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

共 13664 条记录，以下是1361-1370 订阅

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An Autonomous Rover for Weed Detection

An Autonomous Rover for Weed Detection

引用

Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Gadiyaram Veda Lakshmi Durga Haritha V Naga Prudhvi Raj Gona Dayakar Bandaru Sneha Mohammad Nafisa Gadiyaram Balaji Prabhakara Sai Phaneedra Department of EIE VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This project enhances agricultural productivity and promotes sustainable farming through an autonomous weed detection rover. Powered by a Raspberry Pi, the rover integrates a high-definition camera and advanced image processing algorithms to differentiate weeds from crops in real time using visual attributes like color, texture, and shape. Key components, including motor drivers and MOSFETs, enable efficient power management and autonomous navigation, reducing human intervention. The system significantly decreases manual labor by approximately 80 % while ensuring precise herbicide application, minimizing environmental impact. This scalable and efficient solution highlights the potential of intelligent automation in agriculture, fostering sustainable weed management and improving long-term crop yields.

关键词： Productivity Precision agriculture Visualization Accuracy image color analysis Manuals Real-time systems Chemicals Crop yield Farming

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Optimizing Hyperspectral image Enhancement with FPGA Hardware Accelerators

Optimizing Hyperspectral Image Enhancement with FPGA Hardwar...

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Recent Advances in Science and Engineering Technology (ICRASET), International conference on

作者： N Rajesh I G Naveen N Shankari Parth Sheetal Kumthekar Sufiyan Ahmed Khan Rayyan Mohammed Department of ECE Nite Meenakshi Institute of Technology Bengaluru India NMAM Institute of Technology Nitte (Deemed to be University) Nitte Karnataka India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Hyperspectral image processing plays a crucial role in various applications, including remote sensing and agricultural monitoring, yet it poses significant computational challenges due to the high dimensionality and intricate data structures involved. Traditional software-based methods often suffer from high processing time and latency, limiting their effectiveness in real-time applications. To address these challenges, we propose a hardware accelerator implemented on the Genesys 2 Kintex-7 FPGA Development Board, designed specifically for hyperspectral image enhancement. This FPGA-based solution employs parallel processing and optimized algorithms, including interpolation and super-resolution techniques, to significantly improve processing efficiency. We evaluate the performance of our proposed method using the Indian Pines dataset, a standard benchmark for hyperspectral imaging. Experimental results demonstrate that our FPGA implementation achieves a processing time of 50 ms, significantly faster than CPU and GPU solutions, while also reducing power consumption and improving energy efficiency. These findings underscore the potential of FPGA technology for enhancing hyperspectral image processing in resource-constrained environments.

关键词： Power demand Superresolution Graphics processing units Parallel processing Real-time systems Field programmable gate arrays image enhancement Standards Hardware acceleration Hyperspectral imaging

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Transform-domain image Enhancement for Underwater Visual SLAM based on Homomorphic Filter 3

Transform-domain Image Enhancement for Underwater Visual SLA...

引用

3rd International conference on image processing, Computer Vision and Machine Learning, ICICML 2024

作者： Zhang, Shuo Zhang, Chaoyu Wang, Wei Zeng, Xinrui Luo, Bin Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China China Nuclear Power Operation Technology Corporation LTD Wuhan China CNNC Engineering Research Center of Nuclear Industry Special Robot Wuhan China

ISBN: (纸本)9798350355413

Underwater visual SLAM algorithms play a vital role in autonomously localizing Autonomous Underwater Vehicles (AUV) in challenging underwater environments. The low-light conditions underwater necessitate the use of artificial lighting sources. However, this can lead to issues such as overexposed or underexposed images, resulting in reduced image contrast and information content. This study introduces a transform-domain image enhancement technique aimed at bolstering the resilience of underwater visual simultaneous localization and mapping (SLAM) systems. Specifically, a homomorphic filtering method is implemented to alleviate the impact of low-frequency lighting variations on images, thereby enhancing overall image clarity and bolstering the performance of visual SLAM systems. The efficacy of this filtering approach is validated through experiments conducted on the AQUALOC dataset. Our findings demonstrate that this method enhances the success rate of visual SLAM tracking, thereby enhancing its utility in AUV localization tasks. © 2024 IEEE.

关键词： image enhancement

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Task Offloading Based on Multi-agent Reinforcement Learning for UAV-assisted Edge Computing

Task Offloading Based on Multi-agent Reinforcement Learning ...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Yao Dai Yuan Li Ting Lyu CITIC Dicastal Co. Ltd. Qinhuangdao China Beijing Commsat Technology Development Co. Ltd. Beijing China University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The field of Edge Computing (EC) has emerged as a significant advancement that allows Internet of Things (IoT) devices to efficiently manage complex tasks while prolonging their operational lifespan. The focus of this study is on the utilization of unmanned aerial vehicles (UAVs) to augment edge computing systems with the goal of minimizing energy usage during computational tasks. We propose a novel approach that employs multi-agent reinforcement learning to facilitate task offloading in a UAV-supported edge computing setup. This method swiftly devises offloading strategies without the protracted computations typically associated with conventional optimization algorithms. Through experimental trials, we have found that our proposed solution not only reduces the energy required for computing tasks but also enhances the overall efficiency of task processing and demonstrates robust performance stability.

