parameters (QP) is essential. This relationship is ,however, not clear regarding point cloud compression so far. We investigate the correlation between JND and attribute QP using Geometry-based Point Cloud Compression(G-PCC), which is the latest standard for point cloud compression. Moreover, we provide the corresponding datasets in this work. Using the K-means clustering algorithm, we partition the collected raw data into intervals and identify the peaks closest to the means in each sub-interval as the desired JND points. For human-content point clouds, although the number of JND varies, the QP values corresponding to different levels of JND show a regular distribution. The study focuses on the QP corresponding to the starting JND point(JND S ) and the ending JND point (JND E ).The findings indicate that the critical points for human-content point clouds are relatively consistent, with JND S mainly concentrated at QP = 27 and JND E at QP = 45. Conversely, perception in object-content point clouds is more influenced by their content, and the distribution of JND is not as uniform as in human-content point clouds. The experimental datasets and research results are accessible at the following link: https://***/ZhangChen2022/JND-attribute-QP-on-G-PCC.

关键词： Point cloud compression Quantization (signal) Correlation Visual communication image processing Clustering algorithms Distortion Partitioning algorithms Standards

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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 ...

引用

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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Semantic Graph Embedded Energy Minimization Learning for Scene Graph Generation

Semantic Graph Embedded Energy Minimization Learning for Sce...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jinghang Chen Chi Zhang Yuehu Liu Le Wang Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China National Engineering Research Center for Visual Information and Applications National Key Laboratory of Human-Machine Hybrid Augmented Intelligence

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The performance of current scene graph generation models is affected by training with cross-entropy loss, exacerbating the problem of prediction bias stemming from biased training data. Energy-based model adopts a learning method for joint image and scene graph to alleviate this challenge. However, this method only focuses on the visual features of images, neglecting the rich relation information contained in the semantic space. To address this issue, we innovatively employ powerful pre-trained large models to realize a simple yet effective semantic graph embedded energy minimization framework for the SGG task. Specifically, we use large models to generate image descriptions and extract relation triplets, which are then transformed into semantic graphs with entities as nodes and relations as edges. Moreover, by mapping these graphs into the same space using GNN to learn the minimal energy value, our approach enables SGG model to learn structural information in both semantic and visual spaces. We validate the effectiveness and efficiency of our method on the SGG benchmark Visual Genome dataset. Compared with prevailing models and EBM, we achieve a significant performance improvement of up to 2.99% and 2.24%, respectively.

关键词： Training Learning systems Visualization image edge detection Semantics Training data Signal processing Predictive models Minimization Speech processing

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