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

共 16443 条记录，以下是1201-1210 订阅

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A New Tightly-coupled Lidar-Visual-Inertial Odometry with Loop Closures

A New Tightly-coupled Lidar-Visual-Inertial Odometry with Lo...

引用

2024 International conference on Electrical Power systems and Intelligent Control, EPSIC 2024

作者： Li, Weibin Yang, Donghao Li, Zhanbin School of Artificial Intelligence Xidian Univerisity Xi’an China Langge Technology Co. Ltd Hangzhou China

ISBN: (纸本)9798350366075

Simultaneous Localization And Mapping (SLAM) is a technology aimed at concurrently ascertaining the position of a robot while mapping an unknown environment. Its core involves achieving accurate and stable pose estimation for the robot. Fusion of multiple sensors has been demonstrate to be an effective method for improving accuracy. As the types of information acquired by different sensors increase, it inevitably enhances the robustness of pose estimation. This paper introduces a fusion framework designed for the multi-sensor system that combines lidar, inertial, and visual sensors. It utilizes a factor graph to perform global optimization, so that it enables real-time, high-precision, and stable trajectory prediction and map generation for robots. The system is split into two components: a Visual-Inertial Odometry (VIO) subsystem and a Lidar-Inertial Odometry(LIO) subsystem. LIO continuously acquires new point cloud information, extracts features, performs frame-to-frame feature matching for loop closure detection, and append the lidar points into the global map. VIO uses data of the point cloud from LIO for initialization, alternates with LIO processing, and utilizes visual information to apply color rendering to the map. To enhance system robustness, VIO not only directly utilizes point cloud data but also extracts features directly from image information, preventing over-reliance on LIO. Even if one of the subsystems fails, the system can continue to *** experiments were conducted on open datasets, demonstrating that our approach achieves more accurate robot pose estimation and produces point cloud maps with color information. ©2024 IEEE.

关键词： Robots

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Research on Flame image processing of Climbazole Production Line Based on RGB+HSI Color Model 3

Research on Flame Image Processing of Climbazole Production ...

引用

3rd Optics Frontier conference, OFS 2023

作者： Su, Zhan xi, HaiQi Gao, Yan Zhang, Lin Yang, Ben Zhang, Zhifeng High&New Technology Research Center of Henan Academy of Sciences Henan Zhengzhou450000 China College of Physics and Electronic Engineering Zhenghzou University of Light Industry Henan Zhengzhou450000 China White Pigeon Abrasive Tools Co. Ltd Henan Zhengzhou450000 China

ISBN: (纸本)9781510666450

The existing fire alarm system has strict distance and installation requirements between the fire point and the detector, and is easy to be interfered by environmental factors. It is not suitable for places with large space and many interference factors such as Climbazole production line. This paper proposed a flame image detection technology based on RGB+HSI color model and the detection system is designed and developed. The experimental results show that the flame image detection system based on RGB+HSI color model has the better recognition efficiency, which meets the real-time and accuracy requirements for early flame image detection in Climbazole production line. © 2023 SPIE.

关键词： Alarm systems

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Emotion recognition based on facial gestures and Convolutional Neural Networks 3

Emotion recognition based on facial gestures and Convolution...

引用

3rd conference on Information Technology and Data Science

作者： Sanchez-Callejas, Francisco Emiliano Cruz-Albarran, Irving A. Morales-Hernandez, Luis A. Autonomous Univ Queretaro Fac Engn Queretaro Mexico

ISBN: (纸本)9798350387896;9798350387889

Humans express emotions verbally and non-verbally through their voice, facial expressions, and body language. Facial expression recognition systems can identify the emotional state of any person by using different intelligent algorithms, such as Support Vector Machines, Hidden Markov Models, and Convolutional Neural Networks, among others. This study focuses on facial expression recognition using eye and mouth regions of images from the FER-2013 dataset by training convolutional neural network (CNN) models. Seven emotional states - happy, sad, fear, anger, disgust, surprise and neutral - were identified. The methodology included segmenting and concatenating the images to form three CNN models. The best-performing model, a four-layer CNN with 8, 16, 32, and 64 filters, achieved remarkable results: 99.05% accuracy, 100.00% precision, 93.75% recall, 96.77% F1-score, 95.95% validation accuracy, and a 0.15 validation loss with a processing time of 3.03 minutes. It was possible to develop a CNN model capable of identifying seven emotional states from only the data of the eye and mouth region using concatenated images.

