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

共 14631 条记录，以下是211-220 订阅

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ISAApp – image Based Smart Attendance Application 1st

ISAApp – Image Based Smart Attendance Application

引用

1st International conference on Advances in Artificial Intelligence and Machine Learning in Big Data processing, AAIMB 2023

作者： Dutta, Aritra Suseela, G. Niranjana, G. Boral, Pushpita Gupta, Pranav Pal, Subha Bal Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9783031730641

This research paper introduces a Python-based implementation of a facial recognition system utilizing face recognition and Open CV libraries. The system has diverse applications, including security, surveillance, social media, and entertainment. By employing a pre-trained neural network, the system enables users to select an image file and accurately detect and recognize faces within it. Additionally, it includes functionality to encode faces from a specified folder, enabling comparison against the faces in the target image. By employing a blend of face detection, feature extraction, and machine learning algorithms, the system effectively identifies, and labels faces in images. Its effectiveness illustrates the potential of facial recognition technology for numerous applications, while also underscoring the ethical and privacy concerns associated with such technology. Addressing these issues and developing more precise and dependable systems would require further research. The primary objective of the system is to offer an improved solution for classroom attendance, ensuring the avoidance of false attendance records and significantly reducing the time required for attendance taking, through the application of machine learning solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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image Extraction for Linear Segment in 3D Space 3rd

Image Extraction for Linear Segment in 3D Space

引用

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： 3D Space CAD/CAM Geometrical slopes Graphics systems Intersection points Object cropping

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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...

引用

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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Real Time Violence Detection System Using YOLOv7 and Deep Learning Techniques

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

引用

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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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...

引用

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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Evaluation on the Effectiveness of SVM-Based image processing algorithms for the Identification and Classification of Lung Cancer 15th

Evaluation on the Effectiveness of SVM-Based Image Processin...

引用

15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bhaskar, Nuthanakanti Swaroopa Rani, B. Srinivasula Reddy, A. Asha Shiny, X.S. Gayathri, B. Venkateswara Reddy, R. CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031810794

Globally, lung cancer is a prevalent reason for death for individuals. The most common form of lung cancer has a high death rate. If lung cancer is identify and care for in its early on stages, screening can assist identify the disease at an earlier stage. Therefore, a patient's chances of survival are increased by early identification of lung cancer. Lung computed tomography (CT) scans might represent a more reliable method of identifying cancer than MRIs and X-rays. DICOM (Digital Imaging and Communications in Medicine) is used to scan the lungs and obtain images. We improve and smooth photos using a variety of preprocessing approaches. Edge detection and thresholding (ROI) are used in this work to section the lung tumor's region of interest. Lastly, we use Support Vector Machine to compute a number of geometrical aspects of the retrieved ROI and categorize the results into both benign and malignant degrees of severity (SVM). Data is classified using supervised machine learning techniques called SVM classifiers. One of their advantages is their ability to handle small amounts of high dimensional data. Every characteristic is used as an input to evaluate the SVM's efficiency. As such, lung cancer in CT scans is diagnosed using a method that uses methods for processing images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CT image Lung cancer detection MRI SVM classifier

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

Intrusion Detection from Surveillance Video Using Frame Base...

引用

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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Large Language Models Based Framework for Finding Cloth Quality 15th

Large Language Models Based Framework for Finding Cloth Qual...

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15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Devi Priya, R. Tharunika, L. Shreya, S. Rajasekaran, T. Sivasankari, M. Shanmuga Priya, T. Sivaraj, R. KPR Institute of Engineering and Technology Coimbatore India Nandha Engineering College Erode India

ISBN: (纸本)9783031810824

The rapid growth of e-commerce and the increasing demand for online clothing shopping have elevated the importance of accurate clothing quality assessment. In response to this demand, we present a novel mobile application that leverages deep learning techniques for pixel-level analysis on clothing items. The proposed application empowers users to capture images of clothing and receive real-time quality assessments in large datasets. The inputs will be the collection of large set of images for training and traditional machine learning models cannot provide sufficient accuracy. This paper addresses the challenge of quality assessment through a comprehensive pipeline Large Language models (LLM) based deep learning algorithm called ClothQualityFinder (CQF) that involves data collection, preprocessing and the application of deep learning algorithms namely Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). This paper includes a detailed examination of pixel-level analysis, encompassing aspects such as color analysis, texture recognition, and contour detection. The resulting model demonstrates good accuracy, making it a valuable tool for consumers and retailers alike. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloth Quality Finder Convolutional Neural Networks image processing Large Language models Pixel Level Analysis

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Estimation of image noise using polynomial masks

Estimation of image noise using polynomial masks

引用

conference on image processing - algorithms and systems iii

作者： Akhriev, A Bonch-Osmolovsky, A RRC Kurchatov Inst Inst Informat Technol Moscow 123182 Russia

ISBN: (纸本)0819452017

Many computer vision and image processing algorithms rely on the knowledge of the image noise variance as their input. parameter. However, in practice, the distinction between noise and image features is not easy to draw. In this paper, image noise variance is estimated by a novel method employing rigorously derived polynomial masks. The method is based on the assumption that the image can be locally represented as a polynomial of the given degree and constitutes a generalization of some of previously proposed approaches.

关键词： polynomial mask image noise variance

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Dedicated advanced software facility for processing data from earth observing satellites

Dedicated advanced software facility for processing data fro...

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SPIE conference on Earth Observing systems iii

作者： Lampropoulos, GA Boulter, JF AUG Signals Ltd Toronto ON M4V 1K7 Canada

ISBN: (纸本)0819428949

In this paper, a general philosophy about feature windows based applications and a windows-based application are presented for analysis, algorithm development, testing and validation studies for Signal, image and Data processing (SIDP) for Space-Based Surveillance (SBS) Applications. This dedicated facility is called AUG_SIDP. It performs several specialized tasks such as blur estimation, restoration, CFAR detection, clutter modeling, registration, pixel and data level fusion, target tracking and classification. It is still in the development and testing phase.

关键词： image processing Algorithm development Detection and tracking algorithms Sensors Expectation maximization algorithms image processing algorithms and systems Standards development image segmentation Optical filters Target detection

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