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检索条件"任意字段=Conference on Real-Time Image Processing 2006"

共 25649 条记录，以下是131-140 订阅

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Automated Brain Tumor Detection using image processing Techniques 5

Automated Brain Tumor Detection using Image Processing Techn...

引用

5th International conference on image processing and Capsule Networks, ICIPCN 2024

作者： Mane, Vijay Patil, Mansi Pawar, Akanksha Poruthur, Jescintha Vishwakarma Institute of Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350367171

Using MRI to reliably diagnose brain tumors is important but it is often time-consuming. The study uses an automated method for brain tumor detection and classification using image processing techniques and conventional machine learning algorithms in order to considerably accelerate the diagnostic procedure and automate the diagnosis of brain tumors. Through precise identification and classification of brain cancers in MRI images, the system provides a more efficient method of diagnosing brain tumors. The methodology consists of four main stages: data pre-processing, where images are prepared for analysis;application of various image filters such as bilateral, median, and Gaussian filters to enhance image quality and highlight tumor features;training of machine learning models including Logistic Regression and Support Vector Machines on pre-processed image data;and real-time prediction facilitated by a user-friendly graphical interface. © 2024 IEEE.

关键词： Median filters

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Leveraging ShuffleNet and LLaVA-Phi for State-of-the-Art image Deblurring and Description for Mobile Devices

Leveraging ShuffleNet and LLaVA-Phi for State-of-the-Art Ima...

引用

2024 IEEE International conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Ranjan, Arti Ravinder, M. Indira Gandhi Delhi Technical University for Women Department of Computer Science and Engineering New Delhi India Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350385199

The growing demand for real-time image processing on edge devices calls for novel approaches that balance computational efficiency with high performance. This paper introduces an integrated solution combining ShuffleNet, a lightweight convolutional neural network, with LLaVA-Phi model for efficient image deblurring and descriptive analysis on mobile devices. ShuffleNet's structural efficiency, characterized by channel shuffling and depth-wise convolutions, is exploited to deblur images swiftly, while LLaVA-Phi interprets the imagery to generate concise natural language descriptions. Our unified approach significantly enhances both the visual clarity of images and the accuracy of their associated descriptions with minimal computational overhead. Experimental results reveal substantial improvements over existing methods, confirming the efficacy of our approach for enhanced real-time image processing in computationally limited environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approach for a Machine-Human interface based on optical real-time Gesture Recognition for Automated Guided Vehicles

Deep Learning Approach for a Machine-Human interface based o...

引用

conference on real-time processing of image, Depth, and Video Information

作者： Krishnakumar, Kiran Raj Gersmeier, Laura Harders, Leif Ole Hussmann, Stephan West Coast Univ Appl Sci Fritz Thiedemann Ring 20 D-25746 Heide Germany REINHOLZ Technol GmbH Kirchhoffstr 1A D-25524 Itzehoe Germany

ISBN: (纸本)9781510673199;9781510673182

The intersection of deep learning and programmable logic controllers (PLCs) can lead to innovative applications in automation. One of the exciting application areas are gesture-based control systems for Automated Guided Vehicles (AGVs). AGVs are used in various industries for material handling, logistics, warehouse automation, etc. Traditionally, these vehicles are controlled using predefined routes or remote controls, but with gesture-based control, operators can communicate more naturally and efficiently. The incorporation of YOLO-Pose in YOLO versions 7 and 8 has elevated the YOLO algorithm to a leading tool for creating gesture recognition models. The YOLO algorithm employs convolutional neural networks (CNN) to detect objects in real-time. These latest YOLO models offer significantly improved accuracy, speed, and reduced training times. This paper presents the comparative results of 2D gesture recognition transfer learning models created using the YOLO v5, v7, and v8 models, along with the steps taken to implement the model in a PLC-controlled AGV. Over 14,000 images were collected to build the models. A semi-automated approach was used to annotate them. Five models were created: two Keypoint models and three object detection models using transfer learning techniques with the same hyperparameters.

关键词： real-time image processing machine-human interface gesture recognition convolutional neural networks (CNN) automated guided vehicles machine learning deep-learning networks

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image processing based real-time Online Attendance Monitoring System using Facial Recognition 3

Image Processing based Real-Time Online Attendance Monitorin...

