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

共 13627 条记录，以下是381-390 订阅

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Harnessing AI and Imaging Innovations for Enhanced Detection and Diagnosis of Age-Related Macular Degeneration: An In-Depth Review 4

Harnessing AI and Imaging Innovations for Enhanced Detection...

引用

4th International conference on Artificial Intelligence and Signal processing

作者： Swaroop, Sana Rao, Manda Rama Narasinga GITAM Univ Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Age-related macular degeneration (AMD) is a leading cause of irreversible vision impairment among the elderly population worldwide, affecting over 196 million people globally. This study delves into innovative approaches for improving the detection and management of AMD through advanced technological solutions. To investigate the impact of ultraviolet (UV) and blue light exposure on AMD, the research analyzes their contribution to oxidative stress and retinal damage and evaluates potential protective measures or interventions to mitigate these effects. By leveraging state-of-the-art machine learning algorithms and advanced image processing techniques, the research aims to enhance the precision and efficiency of AMD diagnosis. The statistical burden of AMD underscores its significant impact on global health, with projections indicating a rising prevalence due to aging populations, lifestyle factors, and increasing digital screen use among younger generations. Effective management hinges on early detection and accurate monitoring of AMD biomarkers, which these methodologies seek to facilitate. Experimental evaluations demonstrate promising outcomes in diagnostic accuracy and scalability, highlighting the potential for widespread adoption in clinical practice. Furthermore, these advancements contribute to broader efforts in global eye health by offering scalable, AI-driven solutions that can improve patient outcomes and streamline healthcare workflows. By addressing the complexities of AMD diagnosis, this research supports healthcare providers in delivering timely interventions and personalized care, ultimately reducing the burden of AMD-related vision loss on individuals and healthcare systems.

关键词： AMD Vision Impairment image processing techniques AMD Biomarkers Diagnostic Accuracy AI-driven Solutions Healthcare Workflows Digitalization

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image QUANTIZATION TOWARDS DATA REDUCTION: ROBUSTNESS ANALYSIS FOR SLAM METHODS ON EMBEDDED PLATFORMS 29

IMAGE QUANTIZATION TOWARDS DATA REDUCTION: ROBUSTNESS ANALYS...

引用

IEEE International conference on image processing (ICIP)

作者： Picard, Quentin Chevobbe, Stephane Darouich, Mehdi Didier, Jean-Yves CEA LIST F-91191 Gif Sur Yvette France Univ Paris Saclay Univ Evry IBISC F-91025 Evry France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Embedded simultaneous localization and mapping (SLAM) aims at providing real-time performances with restrictive hardware resources of advanced perception functions. Localization methods based on visible cameras include image processing functions that require frame memory management. This work reduces the dynamic range of input frame and evaluates the accuracy and robustness of real-time SLAM algorithms with quantified frames. We show that the input data can be reduced up to 62% and 75% while maintaining a similar trajectory error lower than 0.15m compared to full precision input images.

关键词： SLAM localization image processing embedded systems real-time frame reduction

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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

引用

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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Comparative Study of Various Machine Learning algorithms for American Sign Language Recognition 1

引用

6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Malhan, Prabhat Gusain, Surbhi Raturi, Shashank Singh, Prabhdeep CS/IT Graphic Era University Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

This research paper presents a comparative study on various machine learning algorithms for sign language detection. The objective of this study is to find the sign language identification method that is most accurate and effective for usage in real-time applications. The Residual Network (ResNet), Artificial Neural Network (ANN), Convolutional Neural Networks (CNNs), VGG16, and MobileNet are five well-known machine learning methods whose performance is compared. The dataset utilized in this study comprises camera-captured sign language motions made by diverse people. We assess each algorithm’s performance using a variety of parameters, including accuracy. According to our findings, VGG16 performs better than the other four algorithms, with an accuracy rate of 99%. Therefore, we conclude that VGG16 is the most suitable algorithm for sign language detection, which can be used in real-time applications for the deaf and hearing-impaired community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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Computational Assessment Model for Blind Medical image Watermarking with Deep Learning 6th

Computational Assessment Model for Blind Medical Image Water...