关键词： Facial gestures emotion recognition data concatenation FER CNN image segmentation

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Research on Detection Algorithm Based on YOLO and Lightweight Network EfficientNet

Research on Detection Algorithm Based on YOLO and Lightweigh...

引用

image processing and Media Computing (ICIPMC), International conference on

作者： Yang Zhang Zhongxiao Chen JingYu Zhang YiFan Zhao School of Electronic Information Engineering Xi’an Technological University Xi’an China School of Mechatronic Engineering Xi’an Technological University Xi’an China

Deep learning based object detection algorithms have been applied in various fields of life. As a representative of one-stage detection algorithms, YOLO series algorithms are highly favored for their fast *** resolve the existing issues with complex network architecture and weak real-time performance. YOLOv3 and YOLOv4 are improved by adding the lightweight network EfficientNet, using deep separable convolution rather than regular convolution, reducing a significant number of network parameters, using transfer learning to speed up the model’s training speed, and speeding up the model’s convergence. Finally, the accuracy, number of parameters, and detection rate of the network structure before as well as after improvement are compared to ensure that the model is successful. According to the experimental findings, the number of parameters for E-YOLOv3 and E-YOLOv4 are only 1/9 and 1/7 of what they were before the enhancement, and the detection rate has increased by 4 and 3 frames/second, respectively.

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An algorithm for parallel processing of traffic signs video on a graphics processor

An algorithm for parallel processing of traffic signs video ...

引用

2022 International conference on Information Science and Communications Technologies, ICISCT 2022

作者： Umarov, Mukhriddin Elov, Jamshid Khalilov, Sirojiddin Narzullayev, Inomjon Karimov, Marat Samarkand Br. of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Software Engineering Samarkand Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Nurafshan Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Information Technologies Tashkent Uzbekistan

ISBN: (纸本)9781665472296

In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects. © 2022 IEEE.

关键词： image enhancement

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A Two-Step Reconstructed Method Based on Deep Learning for Compressive Sensing

A Two-Step Reconstructed Method Based on Deep Learning for C...

引用

6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Yang Liu Yuenan Li Feng Li xiaobo Li Yingzhao Shao xiaosong Zheng China Academy of Space Technology (Xi'an) Xi'an China Qian Xuesen Laboratory of Space Technology Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Deep learning (DL) has been successfully applied to recover compressive sensing (CS) signals in recent researches, which significantly reduce the time complexity than conventional model-based reconstruction algorithms. However, some important elements in the CS theory such as sparsity and other priori constraints are abandoned in these DL-based methods, which leads a poor performance in reconstruction results at a high sampling ratio. In this paper, a novel reconstruction method is proposed to introduce sparsity into neural networks for CS reconstruction. More specifically, a two-step reconstruction algorithm based on mixed sparse representations and deep learning are designed to implement the purpose. To the best of our knowledge, there exists no published work that achieves the desirable function. Given a certain sampling ratio, the reconstructed image quality is much better than other CS related image acquisition and compression methods. The framework holds the promise to be widely used in the near future.

关键词： Deep learning image quality image coding Sparse approximation Neural networks Reconstruction algorithms Time complexity image reconstruction Compressed sensing Standards

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Research on Construction Method of Massive Geographic image Database for Power Grid EIA 4th

Research on Construction Method of Massive Geographic Image ...

