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检索条件"任意字段=Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"

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11th International conference on Intelligent Computing and Applications, ICRTC 2023

11th International Conference on Intelligent Computing and A...

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11th International conference on Intelligent Computing and Applications, ICRTC 2023

ISBN: (纸本)9789819717231

the proceedings contain 70 papers. the special focus in this conference is on Intelligent Computing and Applications. the topics include: RNN Learning for Dynamic Selection of Channel Access Scheme in FANETs;a Crime Knowledge Discovery Scheme Based on Entity Recognition, Relation Extraction, and Development of Criminal Profiling Using Modus Operandi;optimized Biometric Key Management System for Enhanced Security;human Posture Identification and Recognition Using Deep Learning Techniques;twitter Sentiment Analysis Using Different Machine Learning Techniques;exploring Empirical Mode Decomposition for Music Genre Classification Using Deep Learning;a Comparative Analysis of Fog Computing’s Problems, Challenges and Future Directions;explainable Artificial Intelligence Insight: An Orderly Survey;deep Learning-Based Sign Language Translator;Analysis of Arrhythmia from Electrocardiogram (ECG) Data Using ML Framework;brain Tumor Detection Using Quantum Neural Network;augmented Reality-Based Application for indian Monuments;fog Obscurity Mitigation;a Load Balancing Using Multi-population Grasshopper Optimization Approach for Workflow Tasks in Clouds;parental Control for Techie Child Using Keylogger;modified Box Filter Design and Noise Analysis on Two-Dimensional images;Change Detection in Remote Sensing SAR image Using a Ratio-Based Operator;automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models;mango Leaf images Quality Improvement Techniques Using Subjective Approach of image Enhancement;real-Time 3D Texture and Motion Analysis for Face Anti-spoofing Using Deep Learning and computer vision;Hybrid Sentiment Polarity Prediction Scheme in Social Networks using Attention Mechanism and Improved CNN;survival Analysis of Heart Failure Patients with Advanced Machine Learning Models;a Differential Privacy Perturbation with Random Forest Classifier in Medical Database;smart Application for Early Detection of Rice Plant Disease Using Incept

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Investigating the Role of image processing to Identify Pattern for Industrial Automation 11

Investigating the Role of Image Processing to Identify Patte...

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11th International conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaur, Gagandeep Balu, Vincent Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

In situations when both the output and the result are graphical, classification is used. the science's name was decided upon because of its concentration on image analysis. Imaging, satellite data, contrasts amplification, and voice signal production all could be done on the architecture here. A image or video cannot be recognized until a certain component or feature is found. In order to spot patterns, Cad system is used in this study. An area of study was the function of image processing in quality assurance. Even at high speeds., excellent ray tracing enables faultless record. Corporate machine learning is often used in process automation. computer tech vision equipment includes things like camera-activating displays and movable video recorded tracks. © 2022 IEEE.

关键词： image recognition

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Performance of Single Board computers for vision processing 11

Performance of Single Board Computers for Vision Processing

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IEEE 11th Annual Computing and Communication Workshop and conference (CCWC)

作者： Manore, Curtis Manjunath, Pratheek Larkin, Dominic US Mil Acad West Point NY 10996 USA

ISBN: (纸本)9781665414906

With the increasing complexity of machine vision algorithms and growing applications of image processing, how do computers without a dedicated graphics processor perform? this research discusses the computational abilities of two low-cost single board computers (SBCs) by subjecting them to various Visual Inertial Odometry (VIO) algorithms. the end goal of this research is to identify a SBC which meets the requirements of being employed on an Unmanned Aerial System for autonomous navigation.

关键词： Single Board computer Odroid computer vision VIO

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11th IAPR TC3 workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2024

11th IAPR TC3 workshop on Artificial Neural Networks in Patt...

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11th IAPR TC3 workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2024

ISBN: (纸本)9783031716010

the proceedings contain 27 papers. the special focus in this conference is on Artificial Neural Networks in Pattern Recognition. the topics include: Neural Decompiling of Tracr Transformers;pitfalls in processing Infinite-Length Sequences with Popular Approaches for Sequential Data;robust Clustering with McDonald’s Beta-Liouville Mixture Models for Proportional Data;evaluating Support Vector Machines with Multiple Kernels by Random Search;Automatic Interpretation of 18F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks;a Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical image Segmentation;An Improved Pix2Pix GAN for Medical image Generation;vision Transformer Features-Based Leukemia Classification;comparative Study of Deep Learning Models in Melanoma Detection;a Metaheuristic Optimization Based Deep Feature Selection for Oral Cancer Classification;machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson’s Disease;explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor imagery Task;Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks;VAeViT: Fusing Multi-views for Complete 3D Object Recognition;leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos;palmprint Classification via Filter Faces and Feature Extraction;deep Multi-label Classification of Personality with Handwriting Analysis;license Plate Detection and Character Recognition Using Deep Learning and Font Evaluation;experiments in Modeling Disagreement;deep Multiresolution Wavelet Transform for Speech Emotion Assessment of High-Risk Suicide Callers;Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming;Leveraging LSTM Embeddings for River Water Temperature Modeling;research on the Identification of Common Economic Shellfish in Jiang