引用

3rd International conference on image processing and Robotics, ICIPRoB 2024

作者： Fernando, Ravishka Athauda, Hashini Cinec Campus Department of Electrical and Electronics Engineering Malabe Sri Lanka

ISBN: (纸本)9798350374766

With the advancement of technology and due to the recent pandemic situation, the education sector has turned to the online teaching method. But the main problem here is the inconvenience and irregularities in the student's attendance. To ensure traditional attendance and reduce time wastage, this research aims to explore and implement an automated attendance marking system using facial recognition technology. This will enable students to be marked present or absent in real-time and will also help teachers to identify students who are present or absent for the online session. As a novelty of this research gives a more efficient and accurate method for attendance marking and eliminating manual and false attendance marking in online sessions. The proposed system employs the latest advancements in image processing and machine learning techniques such as the Haar Cascade feature and LBPH algorithm to accurately detect and recognize the face of a student. The performance of the system is evaluated on its own dataset which was the images of students captured through a video stream from a web camera and the results demonstrate through a confusion matrix its effectiveness in accurately recognizing faces and marking attendance in real-time. The results showed that the attendance system achieved 99.22% accuracy and can accurately mark the attendance of students in an Excel sheet. This real-time GUI-based system is unique in that it revolutionizes the traditional attendance marking process by automating it and providing real-time attendance data. © 2024 IEEE.

关键词： Computer vision

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A SINGLE GRAPH CONVOLUTION IS ALL YOU NEED: EFFICIENT GRAYSCALE image CLASSIFICATION 31

A SINGLE GRAPH CONVOLUTION IS ALL YOU NEED: EFFICIENT GRAYSC...

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2024 International conference on image processing

作者： Fein-Ashley, Jacob Wickramasinghe, Sachini Zhang, Bingyi Kannan, Rajgopal Prasanna, Viktor Univ Southern Calif Los Angeles CA 90007 USA DEVCOM Army Res Off Adelphi MD USA

ISBN: (纸本)9798350349405;9798350349399

image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural networks (CNNs). These networks, while powerful, experience high latency due to the number of operations they perform, which can be problematic in real-time applications. Many image classification models are designed to work with both RGB and grayscale datasets, but classifiers that operate solely on grayscale images are less common. Grayscale image classification has critical applications in fields such as medical imaging and SAR ATR. In response, we present a novel grayscale image classification approach using a vectorized view of images. By leveraging the lightweight nature of Multi-Layer Perceptrons (MLPs), we treat images as vectors, simplifying the problem to grayscale image classification. Our approach incorporates a single graph convolutional layer in a batch-wise manner, enhancing accuracy and reducing performance variance. Additionally, we develop a customized accelerator on FPGA for our model, incorporating several optimizations to improve performance. Experimental results on benchmark grayscale image datasets demonstrate the effectiveness of our approach, achieving significantly lower latency (up to 16x less on MSTAR) and competitive or superior performance compared to state-of-the-art models for SAR ATR and medical image classification.

关键词： GCN grayscale MLP low-latency

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real-time Aerial image Edge Detection with MicroWatt Power ISA on FPGA 3

Real-Time Aerial Image Edge Detection with MicroWatt Power I...

引用

3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshi, Prashant V. Sudharshan, K.M. Asuti, Majunath G. Poornashree, S.J. Research, Sunitha K. School of Ece Reva University Bengaluru India Bnmit Department of Ece Bengaluru India

ISBN: (纸本)9798331515911

This research investigates the implementation of real-time aerial image edge detection using the Canny edge detection algorithm with the MicroWatt Power Instruction Set Architecture (ISA)-Open Core Processor on Field Programmable Gate Arrays (FPGAs). Canny edge detection is particularly advantageous for aerial image processing due to its ability to produce precise and well-defined edges, making it ideal for applications in surveillance, agriculture, and environmental monitoring where detail preservation is critical. The proposed system utilizes the MicroWatt Power ISA-Open core processor to optimize resource utilization, achieving high throughput and low latency while maintaining energy efficiency. The work uses the Xilinx Vivado EDA tool to calculate the power dissipation of the FPGA implementation, demonstrating significant reductions in power consumption compared to traditional processing methods. The experimental results showcase the effectiveness of the Canny edge detector in producing accurate edge maps while minimizing resource usage, thus enabling real-time processing capabilities. This work contributes to the advancement of low-power embedded systems for aerial imaging, highlighting the potential of FPGAs combined with the MicroWatt Power ISA for efficient and effective image analysis. © 2025 IEEE.

关键词： Aerial photography

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Research on the Implementation and Application of Improved Canny Algorithm for image Edge Detection Based on FPGA 24

Research on the Implementation and Application of Improved C...

