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8th international conference on Communication, image and Signal processing, CCISP 2023

作者： Duan, Yubing Wang, Hejun Deng, Kai Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350305838

Text similarity calculation is an important component of many text mining techniques. Most of the existing studies only consider model fusion of textual features or semantic features, which are not suitable for long text processing scenarios due to the time consumption of topic extraction for words of the whole document. This article introduces related work of text similarity calculation, including TF-IDF algorithm, lexical features and LDA(Latent Dirichlet Allocation) model. Then a text similarity calculation method that combines text word features and text implicit topic features is proposed, making full use of the surface and hidden information of text to calculate text similarity. The proposed method first improves traditional word mover's distance (WMD) algorithm by introducing lexical features of text, and then calculates text similarity by linear weighting of the improved WMD algorithm and LDA model algorithm. Experimental results indicate that compared with traditional WMD algorithm and LDA model algorithm, the proposed method obtains better text similarity calculation results. © 2023 IEEE.

关键词： Text processing

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Brightness gain and maximum output brightness adjustment method and test system of third-generation Low-light-level image intensifier 3

Brightness gain and maximum output brightness adjustment met...

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3rd international Computing Imaging conference, CITA 2023

作者： Ren, Yingnan Yan, Bo Zhang, Linlin Jiao, Gangcheng Ni, Xiaobing Song, Haihao Science and Technology on Low-Light-Level Night Vision Laboratory Xi’an710065 China Kunming Institute of Physics Kunming650223 China

ISBN: (纸本)9781510671409

For the current brightness gain and maximum output brightness adjustment method of image intensifier has low efficiency, time-consuming, low precision, and other problems, this paper firstly studies a communication protocol from the perspective of digital communication of auto-gating power supply microcontroller. By means of communication with the host computer, the corresponding parameters of the internal program of the auto-gating power supply microcontroller are adjusted. Secondly, the hardware interface of the whole tube communication of the image intensifier is designed. Finally, the calibrated experimental device is used to adjust the brightness gain and maximum brightness of the whole tube, and the automatic brightness control function of the adjusted whole tube is verified. The adjustment time is short, the adjustment precision is high, and the matching between the auto-gating power supply and the image tube can be better realized. © 2023 SPIE.

关键词： Luminance

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Segmentation-Based Masked Sampling for text-to-animated image synthesis in disaster scenarios 14

Segmentation-Based Masked Sampling for text-to-animated imag...

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14th international conference on Information and Communication Technology Convergence, ICTC 2023

作者： Won, Ru-Bin Choi, Ji Hoon Choi, Minji Bae, Byungjun University of Science and Technology Korea Information and Communication Engineering Daejeon Korea Republic of Media Intellectualization Research Lab Daejeon Korea Republic of

ISBN: (纸本)9798350313277

Generative AI has demonstrated significant capabilities in text-to-video synthesis, using advanced models characterized by extensive parameters. Given that current disaster alert services are mostly text-based and can be less accessible to many, this paper proposes a new approach. We aim to provide animated disaster images that precisely mirrors text prompts, thereby enhancing the efficiency and accessibility of disaster alerts. Our methodology combines the strengths of segmentation models and pre-trained vision Transformer (ViT) mechanisms. By using a unique image selection based on CLIPScore and processing it with the CLIPSeg segmentation model, we generate an animated representation of disaster scenarios. This offers a simple, fast, and effective solution to the challenges of the current disaster alert systems. © 2023 IEEE.

关键词： disaster alert system image segmentation masked sampling text-to-animated image generative model text-to-video synthesis vision Transformer (ViT)

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An Application of image Denoising Technique Based on Convolutional Neural Network in Star Tracker 8th

An Application of Image Denoising Technique Based on Convolu...

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8th international conference on Signal and Information processing, Network and computers, ICSINC 2021

作者： Li, Jingjin Peng, Fei Wang, Yizheng Beijing Aerospace Satellite Herd Science and Technology Co. Ltd Haidian District Beijing100094 China Beijing Institute of Control Engineering Haidian District Beijing100190 China

ISBN: (纸本)9789811933868

Convolution neural network is widely used in the field of computer vision, which can complete the tasks of target detection, image segmentation, semantic generation and so on in complex scenes. As the main optical sensor of the satellite attitude control subsystem, the star tracker is used to calculate the attitude from the star image. In this paper, a local sky area image restoration technique for star tracker based on convolution neural network is proposed and implemented, which can be used to solve the stray light interference, binary star imaging, star point dragging and other problems in the imaging of star tracker, so as to improve the attitude determination accuracy of star tracker. Experimental results show that this method can improve the number of star point recognition and the attitude determination accuracy of the star tracker. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Blindness Risk Prediction caused by Diabetic Retinopathy from Retinal image 3

Blindness Risk Prediction caused by Diabetic Retinopathy fro...

