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

共 13718 条记录，以下是1391-1400 订阅

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In-Sensor Learning on Magnetic Tunnel Junction Devices Using the Forward-Forward Algorithm

In-Sensor Learning on Magnetic Tunnel Junction Devices Using...

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Sensors and Information Technology (ICSI), International conference on

作者： Jiayuan Wang Minhui Ji Liyuan Yang Yueguo Hu Mengchun Pan Jiafei Hu Peisen Li College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Stomatology The Fourth Military Medical University Xian China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Aiming at the real-time processing requirements in edge computing devices, we construct in-sensor computing units based on magnetic tunnel junction (MTJ) arrays, which realizes in-situ computation of vector-matrix multiplication with parameters dynamically programmed to adapt to different tasks. Different from the previously used back-propagation (BP) algorithm, we innovatively introduce the forward-forward (FF) algorithm, which utilizes the positive and negative samples “Goodness” optimization mechanism to reduce the computational complexity. In the noisy image classification task, the FF algorithm reduces the training time by more than 90% compared to the BP algorithm and significantly improves the convergence speed when perfect classification results are achieved.

关键词： Training Heuristic algorithms Real-time systems Classification algorithms Computational efficiency Noise measurement Junctions Magnetic tunneling image classification Convergence

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An Autonomous Rover for Weed Detection

An Autonomous Rover for Weed Detection

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

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

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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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Implemention of image processing Platform on Low-cost ZedBoard FPGA Using Xilinx-Vivado

Implemention of Image Processing Platform on Low-cost ZedBoa...

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Advances in Electrical and Communication Technologies (ICAECOT), International conference on

作者： Mehdi Baali Kamel Messaoudi Nadjla Bourbia El-Bay Bourennane Electrical Engineering and Renewlable Energies Laboratory (LEER) Mohemed Cherif Mssaadia University Souk-Ahras Algeria Laboratory for the Study of Electronic Materials for Medical Applications Mentouri Brothers University Constantine 1 Algeria ImVia Laboratory - Burgundy University Dijon France

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

The execution of complex functions and high computational tasks remains a major obstacle to efficient and transparent real-time processing. Overcoming these challenges is essential to unlocking the full potential of image and video processing technologies, and enabling their widespread adoption across diverse industries and applications. The main objective of this paper is to design an integrated image processing system based on ARM processor. This system aims to perform feature extraction and facilitate the high-speed of data transmission. The implementation results highlight the successful use of system resources, showing efficient allocation of hardware components and computing resources. Through careful design and optimization, we have achieved efficient use of available resources, ensuring that hardware IPs and associated algorithms are effectively exploited to achieve optimum performance.

关键词： Energy consumption Program processors Computer architecture Streaming media Hardware Real-time systems Planning Resource management Streams Optimization

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6th International conference on Big Data Cloud and Internet of Things, BDIoT 2022

6th International Conference on Big Data Cloud and Internet ...

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6th International conference on Big Data Cloud and Internet of Things, BDIoT 2022

ISBN: (纸本)9783031283864

The proceedings contain 49 papers. The special focus in this conference is on Big Data Cloud and Internet of Things. The topics include: Knowledge Embeddings for Explainable Recommendation;Design of Blended Learning Course Based on SPOC for Primary School Teachers: Case of Soft Skills;the Impact of the Online Flipped Classroom on the Learning Outcomes and Motivation of Nursing Students in Computer Science;digitizing Writing: An Empirical Exploratory Study Towards Students’ Writing Skill Fluency Improvement in ‘Padletosphere’;AraBERT with GANs for High Performance Fine-Grained Dialect Classification;genetic-Novelty Oversampling Technique for Imbalanced Data;neural Network for Arabic Text Diacritization on a New Dataset;processing Time Performance Analysis of Scheduling algorithms for Virtual Machines Placement in Cloud Computing Environment;Business - IT Alignment in Cloud Environment Comparative Study;machine Learning-Based Intrusion Detection System: Review and Taxonomy;reduce Cheating in e-Exams Using Machine Learning: State of the Art;comparative Study of Bayesian Optimization Process for the Best Machine Learning Hyperparameters;The Effect of Normalization and Batch Normalization Layers in CNNs Models: Application to Plant Disease Classifications;the Role of Ground Truth Annotation in Semantic image Segmentation Performance for Autonomous Driving;fine-Tuning Pre-trained Vision Transformer Model for Anomaly Detection in Video Sequences;An Efficient Real-Time Moroccan Automatic License Plate Recognition System Based on the YOLO Object Detector;evaluation of Selected Machine Learning Models and Features for Electrical Consumption Prediction in Educational Institutions;Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised Machine Learning algorithms;SNDAE: Self-Normalizing Deep AutoEncoder for Recommendation;Intelligent Evaluation System Using NLP;retrospective Study and Evaluation of School Failure (Junior High School) in Scientific Subj