引用

1st International conference on image processing Machine Learning and Pattern Recognition

作者： Huang, Haojie Luo, Junzhe Beijing Inst Technol Comp Coll Zhuhai Peoples R China

ISBN: (纸本)9798400707032

With the continuous development of digital image processing technology, edge detection technology is playing an increasingly important role in the field of image processing. The Canny algorithm is a classical gradient-based edge detection algorithm with excellent performance and robustness. However, due to its large computational amount and poor real-time performance, the traditional software implementation method has been unable to meet the demands of modern high-speed image processing. Therefore, the Canny algorithm hardware is turned into a popular research direction. As a programmable logic device, FPGA has the advantages of high flexibility, short development period and strong parallel computing power, which is widely used in the field of digital signal processing. At present, there has been a lot of research work on FPGA in implementing the Canny algorithm, but most of the schemes have some problems, such as slow speed and high resource occupancy rate. Therefore, this paper presents an improved scheme for the hardware design of Canny algorithm based on FPGA, aiming to improve the speed and efficiency of image processing while reducing the utilization of hardware resources.

关键词： FPGA Canny algorithm Hardware design Digital image processing technology

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A Method of Video Fire Smoke image Recognition Based on Computer Automatic Location

A Method of Video Fire Smoke Image Recognition Based on Comp...

引用

International conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Tian, Zhijia Wu, Xiaochuan Sha, Shuang Ministry of Emergency Management Shenyang Fire Research Institute Liaoning Shenyang110034 China

ISBN: (纸本)9783031782756

Fire smoke needs early detection and accurate identification, so as to protect people's lives and property, while manual control method has problems such as large time consumption, subjective misjudgment, so an efficient, accurate and real-time fire smoke image recognition method is needed. In this paper, a method of video fire smoke image recognition is proposed based on computer automatic location algorithm, computer vision and image processing technology, combined with optimized algorithms and high performance hardware. Through real-time processing and analysis of video frames, the method can quickly and accurately locate the fire smoke area, and realize real-time monitoring and alarm. After experimental testing, it is found that the accuracy of the proposed method is between 94% and 99%, maintaining a high accuracy. The method has high-speed processing capability and automatic characteristics, and can quickly analyze a large number of video image data and accurately locate the fire smoke area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Digital storage

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Immersive Virtual conference Room: Bridging the Gap Between Remote Collaboration and Physical Presence 31

Immersive Virtual Conference Room: Bridging the Gap Between ...

引用

31st International conference on Systems, Signals and image processing (IWSSIP)

作者： Dwivedi, Vivek Bhatnagar, Mansi Tursunov, Javlon Rozinaj, Gregor Slovak Univ Technol Bratislava Inst Multimedia Informet Commun Technol Bratislava Slovakia

ISBN: (纸本)9798350391893;9798350391886

In this paper, the development of an immersive virtual conference room designed to replicate the experience of a physical meeting environment has been presented. Utilizing Unity software, authors have created a virtual space equipped with several simple screens instead of holograms, each attached with individual cameras. Live camera feeds seamlessly integrate real-time participant interactions within virtual environments, enhancing telepresence and fostering immersive collaboration akin to face-to-face meetings. While the current implementation operates locally, our future focus is on enabling remote connectivity, facilitating collaboration among individuals across different geographic locations, and later a hologram-based virtual conference. This innovative approach aims to enhance remote collaboration experiences and bridge the gap between virtual interactions and physical presence.

关键词： Virtual conference room Unity software multi-screen setup Live streaming Immersive experience Virtual communication Distance interaction Geographical connectivity

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Color image denoising: a hybrid approach for mixed Gaussian and impulsive noise

Color image denoising: a hybrid approach for mixed Gaussian ...

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conference on real-time image processing and Deep Learning

作者： Smolka, Bogdan Kusnik, Damian Smolka, Milena Kawulok, Michal Cyganek, Boguslaw Silesian Tech Univ Gliwice Poland AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9781510673878;9781510673861

This paper tackles the problem of mixed Gaussian and impulsive noise suppression in color images. The proposed method comprises two essential steps. Firstly, we detect impulsive noise through an approach based on the concept of digital path exploring the local pixel neighborhood. Each pixel is assigned a cost of a path connecting the boundary of a local processing window with its center. When the central pixel exhibits a high value of the path with lowest cost, it is identified as an impulse. To achieve this, we use a thresholding procedure for detecting corrupted pixels. Analyzing the distribution of minimum path costs, we employ the k-means technique to classify pixels into three distinct categories: those nearly undistorted, those corrupted by Gaussian noise, and those affected by impulsive noise. Subsequently, we employ the Laplace interpolation technique to restore the impulsive pixels - a fast and effective method yielding satisfactory denoising results. In the second step, we address the residual Gaussian noise using the Non-Local Means method, which selectively considers pixels from the local window that have not been flagged as impulsive. The experimental results confirm that our proposed hybrid method consistently yields superior outcomes compared to state-of-the-art denoising techniques. Moreover, its computational complexity remains low, rendering it suitable for real-time applications.

关键词： image enhancement color imaging mixed noise reduction impulsive noise

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