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

共 13664 条记录，以下是1381-1390 订阅

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Music Note Feature Recognition based on Dynamic Time Warping Algorithm

Music Note Feature Recognition based on Dynamic Time Warping...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Weida Su School of Music and Dance Mudanjiang Normal University Mudanjiang China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Traditional methods for classifying and recognizing musical note features suffer from low accuracy. In response, we propose a music note feature recognition method leveraging the Dynamic Time Warping (DTW) algorithm. The approach begins with an in-depth analysis of musical notes, utilizing the similarity matrix of note similarity under the standard distance of notes as the DTW distance matrix. From this matrix, we derive a criterion for selecting note features, which serves as the objective function for optimizing the subset of music note features. By operating with multiple populations, we obtain the expected value of higher musical note classification accuracy from various populations, serving as the quality evaluation for multi-population classification. Subsequently, a global objective evaluation is conducted on each musical note based on the evaluation values, facilitating music note classification and recognition. Experimental results validate the efficacy of the proposed method, demonstrating its ease of adoption in computer-assisted systems. Moreover, it achieves higher accuracy in music note feature classification and recognition, while reducing the overall recognition time compared to existing musical note feature extraction methods.

关键词： Computers image recognition Accuracy Heuristic algorithms Music Linear programming Feature extraction Classification algorithms Standards

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1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

1st EAI International Conference on Intelligent Systems and ...

引用

1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

ISBN: (纸本)9783031350801

The proceedings contain 75 papers. The special focus in this conference is on Intelligent systems and Machine Learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach;a Novel Oversampling Technique for Imbalanced Credit Scoring Datasets;a Blockchain Enabled Medical Tourism Ecosystem;Measuring the Impact of Oil Revenues on Government Debt in Selected Countries by Using ARDL Model;diagnosis of Plant Diseases by image processing Model for Sustainable Solutions;face Mask Detection: An Application of Artificial Intelligence;a Critical Review of Faults in Cloud Computing: Types, Detection, and Mitigation Schemes;video Content Analysis Using Deep Learning Methods;prediction of Cochlear Disorders Using Face Tilt Estimation and Audiology Data;F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets;quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges;multivariate Analysis and Comparison of Machine Learning algorithms: A Case Study of Cereals of America;competitive Programming Vestige Using Machine Learning;machine Learning Techniques for Aspect Analysis of Employee Attrition;AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance;Real-Time Identification of Medical Equipment Using Deep CNN and Computer Vision;design of a Intelligent Crutch Tool for Elders;an Approach to New Technical Solutions in Resource Allocation Based on Artificial Intelligence;gesture Controlled Power Window Using Deep Learning;novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition;a Novel Model to Predict the Whack of Pandemics on the International Rankings of Academia.

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RGB-Guided Resolution Enhancement of IR images

RGB-Guided Resolution Enhancement of IR Images

引用

International conference on systems, Signals and image processing, IWSSIP

作者： Marcel Trammer Nils Genser Jürgen Seiler AI & Signal Processing Lab Sielaff GmbH & Co. KG Herrieden Germany Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

This paper introduces a novel method for RGB-Guided Resolution Enhancement of infrared (IR) images called Guided IR Resolution Enhancement (GIRRE). In the area of single image super resolution (SISR) there exists a wide variety of algorithms like interpolation methods or neural networks to improve the spatial resolution of images. In contrast to SISR, even more information can be gathered on the recorded scene when using multiple cameras. In our setup, we are dealing with multi image super resolution, especially with stereo super resolution. We consider a color camera and an IR camera. Current IR sensors have a very low resolution compared to color sensors so that recent color sensors take up 100 times more pixels than IR sensors. To this end, GIRRE increases the spatial resolution of the low-resolution IR image. After that, the upscaled image is filtered with the aid of the high-resolution color image. We show that our method achieves an average PSNR gain of 1.2dB and at best up to 1.8 dB compared to state-of-the-art methods, which is visually noticeable.

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A Review on Smart Farming based on Soil and Environmental Factors with Deep Learning Techniques

A Review on Smart Farming based on Soil and Environmental Fa...

