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

共 14633 条记录，以下是211-220 订阅

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Fossa Optimization Algorithm with Convolution Neural Network Based Brain Tumor Segmentation and Classification in MRI images

Fossa Optimization Algorithm with Convolution Neural Network...

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Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： Veeresh Shivaprakash Ramy Read Hossain Aruna T M Tatiraju.V.Rajani Kanth Sir M Visvesvaraya College of Engineering Raichur India Affiliated to Visvesvaraya Technological University Belagavi India Government Engineering College Raichur India Visvesvaraya Technological University Belagavi India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India TVR Consulting Services Private Limited Gajularamaram Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Magnetic Resonance image (MRI) brain tumor segmentation is critical and significant in a clinical field that assist in diagnosis, complete growth prediction and tumor mass rates required for patients. The complexity in segmenting brain tumors are challenging due to the various structures, shapes, locations, visual characteristics, frequency, texture and contrast. To overcome this issue, this research proposes a Fossa Optimization Algorithm (FOA) with Convolution Neural Network (CNN) for brain tumor classification. The Geodesic Active Contour (GAC) is used for segmentation which identifies tumor boundaries precisely in noisy and intricate data. FOA utilizes dynamic search strategy by its hunting behavior, balancing exploration and exploitation effectively. Its adaptive step size enables better convergence when avoiding local optima. Furthermore, its ability to capture edges and patterns makes it effective for classification. CNN are better to diverse in scale, noise and rotation because of its shared weights and local connectivity reduce the complexity thereby leading to higher accuracy and quick processing in classification. The FOA-CNN obtains 99.92% accuracy, 99.20% f1-score, 99.07% sensitivity and 99.34% precision for BRATS2020 dataset which is superior than Spatial Transformer Network and Non-local Attention Mechanism (STN-NAM).

关键词： image segmentation Accuracy Sensitivity Convolution Brain tumors Search problems Classification algorithms Complexity theory Optimization Biological neural networks

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1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

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1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756078

The proceedings contain 53 papers. The special focus in this conference is on Intelligent systems in Computing and Communications. The topics include: Towards Hands-Free Computing: AI Virtual Mouse Interface Powered by Gestures;smartAgro: Precision Yield Prediction, Crop Insights, and Real-Time Dashboard;Artificial Intelligence with MRI-Guided Radiation Therapy for Cancer Treatment;Software Requirements to UML Class Diagrams Using Machine Learning and Rule-Based Approach;CFALEA_LSTM: Adaptive Lotus Effect Algorithm Enabled Long Short-Term Memory for Rainfall Prediction Using Time Series Data;a Deep Learning Survey on Diseases Prediction and Detection in Health Care;fault Diagnosis in Belts Using Signal processing Techniques and Machine Learning;machine Learning Techniques Based Chronic Kidney Disease Detection with Performance Analysis of Fuzzy Rough Set and Correlation Attribute Selection;segmentation and Classification of Unharvested Arecanut Bunches Using Deep Learning;enhanced Satellite image Fusion Using Deep Learning and Feature Extraction Techniques: A Survey;intelligent Aircraft Antiskid Braking systems – A Review;artificial Neural Networks Applied in the Detection of Breast Cancer;supervised and Unsupervised Learning Techniques for Malware Classification Based on Opcode Frequency Features;predictive Analytics for Diagnosing Alzheimer’s Disease Using Artificial Intelligence and Machine Learning algorithms;A Comprehensive Study on Artificial Intelligence (AI) Driven Internet of Healthcare Things (IOHT);predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques;predicting Salinity Resistance of Rice at the Seedling Stage: An Evaluation of Transfer Learning Methods;Multi-camera HD Pedestrian Dataset for Person Detection and Re-identification;Fire Detection System Using Deep CNN.

