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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411-420 订阅

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HSO-Based K-Means Segmentation for Identification of Disease and Deficiency in Paddy Plant

HSO-Based K-Means Segmentation for Identification of Disease...

引用

International conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Sivagami, S. Mulakaluri, Sailaja Saranya, C. Ashtalakshmi, K. Kalpana, R. Manoshankari, M. Department of Computer Science and Applications St. Francis de Sales College Electronic City P.O Bengaluru560 100 India

ISBN: (纸本)9789819762217

In agriculture, image processing is frequently used to identify issues with fruit grading, weed identification, disease diagnosis, and other related tasks. In order to detect nutritional deficiencies and paddy plant disease, this study employs image processing. image has to classified as diseased or nutrient deficient after performing segmentation of image and selection of important features from the image. There are many different image segmentation algorithms available. K-means is an incredibly easy algorithm to understand, apply, and provide accurate results with. Despite its accuracy and simplicity, its drawbacks include the requirement to estimate the value of K and choose K’s initial centroids at random from the provided data values. A unique image segmentation technique was developed to solve this problem. It is based on the identification of inadequacies using K-means and harmonic search optimization (HSO). The K-means algorithm’s performance is significantly impacted by the randomly chosen values for K and the centroid values of the clusters. The standard K-means method takes longer to produce accurate results because it needs random initialization. A K-means method based on HSO is suggested to expedite the initialization procedure. The aim function of the suggested approaches is the Otsu technique with an initial step created from the HSO. The HSO method is used to locate the cluster canters, which are then used to begin the K-means algorithm. Lastly, when segmentation results are compared to those obtained using the fuzzy C-means and classic K-means segmentation algorithms, the recommended HSO-based K-means technique performs better than the rivals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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3rd International conference on Optimization, Learning algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd International conference on Optimization, Learning algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530241

The proceedings contain 26 papers. The special focus in this conference is on Optimization, Learning algorithms and Applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite image Recognition: A Comparative Analysis of Classification algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement Pattern Recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using image processing and Machine Learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

ISBN: (纸本)9783031530845

The proceedings contain 31 papers. The special focus in this conference is on Recent Trends in image processing and Pattern Recognition. The topics include: UI/UX for Aerospace Qualification Business Processes;Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis;a Comprehensive Analysis of Blockchain Network Security: Attacks and Their Countermeasures;Enhancing CT image Visualization and Analysis Through Rescaling Raw Pixel Values to Hounsfield Units;VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks;leveraging Handwriting Impairment as a Biomarker for Early Parkinson’s Disease Diagnosis;blockchain for Patient Data Integrity: Decentralised Storage and Retrieval in Modern Healthcare systems;a Residual Learning Approach Towards the Diagnosis of Colorectal Disease Effectively;Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and Their Performance Evaluation;cincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone;Realistic Skin image Data Generation Leveraging Conditional GAN and Classification Using Deep CNN;Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification;analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray;a Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning algorithms;federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions;optimal Resource Allocation for Energy Harvested Cognitive Radio Networks Based on Learn Heuristic Algorithm;Deep Neural Networks Based Security Solution for ATM Transactions;crypto Bank: Cryptocurrency Wallet Based on Blockchain;radiography and Thermography Based image processing Techniques for Assessment of Lower Back Pain;modified Snapshot Ensemble Algorithm for Skin Lesion Classification;COVID-19 Disease Prediction Using Generative Adversarial

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Virtual Reality image Generation and Enhancement Based on Real-Time Model Simplification 5th

Virtual Reality Image Generation and Enhancement Based on Re...

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5th International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Luo, Lilei Shandong Vocational College of Science and Technology Shandong Weifang261053 China

ISBN: (纸本)9789819751839

Virtual reality technology is a new practical technology that comes directly from the application and involves many disciplines. It is a comprehensive and integrated technology that combines advanced computer technology, sensing and measurement technology, simulation technology, and microelectronics technology. The virtual image is a dynamic image expression in the field of visual image, which is digitally generated by a computer and has its own unique image construction method. As the application of virtual reality technology deepens, the complexity and realism of virtual scenes are required more and more, far exceeding the processing capacity of computer graphics hardware. Therefore, there is an urgent need to resolve the contradiction between the complexity of the scene and the real-time nature of the interaction. In this paper, based on the review of related literature, virtual reality image generation methods and augmentation technology methods are reviewed. Several problems of simplification of multi-resolution models based on computer technology and the classification of algorithms are constructed, and the practical application of multi-resolution models in solving the problems of complexity and real-time scenes is discussed. It is demonstrated that the realism and practicality of the images are achieved by constructing smooth curve models and reducing the coupling of image errors so that the virtual reality images are generated with improved accuracy and clarity for better research use. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual environments

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A Survey on Machine Learning Techniques for Multimodal Biomedical Signal processing

A Survey on Machine Learning Techniques for Multimodal Biome...

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2023 IEEE International conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Prathiksha, M. Badiger, Priyanka R. Thangaraj, Varshini Varshini, D.A. Srikanth, Sahana Gurugopinath, Sanjeev Pes University Department of Electronics and Communication Engineering Bengaluru560085 India

ISBN: (纸本)9798350396195

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

关键词： Data fusion

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

ISBN: (纸本)9783031530814

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Low-Light image Enhancement Based on Retinex Reflectance Compensation 36

Low-Light Image Enhancement Based on Retinex Reflectance Com...

