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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18817 条记录，以下是4411-4420 订阅

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Bridging the Gap: From Food Recognition to Accurate Weight Estimation

Bridging the Gap: From Food Recognition to Accurate Weight E...

引用

Disruptive Technologies (ICDT), international conference on

作者： Bhagyashree Maharana Mayank Kumar Goyal Ali Imam Abidi Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Accurate estimation of food weight is of utmost importance in the assessment of diet in nutrition, healthcare, and wellness. Although great progress has been achieved with machine learning and computer vision in image-based food recognition, the task of accurate weight estimation is still a challenging one. This review summarizes the current methodologies that address the gap, focusing on the reduction of errors and novel innovations. The review compares 2D and 3D computational methods, emphasizes the advantages of hybrid multimodal models, and assesses their strengths and weaknesses. The role of datasets and annotation techniques is analyzed in detail, highlighting the importance of having diverse and accurate datasets for model improvement. Standard evaluation metrics and benchmarks are addressed to evaluate accuracy and comparability across methods. Realism emerging trends of self-supervised learning, multimodal data integration, and real-time edge computing applications are searched for as answers to these practical constraints found in reality: variability in food, occlusion, environmental factors all challenge; solution proposed here. This review is aimed at outlining future research guidance by summarizing recent innovations, addressing gaps, and providing a roadmap for practical, scalable, and accurate dietary assessment systems.

关键词： Technological innovation Image segmentation Accuracy Image recognition Three-dimensional displays Reviews Computational modeling Face recognition Estimation Real-time systems

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Simulation of BLDCM control System Built on Matlab/Simulink

Simulation of BLDCM Control System Built on Matlab/Simulink

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Electrical Engineering and control Science (IC2ECS), international conference on

作者： Junjie Gu Linsheng Li School of Electrical Engineering Shanghai Dianji University Shanghai China

Due to the development of electronic technology, computer technology, sensor technology, power electronics technology, brushless DC motor and its control technology has been a breakthrough progress [1]. This paper selects double closed-loop control technology for brushless DC motor to regulate speed. Firstly, the principle of brushless DC motor is introduced; secondly, the model of brushless DC motor is established and mathematical analysis is carried out; thirdly, the double closed-loop PI speed control is adopted, and the design and improvement of PI controller are mainly carried out to eliminate the static difference in the steady state of the brushless DC motor[2]; lastly, based on the platform of MATLAB/SIMULINK, the simulation model of the control system is established to simulate the speed closed-loop control system of the brushless DC motor. Finally, based on MATLAB/SIMULINK platform, the simulation model of the control system is established to simulate the closed-loop control system of the brushless DC motor. The simulation results show that the model system response is fast and smooth [3]. The double closed-loop control of brushless DC motor adopts current hysteresis loop, which is simple in structure and fast in response, and has certain theoretical and application significance.

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Comparative Analysis of YOLO-based Models for Vocal Cord Segmentation in Laryngoscopic Images 28th

Comparative Analysis of YOLO-based Models for Vocal Cord Seg...

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28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Steinbach, Jakub Vrba, Jan Jirsa, Tomáš Cejnek, Matouš Urbániová, Zuzana Department of Mathematics Informatics and Chemistry Faculty of Chemical Engineering University of Chemistry and Technology in Prague Technická 5 Praha 6 - Dejvice Prague166 28 Czech Republic Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Technická 1902/4 Praha 6 - Dejvice Prague160 00 Czech Republic Department of Otorhinolaryngology 3rd Faculty of Medicine Faculty Hospital Královské Vinohrady Charles University Ruská 87 Praha 10 Prague100 00 Czech Republic

This study presents a comparative analysis of segmentation models based on the YOLO (You Only Look Once) architecture for the task of vocal cord detection in laryngoscopic images. The yolov5, yolov8, and yolov9 architectures were evaluated using images obtained from laryngoscopic videos recorded during standard examinations at ORL clinics. The primary objective was to assess the efficiency of different model sizes and architectures in accurately identifying the position of vocal cords within the images. Our findings reveal that all evaluated architectures demonstrate proficiency in vocal cord detection, with comparable results across the models. However, there is a discernible difference in mean Average Precision (mAP) metrics (at IoU thresholds ranging from 0.5 to 0.95). Notably, yolov8 exhibits the highest mAP scores, followed by yolov5 and yolov9, indicating superior performance in identifying vocal cord regions. This comparative analysis provides valuable insights into the effectiveness of YOLO-based segmentation models for vocal cord detection, highlighting the importance of model size and architecture selection in medical image analysis applications. © 2024 The Authors.