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4th International conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2021

作者： Wu, Yu Li, Zun Guo, Yang Zhang, Songyang Zhang, Zhiguo Liu, Zhentao Han, Xutao Sun, Wei State Grid He-Nan Electric Power Company Zhengzhou China Henan Jiuyu Enpai Power Technology Co. Ltd Zhengzhou China Xidian University Xi’an China Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (纸本)9789811640384

With the continuous development of science and technology, the capacity of data is also showing a geometric increase. Therefore, this paper mainly studies how to store query, change, and replace a large number of geographic images. The text uses SQL server platform and ArcGIS geographic software processing platform. It solves the problem of processing limit exceeding the database in the face of massive database, insufficient network bandwidth compared with massive images, slow image display speed, storage problem of massive image data, and so on. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Database systems

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A Variational Auto-Encoder Framework With Attention Mechanism for image Captioning

A Variational Auto-Encoder Framework With Attention Mechanis...

引用

2022 International conference on Optics and Machine Vision, ICOMV 2022

作者： Sun, Qingqing Xi'an University of Electronic Science and Technology No. 266 Xinglong section Xifeng Road Chang'an District Xi'an city710126 China

ISBN: (纸本)9781510653238

As two core fields of machine learning, Natural Language processing and Computer Vision have derived a variety of cross research topics, and image captioning as a key topic of the current cross field has an important relationship with the development of both. image captioning has been attached importance to by researchers for a long time, and has formed a relatively perfect theoretical model system. At present, this topic has made great progress in accuracy, but this is only one aspect of the evaluation of image content description algorithm, which has new requirements in diversity, stylization and controllability. Based on this, this paper studies the technical routes and modeling methods of various auto-coding models, variational models and attention mechanisms, proposing a new way to solve the inherent contradiction between diversity and accuracy, that is to increase the diversity of each sample while ensuring the upper bound of accuracy index scores in multiple samples. The test results on MS COCO data set show that the model implemented in this paper has good performance in accuracy and diversity. © 2022 SPIE

关键词： Learning algorithms

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Multi-Scale Adaptive Network for Single image Denoising 36

Multi-Scale Adaptive Network for Single Image Denoising

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Gou, Yuanbiao Hu, Peng Lv, Jiancheng Zhou, Joey Tianyi Peng, xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9781713871088

Multi-scale architectures have shown effectiveness in a variety of tasks thanks to appealing cross-scale complementarity. However, existing architectures treat different scale features equally without considering the scale-specific characteristics, i.e., the within-scale characteristics are ignored in the architecture design. In this paper, we reveal this missing piece for multi-scale architecture design and accordingly propose a novel Multi-Scale Adaptive Network (MSANet) for single image denoising. Specifically, MSANet simultaneously embraces the within-scale characteristics and the cross-scale complementarity thanks to three novel neural blocks, i.e., adaptive feature block (AFeB), adaptive multi-scale block (AMB), and adaptive fusion block (AFuB). In brief, AFeB is designed to adaptively preserve image details and filter noises, which is highly expected for the features with mixed details and noises. AMB could enlarge the receptive field and aggregate the multi-scale information, which meets the need of contextually informative features. AFuB devotes to adaptively sampling and transferring the features from one scale to another scale, which fuses the multi-scale features with varying characteristics from coarse to fine. Extensive experiments on both three real and six synthetic noisy image datasets show the superiority of MSANet compared with 12 methods. The code could be accessed from https://***/XLearning-SCU/2022-NeurIPS-MSANet.

关键词： image denoising

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Platform for Interactive Audiovisual Productions and Generative Art Using processing 1

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Multidisciplinary International conference of Research Applied to Defense and Security, MICRADS 2022

作者： D‘Amato, Juan P. Leon, Diana Karina PLADEMA—UNCPBA Institute Campus Argentino Tandil Argentina National Scientific and Technical Research Council Tandil Argentina

ISBN: (数字)9789811976896

ISBN: (纸本)9789811976889

Through computers, artists have found a way to enhance their production, discovering new ways for communicating their productions and devising new forms of expression. Being able to make the most of these facilities requires interdisciplinary groups that seek to enhance creativity and at the same time generate new forms of creation. In particular, an artist takes advantage of the different tools available, from sound to draws in order to provide immersive experiences. This paper presents a platform that implements image and audio processing algorithms, to facilitate the artist's task, in what is called generative art. With the platform it is possible to create animated digital images in real time that adapt to the sound’ properties and that can be used in live audiovisual performances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Audio acoustics

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