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2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Sathish, Routhu Chand, Thulluru Prem Prudhvi Gummaluri, Sai Ram Kanmani, Marripudi Vyas Daniya, T. Information Technology GMR Institute of Technology Andhra Pradesh Rajam India

ISBN: (纸本)9781665489621

India's global economy is critically dependent on agriculture, which also accounts for a sizable portion of GDP. This paper provides a review of the use of machine learning and deep learning techniques in agriculture and elaborates different systems developed for smart agriculture including crop recommendations, fertilizer suggestions, categorization procedures and diseases identification by images. Climate factors like temperature, rainfall, soil quality, and fertilizers are the key determinants of agricultural productivity. Decision making such as determining the diseases and applying solutions to them is an important step in agricultural activities. With advanced technologies evolved in this digital world we can build more sustainable systems to make these farming procedures automated. Deep learning techniques are giving very prominent results in solving problems of predictions and decision making. The application of machine learning algorithms that are used in solving different problems that are arising in farming process are discussed. This paper contributes to know the available techniques for the automated farming procedures. © 2022 IEEE.

关键词： image processing

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Design of Human Resource Management System Based on Ant Colony Algorithm

Design of Human Resource Management System Based on Ant Colo...

引用

Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Yanling Jia Yao Xing Xi'an Polytechnic University School of Management Shaanxi China

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Aiming at the limitations of traditional image recognition algorithms in the design of human resource management system, a design scheme of resource management system based on ant colony algorithm was proposed. Firstly, the influencing factors is accurately located through the colony foraging theory, and the indicators is reasonably divided to reduce interference, and the ant colony algorithm is used to construct the design scheme of the resource management system. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional image recognition algorithm in terms of the design accuracy of the resource management system and the processing time of influencing factors, and has obvious advantages. The design of resource management system plays an extremely important role in human resources, which can accurately predict and optimize the growth characteristics and product generation of human resources. However, traditional image recognition algorithms have certain limitations in solving resource management simulation problems, especially when dealing with complex problems. In this paper, a resource management system design scheme based on ant colony algorithm is proposed to better solve this problem. In this scheme, the influencing factors were accurately located through the swarm foraging theory, so as to determine the division of indicators, and the ant colony algorithm was used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on ant colony algorithm in the design of human resource management system can better solve the limitations of traditional image recognition algorithms and improve the simulation accuracy and efficiency.

关键词： Accuracy image recognition Key performance indicator Interference Prediction algorithms Resource management Human resource management

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Autofocus Backprojection Algorithm for Circular Synthetic Aperture Radar Imaging

Autofocus Backprojection Algorithm for Circular Synthetic Ap...

引用

Software System and Information processing (ICSSIP), IEEE International conference on

作者： Qi Peng Hongtu Xie Mengfan Ge Bin Xu Yingpei Chen Nannan Zhu School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China School of Systems Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

In this paper, an autofocus backprojection (ABP) algorithm for the circular synthetic aperture radar (CSAR) has been presented. First, the effect of motion errors on in the CSAR imaging is analyzed, and the ABP imaging algorithm is studied. Then, an ABP-based CSAR imaging process is investigated for the characteristics of CSAR motion errors, which can improve the imaging accuracy by reducing the image geometrical deformation through the image matching and non-coherent superposition. At last, the electromagnetic simulation data of CVDome and the real measured data of Gotcha are used to test the proposed algorithm, and experimental results verify its correctness and effectiveness.

关键词： Software algorithms Measurement uncertainty Imaging Information processing Radar imaging Software systems Radar polarimetry Software measurement Motion measurement Synthetic aperture radar

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Sport Safety Enhancement: A Novel Approach with Monitoring Matrix-Based image Acquisition and Analysis

Sport Safety Enhancement: A Novel Approach with Monitoring M...

引用

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

作者： Xinyi Zhang Department of Physical Education Shandong Management University Jinan China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

With the development of video array imaging technology, the accuracy and stability of video image acquisition have been improved. In this situation, this study proposes novel approach with monitoring matrix-based image acquisition and analysis for sport safety enhancement. This study includes the following four major innovations 1) Developed an image acquisition system based on a monitoring matrix, which can accurately capture the facial information of athletes through array imaging mode; 2) Proposed a new facial feature extraction algorithm, which realizes real-time analysis of facial images by fusing EAR and MAR features; 3) Proposed an improved expression recognition algorithm, and the improved activation function optimizes the expression recognition network to accurately identify the facial expressions of athletes; 4) Designed an image classification algorithm, and the 8 -pixel average mode is used for fuzzy information processing, so as to provide real-time warning information about the physical condition of athletes through the expression data. The experimental results show that the proposed method has satisfactory recognition accuracy on the Cambridge University Computer Laboratory's facial expression data set, and can accurately provide early warning of the athlete's status.

