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

共 16532 条记录，以下是2161-2170 订阅

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Athlete Score Prediction and Influencing Factors Analysis Based on SVM, RF and BP Neural Networks

Athlete Score Prediction and Influencing Factors Analysis Ba...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： xi Kuang Chenfei Wang Zihe Lu International School of Business and Finance Sun Yat-sen University Guangzhou China School of Mathematics Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The study develops a score prediction model to evaluate player performance. The model is established with two metrics (one-step accuracy and five-step accuracy) to measure its precision. Three different algorithms, including Random Forest (RF), Support Vector Machine (SVM), and BP neural network, are tested. Results indicate that the optimal method is the Random Forest algorithm, achieving the highest five-step accuracy of 84.12%. Additionally, player performance visualization in the context of finals is presented, enhancing the analytical process. Furthermore, two approaches are used to examine whether an athlete's score is related to momentum. The findings suggest that momentum and scores do not conform to a random distribution. The study also highlights the importance and correlation of all factors. The pearson correlation coefficient between momentum and the last 5-point score is found to be 0.255, ranking third among the simplified 9 indicators, only surpassed by historical rankings and servers. This implies a correlation between momentum and player performance.

关键词： Support vector machines Radio frequency Correlation coefficient Visualization Analytical models Accuracy Correlation Neural networks Predictive models Prediction algorithms

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An Efficient Mixed Bit-width Searching Strategy for CNN Quantization based on BN Scale Factors 2022

An Efficient Mixed Bit-width Searching Strategy for CNN Quan...

引用

7th International conference on Multimedia and image processing, ICMIP 2022

作者： Han, Xuecong Zhou, Xulin Ma, Zhong Xi'An Microelectronics Technology Institute Xi'an710065 China

ISBN: (纸本)9781450387408

In recent years, the rapid development of mixed-precision quantification technology has greatly reduced the scale of the model and the amount of calculation. However, the previous mixed bit-width strategies are too complicated, such as reinforcement learning strategies and Hessian matrix strategies. This paper proposes an efficient mixed bit-width searching strategy, which measures the sensitivity of the convolutional layer by the scale factors of the BN layer. The advantage of this strategy is that the parameters of the pre-Trained model are used and no extra computation is introduced, which greatly simplifies the complexity of the bit-width selection strategy. In this paper, Resnet18 and Resnet50 models are used to conduct comparative experiments, and the differences between the proposed strategy and several previous algorithms are compared in terms of accuracy, model size and computation amount. It is verified that the accuracy of quantization in this paper is reduced within 2% compared with FP32 baseline, and the accuracy is reduced with about 0.5% compared with HAWQ. Overall, the performance is similar to that of HAWQ. This paper also compares the calculation complexity of the quantized bit-width of HAWQ-V3 with the calculation complexity of the quantized bit-width of this paper, which proves that the computational complexity of the strategy in this paper is far less than that of HAWQ-V3. © 2022 ACM.

关键词： Convolutional neural networks

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Design of a College Student Mental Health Assessment System Based on Improved Apriori Algorithm

Design of a College Student Mental Health Assessment System ...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Lanfang Mao College of Surveying Mapping and Geographic Information Lanzhou University of Resources and Environment Lanzhou Gansu China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper emphasizes the need to design an enhanced Apriori algorithm-based mental health assessment system to address the mental health issues college students face. The article aims to assess the current mental health evaluation systems of college students and develop a framework for a mental health evaluation. It proposes using data mining algorithms, precisely the Apriori algorithm, to analyze and categorize the data. This process ultimately yields intelligent mental health evaluation results. Finally, the mental health astute assessment system's practicality and superiority are examined by targeted simulation tests. The findings demonstrate that the system described in the article effectively addresses the limitations of the existing mental health intelligence evaluation system. It enhances the precision and efficiency of mental health intelligence evaluation while ensuring system stability. Consequently, it satisfactorily fulfills college students' current mental health evaluation needs.

关键词： Databases image processing Mental health Medical services Computer applications Stability analysis Data systems Data mining Faces

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Two-dimensional Kapur Entropy Multilevel Thresholding Algorithm Based on Dynamic Programming and Spatial Contextual Information

Two-dimensional Kapur Entropy Multilevel Thresholding Algori...