关键词： Image segmentation

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Safe Screening for Logistic Regression with 0–2 Regularization 14

Safe Screening for Logistic Regression with 0–2 Regularizat...

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14th international conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th international Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022

作者： Deza, Anna Atamtürk, Alper Department of Industrial Engineering and Operations Research University of California BerkeleyCA United States

ISBN: (纸本)9789897586149

In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels. In this paper, we present screening rules that safely remove features from logistic regression with 0-2 regularization before solving the problem. The proposed safe screening rules are based on lower bounds from the Fenchel dual of strong conic relaxations of the logistic regression problem. Numerical experiments with real and synthetic data suggest that a high percentage of the features can be effectively and safely removed apriori, leading to substantial speed-up in the computations. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： computer vision

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SP-MAE: Lidar Beam-Induced Domain Adaptation for 3D Object Detection via Semantic Point Masked Autoencoders

SP-MAE: Lidar Beam-Induced Domain Adaptation for 3D Object D...

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computer, control and Robotics (ICCCR), international conference on

作者： Yaolin Liang Zhiming Tu Tao Wu College of Intelligence Science and Technology National University of Defense Technology Changsha China

In autonomous driving scenarios, lidar-based object detectors are widely used in 3D object detection. For the sake of safety redundancy, some self-driving vehicles will be equipped with multiple lidars with different beams, but training multiple lidar-based object detectors requires high costs. In our experiments, the performance of a lidar-based detector model trained on a high-beam lidar domain drops dramatically when transferred to a low-beam lidar domain. At present, there is no public 3D object detection dataset across-lidar-beams domains. In this paper, a 3D object detection dataset adapted across-lidar-beams domain is produced by the LGSVL simulator. The reason for the performance degradation of the lidar-based detectors across lidar-beams domains are mainly due to the large gap in the foreground object point count. To solve this problem, this paper proposed a semantic point generation method based on masked autoencoders, which can bridge the data gap between lidar beam-induced domains by generating more foreground semantic points so as to realize the migration of the detector model. In addition to solving the problem of lidar beam-induced domains migration, SP-MAE can also improve the performance of 3D lidar-based detectors. Experiments showed that the PVRCNN detector can improve 5.11 3D AP of pedestrian on the KITTI dataset after using SP-MAE.

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Research on Crowd Tracking Methods 25

Research on Crowd Tracking Methods

引用

proceedings of the 2025 3rd international conference on Communication Networks and Machine Learning

作者： Jinlong Wu Gang Zhang School of software Shenyang University of Technology Shenyang Liaoning China

ISBN: (纸本)9798400713231

Crowd tracking is an important research topic in the field of computer vision. In crowd scenes, tracking pedestrians often results in missing individuals, i.e. individuals without trajectory information, and incorrectly identifying pedestrian individuals, i.e. identifying non pedestrian targets as pedestrian individuals for tracking. Therefore, it limits the tracking accuracy of existing methods. In the past, most methods only considered pedestrian tracking research in sparse scenes, so they often overlooked the importance of these two issues on crowd tracking. To solve these two problems, this paper proposes a density map based crowd tracking method. This method includes three task branches, namely pedestrian individual detection branch, density map estimation branch, and re identification branch. The pedestrian individual detection branch is responsible for predicting the position information of pedestrian individuals. The density map estimation branch can obtain the number of pedestrian individuals in the image area, which is used to jointly construct the mean square error loss with the pedestrian individual detection branch. In crowded scenes, this method can generate a constraint on the number of pedestrian individuals in the detection branch by counting the population density map, which can help the pedestrian individual detection branch find missed individuals. Similarly, the heatmap of the pedestrian individual detection branch can be used to enhance the localization and counting ability of the population density map, forming a bidirectional constraint. The re identification task branch is responsible for extracting the apparent features of individual pedestrians, which are combined with their location information to establish continuous inter frame pedestrian connections. At the same time, in response to the problem of incorrect recognition of pedestrian individuals during the tracking process, which leads to incorrect association of the same individual bet

关键词： computer vision

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Global Point Cloud Descriptor for Place Recognition in Indoor Environments 16

Global Point Cloud Descriptor for Place Recognition in Indoo...

