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

共 12978 条记录，以下是1171-1180 订阅

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

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18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Automatic target detection utilizing an Edge IR vision transformer (EIR-ViT) 33

Automatic target detection utilizing an Edge IR vision trans...

引用

conference on Automatic Target Recognition XXXIII

作者： Adams, Ethan R. Depoian, Arthur C., II Kurz, Aidan G. Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510661561;9781510661578

The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR Vision Transformer (EIR-ViT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.

关键词： Automatic Target Detection Vision Transformer Infrared Imaging Object Detection FLIR image FLIR dataset Edge Computing

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Enhanced DSP Blocks for Full High-Definition Video Bridging Application on FPGAs

Enhanced DSP Blocks for Full High-Definition Video Bridging ...

引用

International conference on Communications, Circuits and systems (ICCCAS)

作者： Panfeng Wang Yici Cai Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

FPGA chips are widely used in video bridging applications. The DSP (Digital Signal processing) in FPGA can effectively improve the efficiency of video image processing algorithms. The 10 bits of pixel depth is common for Full High-Definition (FHD). However, in the existing commercial FPGAs, the DSP does not naturally support 10x10 multiplication. This brings some waste in area and power consumption. This paper proposes a new architecture of embedded DSP. It increases the maximum number of 10x10 multiplications in a DSP block to 8. It is also compatible with common working modes such as multiplication (18x18, 18x9), and accumulation. Meanwhile, introduce power gate and clock gate units in the DSP block to achieve fine-grained control of the power consumption. Users can dynamically control the power supply through their own logic. The entire circuit was successfully designed and implemented in an industrial 22nm process. The experimental results show that the static power consumption of the DSP block is about 20uW, the dynamic power consumption is about 200uW. And the maximum operating frequency can reach 670MHz. The effective area utilization of our DSP in 10x10 mode is 2.2 and 2.5 times higher than that in Intel Arria 10 series and Xilinx UltraScale series FPGAs, respectively.

关键词： Power demand Circuits and systems image processing Signal processing algorithms Digital signal processing Logic gates Logic

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Smart Oral Health Monitor

Smart Oral Health Monitor

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Karthik S Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder.

关键词： Deep learning image processing Teeth Cameras Real-time systems Mobile applications Monitoring Diseases

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Fast Object Detection Algorithm using Edge-based Operation Skip Scheme with Viola-Jones Method

Fast Object Detection Algorithm using Edge-based Operation S...

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IEEE International conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Cheol-Ho Choi Joonhwan Han Jeongwoo Cha Jungho Shin Hyun Woo Oh Pangyo Research and Development (R&D) Center Hanwha Systems Co. Ltd. Seongnam Republic of Korea

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Machine learning approaches are preferred over deep learning in embedded systems due to their resource efficiency. The widely adopted Viola-Jones method and related algorithms are selected for their high detection accuracy and reasonable processing speed. However, a limitation arises as processing time increases with additional classification iterations based on sub-window operations. To address this issue, we propose an enhanced object detection algorithm that incorporates the Viola-Jones method with edge component calibration and an edge-based operation skip scheme. The introduction of edge component calibration ensures detection performance comparable to conventional methods. This scheme, relying on edge values, significantly reduces unnecessary computations in the background, leading to a marked decrease in classification operations compared to conventional methods. Visual comparisons in experimental results demonstrate that our method increases the detection precision factor while maintaining recall. In terms of classification operations, our approach reduces their number by 31.38% to 85.78% compared to conventional methods. In simpler terms, our method improves processing speed by minimizing classification operations, making it well-suited for embedded systems with limited resource utilization.

关键词： Visualization Embedded systems Machine learning algorithms image edge detection Object detection Transforms Classification algorithms

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A Low-Cost Deployment Case Study for Industrial IoT Data Collection and processing

A Low-Cost Deployment Case Study for Industrial IoT Data Col...

引用

Intelligent Technology and Embedded systems (ICITES), International conference on

作者： Jiajun Zhang Biao Hu College of Engineering China Agricultural University Beijing China

ISBN: (数字)9798331506292

ISBN: (纸本)9798331506308

Despite the widespread implementation of SCADA systems in factories for centralized data management, their functionality is restricted to devices equipped with sensors. Manual readings are still prevalent for critical instruments like temperature and pressure gauges, lacking interfaces, leading to inefficiencies and errors. Upgrading production line sensors with IoT-enabled instruments is costly and disruptive. To tackle this, we propose a cost-effective data acquisition system that leverages edge acquisition devices and deep learning algorithms without altering the existing equipment and processes. Our solution involves installing new sensors or cameras in critical areas and employing image recognition algorithms alongside indirect measurement techniques. This approach enhances the real-time accuracy of data collection, significantly boosts the overall efficiency of the production process, and reduces operating costs.

关键词： Temperature sensors Temperature measurement Deep learning Accuracy image edge detection Data acquisition Data collection Production facilities Sensors Edge computing

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Raindrop Removal for In-Vehicle Camera images with Generative Adversarial Network

Raindrop Removal for In-Vehicle Camera Images with Generativ...