关键词： Performance evaluation image processing Autonomous aerial vehicles Minimization Deep reinforcement learning Stability analysis Internet of Things Resource management Optimization Edge computing

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Power Efficient Real-Time Traffic Signal Classification for Autonomous Driving Using FPGAs

Power Efficient Real-Time Traffic Signal Classification for ...

引用

International conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Rashed Al Amin Md Shahi Amran Hossain Leon Tjard Schmid Veit Wiese Roman Obermaisser Institute for Embedded Systems University of Siegen Siegen Germany

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

The significant advancements in research within the domain of Advanced Driver-Assistance systems (ADAS) have been significantly propelled by the integration of Artificial Intelligence (AI) and Deep Learning (DL). The detection and classification of traffic signals are pivotal for ensuring driver safety and comfort, prompting the development of numerous detection and classification methodologies in recent times. Convolutional Neural Networks (CNNs) and Field Programmable Gate Arrays (FPGAs) have individually demonstrated notable efficacy in signal processing, image detection, and image classification tasks. However, the amalgamation of CNNs and FPGAs for traffic signal classification offers enhanced flexibility, accuracy, and resource efficiency. This study proposes a software-hardware co-design approach for the development of a resource-efficient real-time traffic signal classification system tailored for FPGAs utilizing the CNN algorithm. The software implementation has been carried out within the TensorFlow framework, while the hardware implementation is executed via the Xilinx Vivado 2023.1 platform and rigorously evaluated using the Xilinx ZCU102 FPGA board. Experimental findings demonstrate that the traffic signal classification accuracy can attain approximately 99.98% at a frame rate of 84139 frames per second (FPS) on the FPGA platform, with a power consumption of 4.4W. The exceptionally low latency and heightened accuracy exhibited by the proposed system render it highly advantageous for ADAS and other safety-critical applications.

关键词： Accuracy Software algorithms Pattern classification Signal processing algorithms Real-time systems Software Safety Convolutional neural networks Artificial intelligence Field programmable gate arrays

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Application of image processing Based on Improved Canny Algorithm and Gurukin's Theorem 23

Application of Image Processing Based on Improved Canny Algo...

引用

6th International conference on Information Science and systems, ICISS 2023

作者： Xie, Yufei Yu, Weiwei Song, Shasha Wang, Wenlin Jia, Ying Gao, Xin Wang, Chongyang College of Pharmacy Shenzhen Technology University Shenzhen518118 China School of Automation Wuhan University of Technology Wuhan430070 China Yantai Yuhuangding Hospital Yantai264001 China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen518118 China Daqing Longnan Hospital Daqing163458 China College of Electronic and Information Engineering Tongji University Shanghai200092 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Wuhan China

ISBN: (纸本)9798400708206

Placental volume is an important indicator for evaluating fetal health, and automatic identification of placenta and automatic calculation of its volume is of great significance in clinical practice. Due to the complexity and diversity of placental images, domestic and foreign scholars have proposed a variety of image recognition and segmentation methods. Among them, the convolutional neural network method is widely used, but this method has black box theory, which is difficult to explain and requires a large number of training samples. In this context, this paper proposes an improved Canny algorithm based on meshing and OTSU for edge detection, recognition and segmentation of placental images. The placenta volume was calculated by using Gurukin's theorem, and the calculation results were compared with the results of manual calculation by doctors. The results show that the algorithm model proposed in this paper is convenient to calculate, and the error of manual calculation is within 10%. The feasibility of the method has been verified. Especially when the sample size is not enough to use machine learning algorithms, it provides strong support for clinical decision-making. © 2023 ACM.

关键词： image recognition

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Exploring the Efficacy of Spatial Subsampling Methods for 3D image Projection for 2D Classification

Exploring the Efficacy of Spatial Subsampling Methods for 3D...

引用

IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Alif Aditya Wicaksono I Ketut Eddy Purnama Reza Fuad Rachmadi Feby Artwodini Muqtadiroh Ardyono Priyadi Mauridhi Hery Purnomo Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

In this paper we purpose several methods for creating 2D projection image from a 3D scan such as from CT or MR scans, and then look at different types of 2.5D projections with a growing window sizes and see its effects on the efficacy of the machine learning model when combined with the selection algorithms. As such we are using Shi-Tomasi Detectors, Sobel Edge Detectors, Sum of Pixel Intensity, Center Select, and Random Selection as selection algorithms from which we select the most interesting index for a given scan, from which depending on the window size, we stack the neighboring slices in the axial plane to the channel dimension creating a 2.5D image from which we could pass into our ResNet-9 model configured for classification and train the model for a given number of epochs, under the same conditions for all of our tests, this ensures that for all of our experiments the main effector is window size and the selection algorithm. From our tests we found that the Center Select on average outperforming the other selection algorithm, this result indicate that medical information is mostly centered around the scan, and when combined with the saving of 60% less parameters, the purposed method shows a new way of processing 3D Scans for processing with a 2D CNN model.