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1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

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1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756047

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Stereo-Correct Digital Magnification of the Physical World Using Video-Pass-Through Augmented Reality

Stereo-Correct Digital Magnification of the Physical World U...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Jerald Thomas Mohammed Safayet Arefin Doug Bowman University of Wisconsin - Milwaukee Colorado State University Virginia Tech

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Video-pass-through (VPT) augmented reality (AR) enables greater field of view and fidelity compared to current optical-see-through AR technologies. However, the computational complexity of VPT AR has traditionally resulted in high latency, low resolution, and/or visual artifacts. Only recently have devices come on the market that successfully mitigate these drawbacks, such as the Varjo XR-3 and XR-4, the Apple Vision Pro, and the Meta Quest 3. We propose that low-latency VPT AR introduces several interesting 3D User Interface (3DUI) capabilities. In this position paper, we discuss one of these 3DUIs, namely stereo-correct digital magnification of the physical environment. Specifically, this paper presents a novel system for stereo-correct digital magnification of the physical environment using the Varjo XR-3 (but that can be generalized to other VPT AR systems). The digital magnification algorithm we propose enables stereoscopic magnification of physical objects in real time by utilizing the front-facing camera feeds of the VPT AR system, implemented entirely through software. In addition, we outline the challenges and limitations associated with the proposed system, discuss our current progress, and suggest potential future directions for the development of our proposed VPT AR digital magnification system.

关键词： Performance evaluation Visualization Three-dimensional displays Stereo image processing Software algorithms User interfaces Software Real-time systems Usability Augmented reality

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Real Time Violence Detection System Using YOLOv7 and Deep Learning Techniques

Real Time Violence Detection System Using YOLOv7 and Deep Le...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Sudha Senthilkumar Gracy Agarwal Arshia Shirish Shruti Kolte School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent times, violence in public spaces such as schools, hospitals, and other community environments has increased a lot. Due to this alarming rise in violence CCTV cameras have been deployed everywhere to enhance safety and security. However, these systems depend on manual monitoring which is prone to human errors and cause delays in the identification of violent events and action which can lead to significant damage. To overcome these limitations, this study has proposed a violence detection and alarm system based on deep learning algorithms. The hybridization of YOLOv7 allows rapid recognition of possible threats by identifying human presence and movement, and the lightweight classifier provides a high accurate classification of the identified tasks while reducing the processing computational resources of the system. With this dual-model-based framework, it is guaranteed that both the speed and accuracy can be achieved, and that the system can be implemented on edge devices such as mobile devices. The function of the system is to identify violence while it is happening and to issue alerts to security or law enforcement authorities committed to rapid response. The system was tested in real-time scenarios and achieved 91% accuracy in detecting violent actions. The system incorporates a Telegram bot to send real-time alerts to the relevant authorities. By leveraging advanced deep learning techniques, this study seeks to close the gap between existing surveillance methods and the need for a more automated and accurate violence detection system.

关键词： Deep learning Performance evaluation Visualization Accuracy Surveillance image edge detection Streaming media Real-time systems Safety Usability

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Comprehensive Review of Cybersecurity Vulnerabilities and Safety Concerns in Autonomous Vehicles

Comprehensive Review of Cybersecurity Vulnerabilities and Sa...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Uppalapati Sudheer Kumar Sukanta Halder Basant Kumar Sethi Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat India Department of Electrical Engineering Indian Institute of Technology(ISM) dhanbad Dhanbad India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weight sensors, speed sensors, tire pressure sensors, 3D imaging cameras, satellite-based positioning systems, and advanced radars like light detection and ranging (LiDAR) and 3D radar. AVs utilize these components, along with highly complex control algorithms as their central processing units, to perform autonomous driving, making them vulnerable to cybersecurity attacks. These security attacks raise significant safety concerns for AVs. This article provides a comprehensive review of various cybersecurity threats associated with AVs and presents potential solutions. Additionally, it examines the vulnerabilities of the sophisticated equipment used in AVs. This article aims to enhance the understanding of the safety and security concerns associated with AVs and offers insights into the importance of cybersecurity in this evolving technological field. Furthermore, this article addresses the current state of cybersecurity measures in AVs, evaluating the effectiveness of existing protocols and identifying areas that require improvement. It also explores emerging technologies and strategies that can enhance the security of AVs, such as blockchain for secure data transactions, machine learning for threat detection, and robust encryption methods.

关键词： image sensors Radar imaging Threat assessment Sensor systems Safety Sensors Encryption Security Computer security Autonomous vehicles

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Integrated Recognition Approach and Fining System Towards Detection and Tracking of Traffic Violating Multiple Vehicles using Pairing Net and Light Weight Deep Sort Fast YOLO Rec Architectures

Integrated Recognition Approach and Fining System Towards De...