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CDA-Net: computer vision based automated car damage analysis 14

CDA-Net: Computer vision based automated car damage analysis

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Kannan, Iyshwarya Ratthi Balasubramanian, Yogameena Saravana Permaal, S. Menaka, K. Ramesh, Sivasankari Thiagarajar College of Engineering Tamil Nadu Madurai India National Institute of Technical Teachers' Training and Research TamilNadu Chennai India Velammal Institute of Technology TamilNadu Chennai India

ISBN: (纸本)9798400716256

Car insurance claims are rising in tandem with the rising tide of car users. Every insurance claim requires an engineer's manual assessment and a surveyor's actual examination. this procedure can last anywhere from a few days to several weeks. Current deep-learning techniques have paved the way for this type of mechanization. Both the business and the client would benefit from a comprehensive system. To assess the damage cost for the insurance claim process, the make and model of the car, damaged parts, damage type, and severity of the damage are important parameters. We introduced two datasets, a piqued car make and model (CMM) dataset containing images of the most popular 23 car makes and 148 vehicle models available in the indian automotive market. the second dataset consists of 11,380 images collected from insurance offices and web resources of different types of car damage, including annotations. In addition, it provides an assessment of the damage to each part, the severity of the damage (dents, scratches, bent, broken, cracks, smashed, punched, and pushed), and the location of the damage (front, back, side). the assessment helps estimate the damage cost when combined with structured data. the proposed CDA_YOLOv5 car damage assessment framework outperformed existing state-of-the-art one-stage models with an average per-class accuracy of 87.36% and an overall accuracy of 90.45%. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Damage detection

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Identification of Unlawful Construction Project using Aerial View in Urban Area 11

Identification of Unlawful Construction Project using Aerial...

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11th International conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kumar, Arvind Mahajan, Rohini Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India Chandigarh Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9781665487320

It is quite costly to follow the land operations in metropolitan areas with accurate geo mapping and monitoring. It takes more time and labor. Auditing cannot be done on a regular basis. Someti mes it is impossible to complete a task within a period of six months or a year. therefore, automating these operations in real time from an aerial perspective is more practical. graphics rendering and deep learning make it simple to find unauthorized building structures using income maps and old photographs of the region. the project's goal is to improve accuracy by combining deep learning and image processing methods. As a result, this model ought to be able to recognize various forms and orientations of buildings. Model ought to be able to identify any unlawful activity occurring on public property, from open, bare ground to build structures, and alert the appropriate authorities. Recent advancements in satellite photography have made it feasible to acquire high quality pictures in several bands, including RGB and near infrared. Using deep learning and all these high-resolution photos, image processing methods. Our objective is to identify unauthorized building development in metropolitan areas using all available tools and to email a report to the local tax agency. © 2022 IEEE.

关键词： Antennas

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QAAD: Quality Aware Adaptive Denoising

QAAD: Quality Aware Adaptive Denoising

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International conference on Signal processing and Integrated Networks (SPIN)

作者： Tejas Ajay Parse Tanishq Awasthi Dushyant Yadav Piyush Joshi Department of Computer Science Indian Institute of Information Technology Sri City Chittoor India

image denoising is a fundamental task in computer vision and image processing, crucial for improving the visual quality and interpretability of images captured in noisy environments. In this research, we propose a quality-aware adaptive denoising (QAAD) approach that leverages the Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) as a key factor in determining the optimal denoising algorithm for an image. Our methodology involves an initial assessment of image quality using BRISQUE scores for a diverse set of images. Subsequently, we employ various image quality enhancement algorithms to enhance the image quality. through extensive experimentation, we assess the performance of these enhancement algorithms with respect to specific ranges of BRISQUE scores. this evaluation enables us to determine which denoising algorithm performs optimally for images falling within particular BRISQUE score ranges. the innovation in our approach lies in its adaptability. When an image proceeds for denoising, its BRISQUE score is calculated and, based on this score, the most suitable denoising algorithm for that specific image is selected. this adaptive strategy ensures that the chosen denoising algorithm is tailored to the inherent noise characteristics of the input image, leading to superior denoising results. Our experimental results demonstrate the effectiveness of our approach in enhancing image quality and achieving state-of-the-art denoising results. this research contributes to the field of image processing by providing a data-driven and adaptive solution for image denoising, ultimately enhancing the quality of images in diverse real-world applications.