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36th International conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhaozheng Wang, Wujun Yang, Weiran Ma, Ningtao Sun, Mingyang Wang, Xinxin Ruan, Liangyu Yi, Ru College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)1891706594

Enhancement of low-light images is a low-level visual task aimed at improving the quality of images captured under low-light conditions. In this study, a low-light image enhancement algorithm is proposed by compensating for the reflection loss of illumination components obtained through object reflection. Specifically, the algorithm first utilizes Gaussian filtering to process the value component (V) of the image, separating the illumination component from the reflection component. Then, through a illumination compensation strategy, the illumination component is processed and combined with the reflectance component to synthesize the enhanced value component (V). Finally, an adaptive global balance strategy is applied to optimize the enhanced value component (V) to ensure that the resulting image appears more natural and conforms to human visual perception habits. Experimental results demonstrate the effectiveness and superiority of our method compared to existing traditional processing algorithms and deep learning methods, showing excellent performance in enhancing dark details of images and maintaining natural colors. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： image enhancement

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Approach to controlling laser processes with the help of AI 14

Approach to controlling laser processes with the help of AI

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2025 conference on High Power Laser Materials processing

作者： Wetzig, Andreas Szep, Jeno Ullmann, Linda Dittrich, Dirk Meyer, Julia Schlosser, Michael Rauscher, Peter Pudell, Stefan Schoeps, Patrick Vogelsang, Stefan Sonntag, Frank Fraunhofer IWS Winterbergstr 28 D-01277 Dresden Germany Fraunhofer CMA 5700 Rivertech CourtSuite 210 Riverdale MD 20737 USA

ISBN: (纸本)9781510684607;9781510684614

In the modern industrial context, laser processes, such as laser cutting and laser welding, are predominantly monitored and partially controlled in specific areas, such as process abort scenarios or axis actuator movements. Industrial driven interfaces like OPC UA or proprietary bus interface recently allow data acquisition from those control units within certain limits. This data can be augmented with highly accurate scientific data sources. In our proposed setup this is achieved by integrating laser acoustic sensors along with high speed cameras operating in visual and thermal spectrum. The variety of available data sources offers a significant potential for further processing and analysis via artificial intelligence (AI), contributing to deeper process understanding and further development of enhanced control algorithms of laser material machining processes. A post-mortem annotation with quality characteristics such as dross formation, surface roughness, welding depth, porosity, crater formation, etc. deliver all premises to develop and train AI based control models. To link all data sources and annotations a common time management and time normal is required. Its time resolution depends on the fastest cycle time governing a control answer, typically executed in the range of sub milliseconds. A time scale smaller than standard AI algorithms typically deliver complex inference results. Our paper presents an approach to close the time gap by introducing a smart control platform capable of capturing and preprocessing data in real time by utilizing hardware accelerated acquisition algorithms and time management (FPGA-MPSoC). The solution was implemented, transferred to a state of the art welding and cutting setup, and successfully tested. A foundation for an AI controlled laser machining process is set.

关键词： real time control image and audio processing hardware acceleration AI preprocessing laser welding laser cutting FPGA-MPSoC PTP

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Boosting Accuracy and Reducing Time in Laser Shooting Practices: Video processing Solutions for Impact Evaluation 1

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8th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2024

作者： Rivas-Lalaleo, David Bautista-Naranjo, Víctor Vayas-Ortega, Germania Iza-Chango, Erika Lema-Jumbo, Pamela Bran, Carlos Universidad de las Fuerzas Armadas—ESPE Sangolquí Ecuador Universidad Indoamerica Ambato Ecuador Instituto Investigación e Innovación en Electrónica Universidad Don Bosco Soyapango El Salvador

ISBN: (数字)9789819757992

ISBN: (纸本)9789819757985

This study explores the most effective method for impact measurement in laser shooting ranges, crucial for security training, accident prevention, and cost reduction. It utilizes video surveillance and image processing algorithms in Python to accurately determine laser impact locations. The research found that employing a single target per silhouette is most efficient, minimizing errors to ±1 impact and enabling heat map generation for feedback. The combination of video surveillance and image processing significantly enhances safety and reduces costs by providing real-time data. The findings advocate for the single target approach due to its precision and the comprehensive insights gained from heat maps, maintaining accuracy within a two-hit margin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Video signal processing

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Matrix Inversion Accelerated MCU Circuit for image Recognition Pertinent Computation

Matrix Inversion Accelerated MCU Circuit for Image Recogniti...

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2023 International Technical conference on Circuits/systems, Computers, and Communications, ITC-CSCC 2023

作者： Wang, Weisheng Jin, Yu Yuan, Quan Sun, Heming Beijing University of Chemical Technology Beijing China Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350326413

As the most complex computation in the fundamental matrix manipulation, the operation of matrix inversion limits the processing capability in the field of the image recognition and the pertinent application specified circuit. The intensive computation of matrix inversion is mitigated by efficient algorithms such as the Sherman-Morrison formula and accelerated floating-point multiplication and addition, while still suffering from division operations. Here, we present a high-performance floating-point divider circuit, which is capsuled as an instructor in to a general processor Cortex-M3 MCU to efficiently improve the matrix inversion speed in image recognition. The reconstructed MCU circuits are fully implemented on the Xilinx Spartan6 FPGA platform. Compared to the Xilinx floating point IP, the proposed architecture intensively reduces the division operation time by 82%. © 2023 IEEE.

关键词： image recognition

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