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International conference on ent Computing and Control systems (ICICCS)

作者： R. Mugesh R. Manoj R. Kaviprasth S. Gokilavani Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Innovations in intelligent transport management systems have enhanced vehicle anomaly detection through traffic monitoring sensors and computer vision. Current approaches, however, lack effectiveness in multi-vehicle detection and tracking amidst changing traffic, especially for overspeeding, overloading, helmet violations, frequent lane changes, and imitation or unrecognized license plates. This research study proposes an end-to-end recognition method and self-driving fining system based on PairingNet + Lightweight Deep SORT Fast YOLO-Rec Architecture in order to provide improved detection efficiency and speed. Real-time sensor data from benchmark datasets or live video streams is processed into the form of frames. The multi-scale features are extracted by Fast YOLO-Rec utilizing a CNN backbone with Cross-Stage Partial (CSP) Connection Blocks, Residual Blocks, and Dark Blocks. Path Aggregation Network (PANet) maintains precise detection for objects of multiple sizes. For tracking, an anchor box prediction technique produces bounding boxes, and PairingNet, which is a graph convolution network-based method, pairs vehicles between frames based on contour and texture similarity. Deep SORT algorithm classifies offenses like counterfeit license plates, helmet misuse, and overloaded vehicles. Alerts are triggered to authorities and vehicle owners when infractions are detected. Experimental results show that the suggested model performs better than traditional methods in detection accuracy and processing efficiency and is a stable solution for traffic law enforcement and road safety.

关键词： Head image recognition Convolution Computational modeling Neural networks Computer architecture Feature extraction Safety Classification algorithms License plate recognition

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Intrusion Detection from Surveillance Video Using Frame Based image processing Techniques 1st

Intrusion Detection from Surveillance Video Using Frame Base...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Mohamed Yaseen, M.S. Radeesh, P. Chitra, P. Ramesh, Kishore SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India

ISBN: (纸本)9783031823824

In India, the number of surveillance cameras has increased rapidly, making it challenging for law enforcement to go through the vast amount of recorded footage. We suggest a technique for quickly reviewing large amounts of video feed in order to address this issue. By utilizing sophisticated algorithms such as facial recognition, lightweight YuNet model face detection, and motion detection through background subtraction, our system accelerates the analysis of videos from various camera systems. Our model is a great tool for any agency looking to improve its operational efficiency when handling large amounts of monitoring data because it can quickly identify important events and produce concise summaries with timestamps. This piece of research contributes towards development within this field since such new methods are combined into one single solution thereby advancing surveillance technology while at the same time providing concrete benefits for everyday challenges faced by law enforcement agencies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intrusion detection

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image Extraction for Linear Segment in 3D Space 3rd

Image Extraction for Linear Segment in 3D Space

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3rd International conference on Signal and Data processing, ICSDP 2023

作者： Mamatha, Elliriki Amardeepak, M. Sharma, Rohit Krishna Anand, S. Department of Mathematics GITAM University Bengaluru India Department of Mechanical Engineering CIT-NC Bengaluru India Department of Engineering Garden City University Bangaluru India Department of AI & DS Shridevi Institute of Engineering and Technology Tumkur India

ISBN: (纸本)9789819795772

In computer graphics systems, cropping applications appear to be quite simple from a human perspective as human eyes can easily visualize whether a portion of the picture is inside or outside. However, from a computational perspective, the problem increases multifold as a simple process involves a large number of comparisons and computations. With the added importance given to floating point numbers, the complexity shoots up exponentially. With this viewpoint in perspective, an analytical model has been designed. This model incorporates algorithms using integral calculations in order to find intermediate intersection points. This in turn would improve cropping objects’ speed in three-dimensional spaces. The performance of this algorithm is expected to be robust and can handle computations at a much higher speed for any size of rectangular parallelepiped cropping windows. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Three dimensional computer graphics

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Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social Media Content Classification and Community Detection

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： A. Elumalai S. Rukmani Devi D. Rosy Salomi Victoria A. Jagadeeswaran Aparajita Mukherjee K. Vinoth Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Information Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Institute of Engineering and Management(IEM) University of Engineering and Management Kolkata West Bengal Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Social Media Content Classification and Community Detection (SMCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. Data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis.

关键词： Adaptation models Accuracy Social networking (online) Stereo image processing Scalability Heuristic algorithms Blogs Logic gates Classification algorithms Convolutional neural networks

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