关键词： image recognition Accuracy Face recognition Imaging Feature extraction Real-time systems Classification algorithms Safety Monitoring Sports

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Advancing Brain Tumor Detection: A Cutting-Edge Machine Learning Approach Leveraging CAD systems

Advancing Brain Tumor Detection: A Cutting-Edge Machine Lear...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Mohamed Amine Guerroudji Kahina Amara Nadia Zenati Zohra Ameur Centre for Development of Advanced Technologies Algeria University of Mouloud Mammeri UMMTO Tizi-Ouzou Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

In recent decades, medical imaging has emerged as a vital field in medicine, playing a crucial role in diagnosis. Computer Assisted Diagnosis (CAD) systems have become instrumental in this arena, employing sophisticated algorithms to extract crucial information from medical images. This study presents an innovative brain cancer detection system utilizing statistical classification methods. The approach involves three key stages: firstly, the identification of regions of interest through Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques; secondly, the characterization of these regions using morphological and textural parameters; and finally, employing this characterization as inputs for a Bayesian network to classify malignant and benign cancer cases. Experimental validation of the proposed approach yielded impressive results, including a 100% sensitivity rate and a classification accuracy exceeding 98% for tumor segmentation. These findings underscore the high efficacy of the proposed CAD system, showcasing its potential in enhancing cancer diagnosis and patient care.

关键词： Solid modeling Sensitivity Instruments Signal processing Vectors Bayes methods Topology

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CONFIDENCE GUIDED NETWORK FOR ATMOSPHERIC TURBULENCE MITIGATION

CONFIDENCE GUIDED NETWORK FOR ATMOSPHERIC TURBULENCE MITIGAT...

引用

IEEE International conference on image processing (ICIP)

作者： Nair, Nithin Gopalakrishnan Patel, Vishal M. Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

ISBN: (纸本)9781665441155

Atmospheric turbulence can adversely affect the quality of images or videos captured by long range imaging systems. Turbulence causes both geometric and blur distortions in images which in turn results in poor performance of the subsequent computer vision algorithms like recognition and detection. Existing methods for atmospheric turbulence mitigation use registration and deconvolution schemes to remove degradations. In this paper, we present a deep learning-based solution in which Effective Nearest Neighbors (ENN) based method is used for registration and an uncertainty-based network is used for restoration. We perform qualitative and quantitative comparisons using synthetic and real-world datasets to show the significance of our work.

关键词： Atmospheric turbulence mitigation deep learning image registration deblurring

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Synthmanticlidar: A Synthetic Dataset For Semantic Segmentation On Lidar Imaging

Synthmanticlidar: A Synthetic Dataset For Semantic Segmentat...

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IEEE International conference on image processing

作者： Javier Montalvo Pablo Carballeira Álvaro García-Martín Video Processing and Understanding Lab Universidad Autónoma de Madrid Madrid Spain

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, collecting and labeling real LiDAR data is an expensive and time-consuming task. While datasets such as SemanticKITTI [1] have been manually collected and labeled, the introduction of simulation tools such as CARLA [2], has enabled the creation of synthetic datasets on demand. In this work, we present a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counter-parts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution. Using this tool, we have generated SynthmanticLiDAR, a synthetic dataset for semantic segmentation on LiDAR imaging, designed to be similar to SemanticKITTI, and we evaluate its contribution to the training process of different semantic segmentation algorithms by using a naive transfer learning approach. Our results show that incorporating SynthmanticLiDAR into the training process improves the overall performance of tested algorithms, proving the usefulness of our dataset, and therefore, our adapted CARLA simulator. The dataset and simulator are available in https:// ***/vpulab/SynthmanticLiDAR.

关键词： Training Laser radar Semantic segmentation Transfer learning Imaging Labeling Task analysis

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