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Digital image Transformation Using Outer Totality Cellular Automata 4th

Digital Image Transformation Using Outer Totality Cellular A...

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4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Sharma, Sandeep Kumar Kumar, Anil Manipal University Jaipur Jaipur India Poornima Institute of Engineering and Technology Jaipur India

ISBN: (纸本)9789819900848

In the recent technology, digital images play an important role to implement various applications, i.e., pattern recognition, object detection, quality enhancement, object tracking, face identification, etc. the image applications may accept input of different types, and some applications have to perform various internal operations to achieve desired results. the proposed research provides best methods, which could be used as internal operations of image-based applications. the methods of proposed research target various features of image like brightness, sharpness, colors and intensity of pixels, and by analyzing these features, the proposed methods can increase efficiency and accuracy of the image-based applications. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Rail transit obstacle recognition technology based on infrared image and SURF algorithm 4

Rail transit obstacle recognition technology based on infrar...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Liu, Bo Zhang, Yu Li, Cheng Beijing Metro Operation Co. Ltd. Beijing100032 China

ISBN: (纸本)9781510671232

Currently, rail transit has developed more and more intelligently, and intelligent vehicles are of significance in reducing traffic accidents and improving transportation capacity and have broad market prospects. this research combines infrared image acquisition and machine algorithms for obstacle recognition. the SURF algorithm performs feature extraction and description, and elimination of mismatched point pairs on infrared and visible light images, which can do image recognition on track obstacles. On this basis, a foreign object monitoring system consisting of an optical imaging subsystem, a lighting and detection light source subsystem, a pan tilt synchronous control subsystem, an image acquisition and processing subsystem, and an installation fixture and protection subsystem was designed. Finally, a demonstration application of foreign object monitoring under different working conditions in the section adjacent to the elevated line and highway of Guangzhou Metro Line 4, the open line section (K46+175) from Tung Chung Station to Huangge Auto Town station, for the 100 m section of the uplink and downlink double lines was carried out on the proposed system, which verified that the foreign object detection rate was 100% based on the test data, and the maximum detection response time was 4s (less than 5s). the relative deviations between the detected foreign objects and actual positions were less than 2.13% and the relative deviations between the detected foreign objects sizes and the actual sizes were less than 8.8%, which fully proved the effectiveness of the monitoring system. © 2023 SPIE.

关键词： image acquisition

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Research on computer network security situation awareness warning mechanism based on artificial intelligence 4

Research on computer network security situation awareness wa...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Chaowen, Chen Guangzhou Huashang Vocational College Guangzhou City511300 China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361643

As computer network security threats become increasingly intricate, this paper centers on developing an AI-based network security situational awareness early warning mechanism to establish an accurate, real-time, and efficient defense system. By leveraging AI's prowess in big data processing, pattern recognition, and adaptive learning, the study enhances comprehensive understanding of network security situations, enabling timely detection and response to potential threats. the paper initially highlights the need and benefits of AI in network security situational awareness. Integrating deep learning, machine learning, and related technologies, the system accurately identifies various security incidents (e.g., abnormal traffic, malware, intrusions), adapts to dynamic network changes, and effectively combats novel, unknown threats. Subsequently, the paper presents an AI-driven network security situational awareness early warning model, consist of data acquisition, feature extraction, situation analysis, and early warning phases. Real-time network behavior data is gathered, deep neural networks extract key situation features, integrated learning constructs a risk assessment model, and automatic warnings trigger when risk thresholds are exceeded, accompanied by risk details and mitigation strategies. A prototype of the AI-based early warning system is designed and implemented. Experiments reveal its superior precision, minimal delay, and robust performance in large networks. Compared to traditional approaches, it improves threat detection by around 30%, reduces false alarms by over 40%, and significantly shortens response times. the system is scalable, adaptable to expanding network sizes and evolving attack tactics. Looking ahead, the paper explores future research topics like cross-domain data fusion, heterogeneous model collaboration, and human-computer interaction optimization, emphasizing the importance of considering legal, regulatory, ethical aspects in AI's appli

关键词： Network security

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vision Transformer and Bidirectional RoBERTa: A Hybrid image Captioning Model Between VirTex and CPTR 12th

Vision Transformer and Bidirectional RoBERTa: A Hybrid Imag...

引用

12th international Advanced Computing conference, IACC 2022

作者： Lam, Khang Nhut Le, Diem-Kieu thi Ngo, Truong Dinh Kalita, Jugal Can Tho University Can Tho Viet Nam University of Colorado Colorado Springs United States

ISBN: (纸本)9783031356407

image captioning neural networks are trained simultaneously on image recognition sub-models and natural language processing sub-models to generate description sentences for images. this paper presents several image captioning models based on the encoder-decoder framework. We change the neural sub-models used for the encoder as well as the decoder, and make comparisons. First, we experiment with several ResNet architectures (viz., ResNet-50, ResNet-101, and ResNet-152) as encoders, and Transformer or bidirectional Transformer models as decoders. Second, we use the combination of the vision Transformer as a visual encoder, and the standard Transformer or RoBERTa as the language decoder. Finally, we propose an image captioning model using vision Transformer for encoding images and bidirectional Transformer for predicting image captions. the models are trained on the Flickr8k dataset in English and Vietnamese and evaluated using the BLEU metric. the combination model between the vision Transformer and the bidirectional RoBERTa model outperforms the existing image captioning models, including VirTex and CPTR models. the BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of our best image captioning model are 0.870, 0.661, 0.443, and 0.331 on the English dataset, and 0.829, 0.647, 0.483, and 0.387 on the Vietnamese dataset. © 2023, Springer Nature Switzerland AG.