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Analysis of Retinal Detachment Severity using RDidean Evaluative System and Classifier Assessment Implementation Models 11

Analysis of Retinal Detachment Severity using RDidean Evalua...

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11th International conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Poongothai, L. Sharmila, K. Department of Computer Science Chennai India Dr. Mgr Janaki College for Women Department of Computer Science India VISTAS

ISBN: (纸本)9781665487320

vision loss can be a permanent disability of a human, that could be attributed to due to Retinal Detachment (RD). this is perilous disorder that could be caused due to dealignment of the layers in a retina. the choroid supplies oxygen and nutrients to the outer segments of the photoreceptors. the retina's photoreceptors will stop working if the choroid separates from the retina. the degree of dependence on the choroid is high, due to its supply of oxygen to the fovea that increases the breathability of the retinal blood vessels. this tapering of the oxygen levels can paramount to macula detachment that can be an irreversible damage to the cones and rods at the posterior pole, thereby leading to blindness. If the retina is quickly reattached, good vision can be preserved even if the macula is not removed. Retinal detachment hitherto has been studied through conceptual data processing and image processing techniques. However, this paper analyzes the retinal detachment for an individual through the various features, and the same is encompassed in an equational form to be termed as the 'RDidean' evaluative system. the evaluative model thus explicitly categorizes the retinal database into normal and abnormal images based on the value obtained from the system. the performance of this system is then effectuated through diverse tree classifier models and the deep learning AlexNet classifier in MATLAB to comprehend the precision of classification. While effectively entailing another pool of algorithmic models like the SVM variations and the Naïve Bayesian methods to cognize the accuracy of retinal detachment severity that the evaluative system rendered. this indagation analyzes focusses to establish a corroborative and impeccable prediction system for the classification of normal and abnormal eye through color fundus images. thereby aiding to improve the ergonomic environment of clinicians to improve the treatment plan, along with delivering complementary clinical decisions,

关键词： Ophthalmology

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AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production 16

AI-assisted Automated Pipeline for Length Estimation, Visual...

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16th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP) / 16th International conference on computer vision theory and Applications (VISAPP)

作者： Hashisho, Yousif Dolereit, Tim Segelken-Voigt, Alexandra Bochert, Ralf Vahl, Matthias Fraunhofer Inst Comp Graph Res IGD Joachim Jungius Str 11 D-18059 Rostock Germany State Res Ctr Agr & Fisheries Mecklenburg Vorpomm Inst Fisheries Sudstr 8 D-18375 Born Germany

ISBN: (纸本)9789897584886

Shrimp farming is a century-old practice in aquaculture production. In the past years, some improvements of the traditional farming methods have been made, however, it still involves mostly intensive manual work, which makes traditional farming a neither time nor cost efficient production process. therefore, a continuous monitoring approach is required for increasing the efficiency of shrimp farming. this paper proposes a pipeline for automated shrimp monitoring using deep learning and image processing methods. the automated monitoring includes length estimation, assessment of the shrimp's digestive tract and counting. Furthermore, a mobile system is designed for monitoring shrimp in various breeding tanks. this study shows promising results and unfolds the potential of artificial intelligence in automating shrimp monitoring.

关键词： computer vision image processing AI Deep Learning Shrimp Aquaculture

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Pest Detection on Leaf using image processing

Pest Detection on Leaf using Image Processing

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11th International conference of computer Communication and Informatics (ICCCI)

作者： Nagar, Harshita Sharma, R. S. Rajasthan Tech Univ Dept Comp Sci Engn Kota India

ISBN: (纸本)9781728158754

A survey report showed that 70% indian population depends on agriculture sector. Numerous heterogeneous diseases and various kind of pests affect the production of crops which leads to quality and quantitative loss. Automatic in-field pest detection using computer vision technique is an important topic in modern intelligent agriculture but suffers from serious challenges including complexity of wild environment, detection of tiny size pest and classification into multiple classes of pests. In the literature attention is mostly focused on machine learning based techniques and image processing has not received equal attention. this paper illustrates an automatic approach for pest detection using Wavelet transformation and Oriented FAST and rotated BRIEF (ORB). the core objective of the research is to enhance feature extraction phase to improve the detection efficiency. the proposed approach is implemented on images of fluffy caterpillar pests on mustard crop and fava bean collected from farms in Rajasthan. the experimental results affirm the efficiency of the proposed approach.

关键词： Pest detection image processing computer vision Keypoints

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