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2022 IEEE International conference on systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zihao Jiang, Min Guo, Jia Yang, Xiaoyu Hu, Yudie Zhou, Xianlong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Raindrop removal for in-vehicle camera images is useful for surveillance and analysis system such as intelligent driving or case diagnosis. As in rainy days, images taken by in-vehicle cameras such as monitor or drive recorder often suffer from noticeable image degradation, and make it difficult to identify the objects on the road. With the uncertainty of raindrop distribution and the complexity of raindrop status, the attached raindrops of in-vehicle camera images can have different effects. And the large-diameter raindrop will greatly reduce the image quality. However, most of the popular deraining algorithms have been successful in recovering images with small raindrop noise and tiny image distortion, but fails to restore those with raindrops covering large areas. We propose a single image raindrop removal network based on Generative Adversarial Network for in-vehicle camera images. We incorporate two overlapping attention layers into the deraining network, which adopt task-driven visual attention and content perception mechanism. The former obtains features by recursive neural network to guide network more interested in the raindrop regions and the surrounding structures, while the content perception schema extracts the features far from raindrops. Moreover, we collect a new benchmark for In-Vehicle Camera image Deraining (IVCRID), it chooses in-vehicle camera images from various deraining dataset. The experimental results show that the proposed deraining network outperforms other state-of-the-art raindrop removal methods in image recovering test on the IVCRID dataset and also has the best performance on the experiments of traffic objects detection. © 2022 IEEE.

关键词： Generative adversarial networks

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Autonomous Drone for Surveying Flooded Area and Performing Optimal Rescue Operation Using image processing

Autonomous Drone for Surveying Flooded Area and Performing O...

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conference on Modeling of systems Circuits and Devices (MOS-AK India)

作者： L David William Raj S Sampreeth R Sri Lakshmi Leena Juliet S Sneha Department of ECE MVJ College of Engineering Bangalore India

ISBN: (数字)9798350395556

ISBN: (纸本)9798350395563

During life threatening conditions caused due to natural disasters, search and rescue operations manually has not been possible to implement quickly. To overcome this issue, the proposed methodology provides a solution of using an autonomous drone with the help of image processing techniques in flooded scenarios. When any victims are identified, our proposed system aids in the rescue operations by visually screening the flooded areas and sending a signal to the rescue team. Using this UAV camera several image processing algorithms are performed to detect survivors to approximate the area of the flooded region.

关键词： Technological innovation image segmentation Disasters Autonomous aerial vehicles Cameras Approximation algorithms Classification algorithms

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Multi-Modal Tracking Using LiDAR and Visual Signals 11

Multi-Modal Tracking Using LiDAR and Visual Signals

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11th International conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Kumar, Sanjay Hassan, Mohamed Escudero-Vinolo, Marcos Hannan, Abdul Manzoor, Anam Godinho, Antonio Pires, Ivan Miguel Coelho, Paulo Jorge Autonomous University of Madrid Madrid Spain University of Bordeaux Bordeaux France University of Management and Technology Punjab Pakistan Polytechnic Institute of Coimbra Technology and Management School of Oliveira Do Hospital Oliveira Do Hospital Portugal Research Center for Arts and Communication Santarém Polytechnic University Santarém Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestião de Agueda Universidade de Aveiro Agueda Portugal School of Technology and Management Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal

ISBN: (纸本)9798350355314

Multi-object tracking (MOT) is a crucial technique for detecting and tracking multiple objects over time in a scene. It involves locating objects in consecutive frames of a video or sequential observations and establishing correspondences to maintain their identity. MOT is essential in various applications, including autonomous vehicles, robotics, and video surveillance. In autonomous vehicles, it is typically performed using 3D models (LiDAR/stereo) or 2D models (one or more RGB cameras). The LiDAR 3D point-cloud provides a 360-degree view of its surroundings but has a low resolution compared to the camera's visual signals. This paper presents a sensor fusion approach for multi-object detection and tracking, combining object detection information from sensors, LiDAR, and cameras and applying data association algorithms like Hungarian algorithms for tracking to increase the overall system. The EagerMOT approach for 3D Multi-Object Tracking via Sensor Fusion achieves state-of-the-art results across several MOT tasks on the KITTI dataset, achieving an accuracy of 87.95 %. © 2024 IEEE.

关键词： Stereo image processing

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A GPU Parallel Stitching Algorithm for Low-Altitude Parallax Scenes

A GPU Parallel Stitching Algorithm for Low-Altitude Parallax...

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Machine Learning and Computer Application (ICMLCA), International conference on

作者： Sha Liu Shan Zhou Yan Hu China Electronics Technology Group Corp 54th Research Institute Shijiazhuang China China Electronics Technology Group Corp 20th Research Institute Xi'an China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

Panorama stitching of low-altitude captured images is a very interesting and challenging task. Since 3D scenes are not planar, there may be significant changes in the relative positions of scene structures in each perspective in the images, thus causing parallax. Existing image stitching algorithms often fail in parallax scenes and are not efficient enough to meet the real-time processing requirements of drones. To address this problem, we introduce gridded dense projection estimation and design a parallel computing unit suitable for GPUs for rapid modeling of projection fields. Experimental results show that our algorithm significantly outperforms the comparison algorithms in terms of computational performance, has high real-time performance, and achieves nearly the same level of stitching quality and naturalness.

关键词： Three-dimensional displays Computational modeling Graphics processing units Machine learning Parallel processing Real-time systems Partitioning algorithms Computational efficiency image stitching Periodic structures

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