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3rd international conference on Electrical, computer and Communication Engineering, ECCE 2023

作者： Paul, Laboni Talukder, Kamrul Hasan Khulna University Computer Science and Engineering Discipline Khulna Bangladesh

ISBN: (纸本)9798350345360

Type-II diabetes is growing at an alarming rate worldwide. Eventually, it leads to visual impairment by damaging the retinal blood vessels, which is known as Diabetic Retinopathy (DR). A plethora of research is ongoing to automate the early detection of DR to help the doctor with periodic eye examinations due to advancements in AI based computer vision algorithms and camera technology. In this paper, we have proposed a comparison between two widely used very deep convolutional neural network architectures (ResNet-101 v2, InceptionResNet V2) with the comparatively new optimized and composite scalable architecture (EfficientNet B5) with while training them from scratch and using them as a pre-trained to transfer learning. We have found out the pre-trained EfficientNet B5 has outperformed our other candidates as well as available methods in the current literature by achieving accuracy of 97.78%. We also provide detailed enough information to make the result reproducible. © 2023 IEEE.

关键词： Risk assessment

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NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines

NN-VVC: Versatile Video Coding boosted by self-supervisedly ...

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2023 IEEE international Symposium on Multimedia, ISM 2023

作者： Ahonen, Jukka I. Le, Nam Zhang, Honglei Hallapuro, Antti Cricri, Francesco Tavakoli, Hamed Rezazadegan Hannuksela, Miska M. Rahtu, Esa Nokia Technologies Finland Tampere University Finland

ISBN: (纸本)9798350395761

The recent progress in artificial intelligence has led to an ever-increasing usage of images and videos by machine analysis algorithms, mainly neural networks. Nonetheless, compression, storage and transmission of media have traditionally been designed considering human beings as the viewers of the content. Recent research on image and video coding for machine analysis has progressed mainly in two almost orthogonal directions. The first is represented by end-to-end (E2E) learned codecs which, while offering high performance on image coding, are not yet on par with state-of-the-art conventional video codecs and lack interoperability. The second direction considers using the Versatile Video Coding (VVC) standard or any other conventional video codec (CVC) together with pre- and post-processing operations targeting machine analysis. While the CVC-based methods benefit from interoperability and broad hardware and software support, the machine task performance is often lower than the desired level, particularly in low bitrates. This paper proposes a hybrid codec for machines called NN-VVC, which combines the advantages of an E2E-learned image codec and a CVC to achieve high performance in both image and video coding for machines. Our experiments show that the proposed system achieved up to -43.20% and -26.8% Bjontegaard Delta rate reduction over VVC for image and video data, respectively, when evaluated on multiple different datasets and machine vision tasks. To the best of our knowledge, this is the first research paper showing a hybrid video codec that outperforms VVC on multiple datasets and multiple machine vision tasks. © 2023 IEEE.

关键词： computer vision

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Morphology Approach for Segmentation of Blood Vessels in Retinal images 6

Morphology Approach for Segmentation of Blood Vessels in Ret...

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6th international conference on computer and Informatics Engineering, IC2IE 2023

作者： Triwijoyo, Bambang Krismono Adil, Ahmat Chani Saputri, Dian Syafitri Anggrawan, Anthony Bumigora University Department of Computer Science Mataram Indonesia

ISBN: (纸本)9798350345162

Segmentation is a challenging and important part of image analysis. Various kinds of eye diseases are identified through the retinal blood vessels, so retinal image segmentation plays an important role in medical image analysis. Even though it's been a lot of algorithms for extraction and retinal vessel processing, there are still obstacles to producing the best output. Some of the obstacles include poor contrast and retinal images that lack details, especially on the thin blood vessels in the retina. This paper proposes a morphological approach for retinal blood vessel segmentation using edge detection first, we apply contrast enhancement to the retinal image to get better segmentation results, and then we apply top hat morphology using multi-structure elements to detect blood vessel edges using a 7×7 pixel structure element with direction intervals of 15°. finally, eliminate false edges using the aperture operator. Based on the results of tests carried out on the DRIVE dataset, the proposed model provides an average accuracy of 96.25%. © 2023 IEEE.