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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： Komorowski, Jacek Kurzejamski, Grzegorz Wysoczanska, Monika Trzcinski, Tomasz Warsaw Univ Technol Warsaw Poland

ISBN: (纸本)9789897584886

This paper presents an approach for learning-based discriminative 3D point cloud descriptor from RGB-D images for place recognition purposes in indoor environments. Existing methods, such as such as Point-NetVLAD, PCAN or LPD-Net, are aimed at outdoor environments and operate on 3D point clouds from LiDAR. They are based on PointNet architecture and designed to process only the scene geometry and do not consider appearance (RGB component). In this paper we present a place recognition method based on sparse volumetric representation and processing scene appearance in addition to the geometry. We also investigate if using two modalities, appearance (RGB data) and geometry (3D structure), improves discriminativity of a resultant global descriptor.

关键词： Place Recognition 3D Point Cloud RGB-D Deep Metric Learning

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Adaptive Engine Utilizes Least-Mean Square and Eye-Opening Monitor for Multi-Gb/s Serial Links

Adaptive Engine Utilizes Least-Mean Square and Eye-Opening M...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Alaa R. Altaee Department of Electrical and Computer Engineering Utah Valley University Orem Utah USA

A new adaptive engine employed for high-speed decision feedback equalizer (DFE) is proposed. The proposed adaptive engine utilizes the least mean square (LMS) of maximum jitter at the cross point of the received data signal to update the feedback tap coefficients of the DFE while the eye-opening monitor (EOM) is used to optimize the adaptation process. Updating the tap coefficients relies on the tolerance of maximum jitter at the received data cross point to record the violations and generate two-direction error signals. These signals are used to control the adaptation process. The proposed adaptive engine overcomes the drawback of wasting energy after approaching the convergence time when LMS is utilized and eliminated the drawback of EOM for not handling the adaptation process in two directions. The proposed adaptive engine is verified by employing a serial link designed in an IBM 65 nm 0.8V CMOS technology. The simulation results show that the data link with variable channel lengths is explored utilizing BSIM4 device models with Cadence Design Systems.

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Grid-connected Debugging Method of Digital Power Grid Based on LR Model

Grid-connected Debugging Method of Digital Power Grid Based ...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Jiguang Zhao Digital Power Grid Branch of China Southern Power Grid Digital Grid Group Co. Ltd. Guangzhou China

As an important data analysis method, LR has been widely used in many fields. In applications to practical classification problems, LR tends to give good results. However, traditional LR has significant weaknesses in overcoming the complexity and redundancy of the solution. To this end, many solutions have been proposed, which tend to be a common approach with few results. The main purpose of this paper is to research and design the debugging method of digital grid connection (GC) based on the logistic regression model (LRM). On this basis, this paper proposes an accurate autoregressive model and provides detailed information to ensure the reliability and excellence of the model, which is verified by experiments. The experimental results show that using different classification methods, the test error and selection of data sets have different results. It can be seen that the ten-fold cross-validation error for adaptive elastic net LR is 1/38 and the test error is 1/34. Compared with other methods, the classification performance of adaptive elastic net LR is also superior, even better than other classification methods.

关键词： Digital control Adaptation models Systematics Redundancy Process control Debugging Inverters

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Machine Learning Models for Automated Heart Disease Prognosis 3

Machine Learning Models for Automated Heart Disease Prognosi...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Ghoualmi, Lamis Benkechkache, Mohamed El Amine Ghoualmi, Abderraouf Djalil University of Constantine 2 Department of Computer Science Constantine Algeria University of Tennessee Department of Nuclear Engineering TN United States University of Algiers 1 Dapartement of Medicine Algiers Algeria

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

As reported by the Centers for Disease control and Prevention (CDC), heart disease is the major cause of death for both sexes and members of most racial and ethnic groups. Cardiovascular disease claims one life in the United States every 34 seconds. The expense of medical services, medications, and lost productivity because of deaths from heart disease causes the United States to lose a significant amount of money each year. At least half of all Americans have one or more of the following three major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other significant indicators are the presence of diabetes, obesity (high body mass index), a lack of physical activity, and excessive alcohol consumption. In recent times, the healthcare sector has discovered that machine learning is a practical and reliable decision-making tool. The latter tends to assist doctors in making informed decisions about the heart condition of a patient. It has become increasingly important to develop an effective early-stage cardiac disease prognosis using digital patient information to enhance clinical decision-making. This study intends to find the most accurate machine learning classifiers for these prognostic uses. To predict cardiac disease, several supervised machine-learning algorithms were used and compared in terms of accuracy and performance. The following methods were used in our research: Logistic Regression, Random Forest, Decision Tree, and K Nearest Neighbor. The heart disease dataset from Kaggle has been used to validate this research. The latter was created using data from the 2020 United States national health status survey of more than 410,000 people, performed yearly by the centers for disease control and prevention. © 2022 IEEE.

关键词： Decision trees

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