引用

International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Jinming Li Bo Lei School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

The Kapur entropy multilevel thresholding image segmentation algorithm based on the two-dimensional histogram does not consider the spatial contextual information of image pixels, and the algorithm’s time complexity increases exponentially with the increase of the number of thresholds. To address the above problems, this paper proposes a 2D-Kapur entropy multilevel thresholding image segmentation algorithm based on an energy curve by using both the neighborhood information and spatial contextual information of pixels. To improve the computational efficiency of the multilevel thresholding segmentation algorithm, a dynamic programming computational framework of the 2D-Kapur entropy multilevel thresholding algorithm based on the energy curve is constructed. The performance of the proposed algorithm is compared with several different image thresholding algorithms on the noise and complex images using several evaluation indexes. The experimental results verify the effectiveness of the proposed algorithm.

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Improving Sparse Dictionary Learning with Rejection of Fully Exposed Patches

Improving Sparse Dictionary Learning with Rejection of Fully...

引用

European Signal processing conference (EUSIPCO)

作者： Matthieu Chancel Hacheme Ayasso Michel Desvignes Jean-Michel Vignolle Eric Lespessailles GIPSA-Lab Univ. Grenoble Alpes CNRS Grenoble INP Grenoble France Image Group TRIXELL Moirans France Department of Rheumatology Centre Hospitalier Universitaire Régional d'Orléans Orléans France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Sparse dictionary learning is widely used in image processing tasks such as denoising, classification and image enhancement. However, learning features from X-ray images can be challenging since some parts of the image contains no or very little clinical information which makes them totally exposed to X-rays. These areas are irrelevant for medical content reconstruction and waste the space in the dictionary. Moreover, they slow down the convergence of the learning methods. In this paper, we present a prior knowledge about these fully exposed areas based on their physical distribution and propose a specialized dictionary learning algorithm to improve convergence speed and the quality of the learned dictionary while keeping the same quality of reconstruction. We compare our solution to a variance based approach and show that our solution is robust to the X-ray dose and then more efficient than the variance-based approach.

关键词： Learning systems Dictionaries Noise reduction Signal processing algorithms Machine learning Signal processing X-ray imaging image reconstruction image enhancement Convergence

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Adapting Spatial Transformer Networks Across Diverse Hardware Platforms: A Comprehensive Implementation Study

Adapting Spatial Transformer Networks Across Diverse Hardwar...

引用

IEEE International conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Meriem Bettayeb Eman Hassan Muhammad Umair Khan Yasmin Halawani Hani Saleh Baker Mohammad Computer and Communication Engineering Department System-on-Chip (SoC) Lab Khalifa University Abu Dhabi UAE College of Engineering and IT University of Dubai Dubai UAE

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

The field of artificial intelligence (AI) holds a variety of algorithms designed with the goal of achieving high accuracy at low computational cost and latency. One popular algorithm is the vision transformer (ViT), which excels at various computer vision tasks for its ability to capture long-range dependencies effectively. This paper analyzes a computing paradigm, namely, spatial transformer networks (STN), in terms of accuracy and hardware complexity for image classification tasks. The paper reveals that for 2D applications, such as image recognition and classification, STN is a great backbone for AI algorithms for its efficiency and fast inference time. This framework offers a promising solution for efficient and accurate AI for resource-constrained Internet of Things (IoT) and edge devices. The comparative analysis of STN implementations on the central processing unit (CPU), Raspberry Pi (RPi), and Resistive Random Access Memory (RRAM) architectures reveals nuanced performance variations, providing valuable insights into their respective computational efficiency and energy utilization.

关键词： Accuracy Resistive RAM Transformers Hardware Computational efficiency Classification algorithms Central processing Unit

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Underwater Three-Dimensional Reconstruction Based on Sub-regional processing of Forward-Looking Sonar images 3rd

Underwater Three-Dimensional Reconstruction Based on Sub-reg...