关键词： morphology retina blood vessels segmentation

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Intelligent Traffic Control using computer vision Algorithms 17

Intelligent Traffic Control using Computer Vision Algorithms

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Optics and Photonics for Information processing XVII 2023

作者： Anwar, Syed Asim Zohura, Fatima Tuz Paul, Joy North South University Bangladesh Rajshahi University of Engineering & Technology Bangladesh Department of Electrical & Computer Engineering

ISBN: (纸本)9781510665606

Traffic congestion has become one of the major issues in Bangladesh. The vehicle density on the road is slowly becoming greater than the road capacity and resulting in difficult commutes. This traffic delay leads to wastage of valuable time which impacts the economic development of the country. One of the main reasons for this type of road congestion is due to poor traffic management. This paper presents implementation of an intelligent traffic control system using computer vision algorithms. In this research, we propose a smart traffic management system by measuring the traffic density of the road by real time detection and image processing. The vehicle detection system counts the number of vehicles approaching a traffic signal to determine the congestion of the traffic on the road. Then traffic controller uses an algorithm to control the timings of the traffic signals, red, green and yellow, based on the number of vehicles on the road. Our system was developed by capturing real traffic video using smartphone, vehicle detection system tested in the computer and the traffic signal was implemented in Arduino hardware. Vehicle detection accuracy was increased by training a more extensive dataset with Faster R-CNN (Region-based Convolutional Neural Network) and YOLOv5 (You Only Look Once version 5) models. © 2023 SPIE.

关键词： computer vision

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Entropy Transformer Networks: A Learning Approach via Tangent Bundle Data Manifold

Entropy Transformer Networks: A Learning Approach via Tangen...

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international Joint conference on Neural Networks (IJCNN)

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh East China Normal Univ Sch Commun & Elect Eng Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Eng Shanghai Peoples R China

ISBN: (纸本)9781665488679

This paper focuses on an accurate and fast interpolation approach for image transformation employed in the design of CNN architectures. Standard Spatial Transformer Networks (STNs) use bilinear or linear interpolation as their interpolation, with unrealistic assumptions about the underlying data distributions, which leads to poor performance under scale variations. Moreover, STNs do not preserve the norm of gradients in propagation due to their dependency on sparse neighboring pixels. To address this problem, a novel Entropy STN (ESTN) is proposed that interpolates on the data manifold distributions. In particular, random samples are generated for each pixel in association with the tangent space of the data manifold, and construct a linear approximation of their intensity values with an entropy regularizer to compute the transformer parameters. A simple yet effective technique is also proposed to normalize the non-zero values of the convolution operation, to fine-tune the layers for gradients' norm-regularization during training. Experiments on challenging benchmarks show that the proposed ESTN can improve predictive accuracy over a range of computer vision tasks, including image reconstruction, and classification, while reducing the computational cost.

关键词： Transformer image reconstruction data manifold

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image Recognition of Ore Specimens Based on Deep Neural Network 2

Image Recognition of Ore Specimens Based on Deep Neural Netw...

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2nd international conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2023

作者： Liu, Lirui Upper SchoolRanney School New Jersey NJ07724 United States

ISBN: (纸本)9798350339765

Ore and mineral identification is an excellent obstacle for today's geological informatization popularization work. At the same time, identification plays a vital role in many research areas. Therefore, intelligent identification of ores and minerals is the basis for pushing for the work to popularize geological science. Today's computer vision technology and deep learning theory made the intelligent identification of ore minerals possible. This paper introduces artificial intelligence to the field of geological science by utilizing a combination of a cloud server, recognition model, convolutional neural network, and deep learning. This integration realizes the 'face ID' for minerals and rocks, which can help people in the industry and the public quickly identify minerals and rocks found in various environments. At the same time, it can widely promote geology's informatization and the work's *** the research and analysis of ore identification-related technologies, the author selected the Pytorch framework to optimize the ResNet50 deep learning model to identify ore samples. The main contents of the essay are as follows:(1)Fully studied and researched the related technologies in computer vision technology and ore identification, developed an in-depth understanding of the identification framework, analyzed its shortcomings, and finally chose the Pytorch framework to identify ore samples.(2)Eleven ore samples were selected to make the dataset, including quartz, biotite, bornite, malachite, granite, limestone, and malachite.(3)The image is denoised. Then, combined with the preprocessed images, data augmentation is performed on the ore color image by rotation and translation to reduce the sample imbalance.(4)The ResNet50 model is optimized based on the Pytorch framework, and the optimized model is used to prepare, evaluate, and validate the data set. As a result, the resulting model achieved an impressive recognition accuracy rate of 98%.(5)Combining t

关键词： Ores

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