关键词： Solid modeling Three-dimensional displays Machine learning algorithms image edge detection Computed tomography Neural networks Detectors Machine learning Classification algorithms Indexes

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Personal image Classifier Based Handy Pipe Defect Recognizer (HPD): Design and Test 7

Personal Image Classifier Based Handy Pipe Defect Recognizer...

引用

7th International conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Moshayedi, Ata Jahangir Khan, Amir Sohail Yang, Shuxin Zanjani, S. Mohammadali Jiangxi University of Science and Technology School of Information Engineering Ganzhou China Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9781665478571

Pipelines are known as a traditional solution for transporting various media such as gas, oil, and water. But pipes are always combined with the defects. Some of these defects are caused by the manufactory process, while some defects occur after installation due to the type of medium and environmental condition. Currently, various methods are used to detect these defects from industry to the site, but vision-based systems due to huge amount of data that can capture machine learning development algorithms have more demand. In this research paper Personal image Classifier (PIC) is used as the machine learning method combine with the MIT APP inventor to make the handy system called Handy Pipe Defect assistant recognizer (HPD) to address the defect name and help the user to investigate and somehow correct their process of product in industry (as the production stage) or the possible change in usage place (as the maintenance stage). The HPD designed based on the hypothesis of having the handy, portable and available tool for the operator in industries as the quality control and site engineers. The HPD can classify the pipe defects, especially the welding type by taking the picture of the defect and give the user feedback. The HPD focuses on the welding defect with the manufactory source over 150 image number trained and tested with the real 28 sample image. The design process and training model for HPD described and result based on sample images from manufactory and real situation are shown the 100% defect name detection of the defect along with image quality affected by environment light and similarity between the defects. © 2022 IEEE.

关键词： Defects

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Lightweight YOLOv4 Algorithm for Remote Sensing image Detection 14

Lightweight YOLOv4 Algorithm for Remote Sensing Image Detect...

引用

14th International conference on Signal processing systems, ICSPS 2022

作者： Ma, Li He, Tong Sun, Yong Hu, Bin-Bin College of Information Liaoning University Shenyang China

ISBN: (纸本)9798350336313

Remote sensing images have the characteristics of complex backgrounds, high resolution, and small targets. Although the existing object detection algorithms can improve the detection accuracy, there are generally problems such as a large number of model parameters, high computational cost, and poor real-time performance. Aiming at the above problems, this paper designs a lightweight object detection algorithm GSC-YOLO based on YOLOv4 to achieve fast and accurate detection of remote sensing images. First, Ghostnet is used as the feature extraction network of GSC-YOLO to reduce the number of parameters and improve the detection speed;Secondly, the improved shuffle attention mechanism is introduced in the prediction head to make the model pay attention to important information and improve the detection accuracy;Finally, the Confidence Propagation Cluster algorithm CP-Cluster is used to post-process the prediction frame to improve the object recognition. Taking the preprocessed DOTA dataset as the experimental object, the experimental results show that the GSC-YOLO algorithm has a detection accuracy of 93.44%, a detection speed of 58 frames per second, and a model size of 43.65MB. Compared with the remote sensing image object detection algorithm based on YOLOv4, the detection accuracy is increased by 3.93%, the detection speed is increased by 1.87 times, and the model size is reduced by 5.62 times, which is more suitable for deployment on devices with limited resources. © 2022 IEEE.

关键词： Object recognition

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Pre-processing algorithms for Accurate Analysis of an image: A Review

Pre-Processing Algorithms for Accurate Analysis of an Image:...

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International conference on Smart systems and Inventive Technology (ICSSIT)

作者： Yasaswini Satuluri K Venkata Ratna Prabha Jairam Swamy Polavarapu Harsha Vardhan Battina Venu Kota P Ramesh Dept. of Electronics and Communication Engineering V R Siddhartha Engineering College Vijayawada India Dept. of Conservative Dentistry and Endodontics Dr. S&NS Institute of Dental Sciences Chinoutpalli AP India

Real-world databases nowadays are particularly vulnerable to noisy, missing and inconsistent data due to their large size (often several terabytes or so more), as well as the potential that they come from multiple and diverse sources. Poor mining findings will result from low-quality data. To produce the appropriate image for a better analysis, images from the relevant dataset are pre-processed using a variety of currently available approaches. In this work, some of the numerous pre-processing methods are discussed and it is observed that filtering techniques like Gabor filter are more popular than other state-of-the-art techniques like interpolation, kernels etc.

关键词： Histograms Interpolation Convolution Filtering image edge detection Filtering algorithms Adaptive equalizers

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