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6th conference on Computational Methods in systems and Software (CoMeSySo)

作者： Chacko, Anusha Chacko, Shanty Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India Vimal Jyothi Engn Coll Dept Elect & Commun Engn Kannur Kerala India Karunya Inst Technol & Sci Dept Elect & Elect Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9783031214370;9783031214387

Blind watermarking is identified as one of the effective method of data hiding in image processing;however, existing literatures shows an adoption of sophisticated technique considering specific attacks. However, a potential gap is found where there is no report of resiliency of using blind watermarking towards resisting lethal threats. Therefore, this manuscript contributes towards offering a computational assessment model by constructing a lethal blind watermarking attacker model where a discrete orthogonal moments are extracted followed by dithering. The model is assessed on multiple modalities of standard medical image dataset as well as deep learning models. The outcome shows presented model accomplishes more than 45% of performance degradation from accuracy perspective. This outcome will offer clear guidelines of using deep learning models considering different medical image modalities to achieve better watermarking performance.

关键词： Blind watermarking Data hiding Attack Deep learning Modalities Accuracy Medical image

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Extracting Graphs from Plant Leaf Venations Using image processing 1

引用

4th International conference on image processing and Capsule Networks, ICIPCN 2023

作者： Balangcod, Ashlyn Kim D. Pabico, Jaderick P. Department of Mathematics and Computer Science University of the Philippines Baguio Baguio Philippines Institute of Computer Science University of the Philippines Los Baños College Laguna Philippines

ISBN: (数字)9789819970933

ISBN: (纸本)9789819970926

Leaf venation is an important characteristic of plant species that can lead to the identification of a plant. The study of leaf venation is laborious if done by manual inspection. This study proposes a leaf venations’ extraction process requiring minimal algorithms of image processing. Canny edge detection and Zhang-Suen thinning algorithm were used to extract the venation of the leaf images. Using ground truth tracing, the average values computed are 0.522 for correctness and 0.406 for completeness of the extracted venation. This study also presents the use of graph metrics to describe the leaf venations of plants. Using thirty images of five plant species, leaf venations up to the tertiary veins are compared using graph properties: size, volume, number of claws, number of wedges, and maximum degree. This study proposes these mentioned graph metrics as additional characteristics of leaf venations as results show a possible pattern for the five plant species. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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image Filtering and Edge Detection System Based on FPGA 10th

Image Filtering and Edge Detection System Based on FPGA

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10th International conference on Communications, Signal processing, and systems

作者： Sun Xiaodong Wang Zhiqiang Jing Wei Dalian Neusoft Univ Informat Sch Intelligence & Elect Engn Dalian 116023 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

The edge of the feature image contains rich data information, which is an important feature information of the image. The real-time display of the image is required in the actual system. In this paper, a real-time image filtering and edge detection system is designed by using Gaussian filtering and Sobel edge detection algorithm. The system is implemented on FPGA. According to the processing flow of image acquisition, image processing and image display, the function of real-time image display on the screen is realized. The image display effect under different threshold settings is compared, and the appropriate threshold settings are proposed.

关键词： FPGA image processing Edge detection algorithms Gauss filtering

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Frame-Adaptive Multi-Object Tracking for Discontinuous image Sequences 2

Frame-Adaptive Multi-Object Tracking for Discontinuous Image...

引用

2nd International conference on Algorithm, image processing and Machine Vision, AIPMV 2024

作者： Li, Xiaolei Xiao, Xingjie Yang, Siyuan Sha, Moquan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Wuhan China Wuhan Luojia Zhongheng Remote Sensing Data Technology Co Wuhan China China Unicom Smart City Research Institute Beijing China

ISBN: (纸本)9798350390254

In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance and traffic management. Despite the significant achievements of deep learning-based tracking methods in processing high frame rate videos, frame loss remains a prevalent issue in real-world deployments due to network and hardware constraints, which can result in erroneous target associations and interrupted trajectory tracking. Our research begins by detecting frame loss through a timestamp- based analysis, this is followed by the deployment of an adaptive algorithm that fuses spatial and appearance metrics to compensate for the positional discrepancies and visual inconsistencies caused by missing frames, thus improving the precision of data association. We have curated a dataset that accurately reflects real-world environments, and conducted validation on subsampled versions of the MOT17 and MOT20 datasets, in addition to our original dataset. Empirical results indicate that our multi-object tracking approach demonstrates significant adaptability and robustness in handling discontinuous image sequences, reducing the number of ID switches by 54.1 % on our original dataset compared to state-of-the-art methods. The proposed framework has the potential to significantly enhance the performance of multi-object tracking systems in real-world scenarios where frame loss is a common occurrence, thereby improving the reliability and accuracy of video surveillance and traffic management applications. © 2024 IEEE.

关键词： Adaptive algorithms

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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)9783031530357

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

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

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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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