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

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

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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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1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

1st EAI International Conference on Intelligent Systems and ...

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1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

ISBN: (纸本)9783031350771

The proceedings contain 75 papers. The special focus in this conference is on Intelligent systems and Machine Learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach;a Novel Oversampling Technique for Imbalanced Credit Scoring Datasets;a Blockchain Enabled Medical Tourism Ecosystem;Measuring the Impact of Oil Revenues on Government Debt in Selected Countries by Using ARDL Model;diagnosis of Plant Diseases by image processing Model for Sustainable Solutions;face Mask Detection: An Application of Artificial Intelligence;a Critical Review of Faults in Cloud Computing: Types, Detection, and Mitigation Schemes;video Content Analysis Using Deep Learning Methods;prediction of Cochlear Disorders Using Face Tilt Estimation and Audiology Data;F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets;quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges;multivariate Analysis and Comparison of Machine Learning algorithms: A Case Study of Cereals of America;competitive Programming Vestige Using Machine Learning;machine Learning Techniques for Aspect Analysis of Employee Attrition;AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance;Real-Time Identification of Medical Equipment Using Deep CNN and Computer Vision;design of a Intelligent Crutch Tool for Elders;an Approach to New Technical Solutions in Resource Allocation Based on Artificial Intelligence;gesture Controlled Power Window Using Deep Learning;novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition;a Novel Model to Predict the Whack of Pandemics on the International Rankings of Academia.

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A 640x512 30μm Pixel Pitch 1.8mK-NETD 90.1dB-SNR Digital Read-out Integrated Circuit with Fully On-chip image Algorithm Pixel-Level Calibration

A 640x512 30μm Pixel Pitch 1.8mK-NETD 90.1dB-SNR Digital Re...

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17th IEEE Asian Solid-State Circuits conference (A-SSCC) - Integrated Circuits and systems for the Connection of Intelligent Things

作者： Zeng, Yan Yang, Shiheng Liu, Yueduo Li, Zehao Huang, Wengang Huang, Xiaozong Zhou, Xiong Liu, Jiaxin Li, Qiang Univ Elect Sci & Technol China Chengdu Peoples R China China Elect Technol Grp Corp Res Inst 24 Chongqing Peoples R China

ISBN: (纸本)9781665443500

Infrared focal plane arrays (IRFPAs) have been widely used in industrial, scientifical and medical imaging applications [1]–[5]. Due to the limitation of the manufacturing process, material quality and other factors, IRFPAs inevitably suffer from the current non-uniformity under the same light intensity; and the bad pixel with extreme photoelectric response which significantly degrade the quality of the image. In addition to the bad pixel compensation and non-uniformity calibration, some other processing algorithms are often implemented for IRFPAs to improve the image quality, such as spatial filtering, background subtraction, etc. Conventionally the image processing algorithms are implemented by caching one or more frames of images via on-chip memory or FPGA first, and then the algorithmic processing is performed on the cashed image data. It takes up large amounts of resource space to store the data, and the need of multiple image algorithms not only increases the system complexity but also prolongs the processing time significantly [1].

关键词： image quality Manufacturing processes Filtering image processing conferences System-on-chip Calibration

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Lightweight YOLOv4 Algorithm for Remote Sensing image Detection 14

Lightweight YOLOv4 Algorithm for Remote Sensing Image Detect...

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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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