引用

3rd International conference on Autonomous Unmanned systems, ICAUS 2023

作者： Guo, Jingwei Gao, Jian Li, Yufeng Su, Sijia Wang, Weixuan School of Marine Science and Technology Northwestern Polytechnic University Xi’an710072 China

ISBN: (纸本)9789819710942

Three-dimensional (3D) reconstruction plays a vital role in underwater exploration. However, forward-looking sonar technology captures 3D data in 2D images, which cannot be directly utilized for 3D reconstruction. In this paper, we present a novel method for underwater 3D reconstruction using an autonomous underwater vehicle (AUV) and sub-regional processing of sonar images. Firstly, we design an algorithm to extract pixel points based on the imaging characteristics of different regions in the sonar images, thereby dividing them into an extended highlighted frontier point set and a non-extended point set. Utilizing the sonar imaging principle, the non-extended highlight point set is directly recovered to generate a 3D point cloud according to the sonar imaging principle. For the highlighted extended boundary point set, the boundary extension of the highlighted part is analyzed to generate the edge line point cloud. Subsequently, the AUV poses are combined with the continuously generated point clouds from the sonar images to perform 3D point cloud reconstruction. Experimental results demonstrate that our method effectively enhances the utilization of sonar image data. Furthermore, we establish an underwater simulation experimental platform to validate the proposed method for reconstructing underwater environments. The results exhibit that our approach efficiently generates high-quality underwater 3D point clouds, yielding satisfactory outcomes. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Autonomous underwater vehicles

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Innovative System of Cameras Allowing to Coordinate the Flight of a Swarm of UAVs

Innovative System of Cameras Allowing to Coordinate the Flig...

引用

International conference on Control and Robots (ICCR)

作者： Jens Müller Joachim Horn Kevin Larkin Marie Schweim Anne Schweim Dept. of Control Engineering Helmut-Schmidt-University Hamburg Germany Dept. of Materials Science and Engineering Carnegie Mellon University Pittsburgh (PA) USA

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

This paper aims to deliver insights into an innovative camera system called the Swarm Flight Unit (SFU). The SFU enables unmanned aerial vehicles (UAVs) to coordinate their movements as a swarm without any external help. Over the course of several years of development, conventional image processing algorithms were used to tackle the task of identifying and localizing drones in real time. This paper will describe applied methods, show the results of validation tests performed within a dedicated test bench, and prospect further actions regarding device integration.

关键词： Performance evaluation Robot kinematics image processing Robot vision systems Cameras Autonomous aerial vehicles Real-time systems Drones

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A Detection Algorithm Based on SegFormer for Unmanned Driving

A Detection Algorithm Based on SegFormer for Unmanned Drivin...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Ziqi Liu Shenyan Guo Ziteng xie School of Ocean Engineering Harbin Institute of Technology Weihai China School of Automation Science and Engineering South China University of Technology Guangzhou China College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

Semantic segmentation matters in unmanned driving. The accuracy of semantic segmentation determines whether the vehicle can accurately identify the object on the road during driving. At present, the most popular semantic segmentation algorithm is deep learning network model with convolutional neural network as the main body, while SegFormer based on encoder and decoder proposed in recent years has not been introduced into the field of unmanned semantic segmentation. Aiming at this problem, this paper firstly divides the city road images into several small blocks. Secondly, the obtained block is sent to the encoder as input to get the multilayer feature maps. Finally, the feature maps are sent to the decoder predictive segmentation mask. On the Cityscape dataset, using SegFormer improved mIoU accuracy by an average of 6.564% compared to algorithms such as DeepLabV3+ and FPN, and SegFormer mIoU(ms+flip) similarly improved accuracy by an average of 6.072% in the case of data enhancement. It can be seen that SegFormer has unique advantages in the field of semantic segmentation, and will play an infinite potential in unmanned driving technology.

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Application of Time Series Anomaly Detection algorithms in Risk Identification

Application of Time Series Anomaly Detection Algorithms in R...

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International conference on

作者： Dawei Li Jian Zhang Shuai Yang Jian Zhao Bin Zhou Lei Xu Competition Policy and Assessment Center of State Administration for Market Regulation Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Traditional risk identification methods often rely on manual inspections and post-event processing, which struggle to cope with the vast amounts of data and the real-time changes in the internet environment. Time series anomaly detection algorithms, as an effective data analysis tool, can identify abnormal changes in time series data. By utilizing spectral residual anomaly detection algorithms and sub-sequence anomaly detection algorithms, models were constructed to conduct an in-depth anomaly detection analysis of price data on the JD platform, providing a new solution for risk identification. The experimental results show that the precision, recall, and F1 scores of these two time series anomaly detection algorithms are significantly better than those of other comparative algorithms, demonstrating significant application value in risk identification.

关键词： Sensitivity image processing Time series analysis Manuals Inspection Real-time systems Object recognition Detection algorithms Anomaly detection Monitoring

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