关键词： image recognition

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. this paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. the complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. the proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. the deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning image processing

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Automatic identification system of aircraft cockpit indicators based on machine vision 4

Automatic identification system of aircraft cockpit indicato...

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4th international conference on Advanced Information Science and System, AISS 2022

作者： Yao, Jiaqing Chen, Renwen Huang, Yijun State Key Laboratory of Mechanics and Control of Mechanical Structure Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9781450397933

Aiming at the problem that the test of civil aircraft is greatly affected by human factors in the batch manufacturing process, the automatic identification system of aircraft cockpit indicators based on machine vision is researched. Firstly, the slant screen images are automatically corrected and segmented by image processing technology. Secondly,different methods are used to identify different regions according to the specificity of different regions in the images. YOLOv5 algorithm is used for target detection of unconventional indicators;EasyOCR algorithm is used for character recognition, especially in decimal recognition, this research improved EasyOCR, in which using the projection of the legal decimal point position and cover at the data input end, modify the initial characteristics, to avoid the wrong identification interference of the decimal point. At the output end, the correct decimal point is reset between digits. the accuracy is improved by 27.65% and the average accuracy is 96.60%;Other general indicators recognition, using common image processing techniques, such as Hough transform, HSV color matching, etc. Experimental results show that the average error rate of identification results of various indicators is only 1.96%, the speed of machine recognition is 4.8 times that of manual test. Compared with manual test, it can effectively solve the problems of misjudgment, heavy workload and inefficient, and improve the degree of automation in the batch production process of civil aircraft. © 2022 ACM.

关键词： computer vision

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Compression of Medical images Using Lifting Haar Wavelet Transform for Teleradiology Applications 1

引用

4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Antony, Linu Tess Kumar, S.N. Department of Basic Science Amal Jyothi College of Engineering APJAKTU Kerala Kanjirappally India Department of EEE Amal Jyothi College of Engineering Kerala Kanjirappally686518 India

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

Mathematical transforms gain prominence in computer vision and image processing applications. Wavelet transform provides multiresolution analysis of signals and uses adaptive basis function that was tailored to the specific signal being analysed. In this research, the lifting wavelet transform is utilized to compress medical images. Compression of medical data play vital role in minimization of storage space for Tele radiology application. the lifting wavelet transform utilizes the lifting scheme, it is computationally less complex than discrete wavelet transform. this research work proposes compression of medical images using lifting Haar wavelet transform. the achieved outcomes were found to be satisfactory, and their performance metrics were thoroughly validated. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image compression

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A face detection method via ensemble of four versions of YOLOs 12

A face detection method via ensemble of four versions of YOL...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Khalili, Sanaz Shakiba, Ali Vali-e-Asr University of Rafsanjan Department of Computer Sciences Rafsanjan Iran

ISBN: (纸本)9781665412162

We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). the experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. these numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble. © 2022 IEEE.

关键词： Face recognition

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A complex network approach for fish species recognition based on otolith shape 11

A complex network approach for fish species recognition base...

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11th international conference on image processing theory, Tools and Applications (IPTA)

作者： Ribas, Lucas C. Scabini, Leonardo Bruno, Odemir M. Univ Sao Paulo Sao Carlos Inst Phys Sao Carlos SP Brazil

ISBN: (纸本)9781665469647

Fish otolith recognition is an essential task to study the evolution and food chains in paleontological and ecological sciences. One of the approaches to this problem is to automatically analyze the shape of otolith contour present in images. In this paper, we explore a state-of-the-art shape analysis method called "angular descriptors of complex networks (ADCN)" applied to the classification of otolith images for fish species recognition. the ADCN method models the otolith contour as a graph, or complex network, and computes angular properties from its connections for shape characterization. the ADCN method is evaluated in an otolith image dataset composed of 14 fish species from three families. Up to 95.71% of accuracy is achieved, which surpasses other literature methods and confirms that the ADCN method can be an important tool for such biological problems.

关键词： Complex Networks computer vision Feature Extraction Shape Analysis Fish recognition Otolith

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Self-distilled Dual-Network with Pixel Screening Loss for Blind image Deblurring 27th

Self-distilled Dual-Network with Pixel Screening Loss for ...

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27th international conference on pattern recognition, ICPR 2024

作者： Li, Tianyi Tian, Ming Gao, Changxin Sang, Nong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

In the field of image processing, blind image deblurring aims to restore sharp details in images blurred by an unknown convolution kernel. Recent advancements have shown that deep networks can act as effective image generative priors (DIP) for restoring clear images without requiring external datasets. However, the inherent non-uniqueness of solutions in blind image deblurring often leads DIP-based methods to converge on local optima, resulting in over- or under-deblurred images. To overcome this limitation, we propose a novel deblurring framework featuring dual image generators. these generators mutually constrain each other during training, guiding the model towards the optimal solution. Building on our network structure, we employ a self-ensemble and self-distillation strategy to guide network training, further enhancing performance. Additionally, we introduce a novel loss function based on a pixel screening method, which focuses on the important pixels. this loss enables the network to model the blur kernel more accurately and facilitates the restoration of image details. Our experiments demonstrate that our deblurring approach outperforms most existing methods both qualitatively and quantitatively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Restoration

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