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检索条件"机构=Key Laboratory of Image Processing and Intelligent"

共 1671 条记录，以下是11-20 订阅

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An English Pronunciation Evaluation Model Based on Acoustic Phonetics Features 4

An English Pronunciation Evaluation Model Based on Acoustic ...

引用

4th International Conference on Computer Science and Blockchain, CCSB 2024

作者： Wang, Hongkang Guo, Qingkai Huang, Guimin Zhou, Ya Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350356502

Oral English practice constitutes a crucial component of English language acquisition for non-native speakers;however, traditional classroom teaching methods are increasingly insufficient to address the diverse needs of learners. This paper enhances the evaluation of pronunciation quality within Computer-Assisted Language Learning (CALL) system. To assess the fluency of English learners' pronunciation, this paper introduces the innovative feature known as wordDurationRatio. Additionally, an intonation evaluation method utilizing dynamic time warping (DTW) algorithms is explored. Finally, this study incorporates support vector regression (SVR) algorithms to effectively fuse features, enhancing the assessment result of pronunciation quality. © 2024 IEEE.

关键词： Support vector regression

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Research on YOLOv8-Based Household Item Detection for Indoor Scenes 1

Research on YOLOv8-Based Household Item Detection for Indoor...

引用

1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Wu, Jingwen Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

This research is driven by the growing need for efficient, scalable object detection in smart home applications with real-time performance and resource constraints. In this study, we utilize the YOLOv8 deep learning model for household item detection, focusing on its performance in real-time scenarios. We compare different versions of YOLOv8 - specifically, YOLOv8n, YOLOv8s, and YOLOv8m - using a preprocessed custom dataset. The results indicate that YOLOv8n stands out due to its lower parameter count and faster inference speed, making it ideal for real-time applications, even though it has slightly lower precision and recall. In contrast, while YOLOv8s and YOLOv8m show improvements in certain metrics, their higher computational demands limit their effectiveness in resource-constrained settings. Overall, YOLOv8n effectively balances speed and accuracy, offering a viable solution for household item detection. © 2024 IEEE.

关键词： Smart homes

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Liver Tumor image Segmentation Method Based on U-Net Network 1

Liver Tumor Image Segmentation Method Based on U-Net Network

引用

1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Lu, Dinghong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

Liver tumors are the most common and fatal ones globally, bringing great harm to human life. In order to ascertain the precise location, size, and volume of tumors, the conventional method of manual segmentation necessitates that physicians delineate the tumor boundaries within the patients' abdominal CT scans, a process that demands considerable expenditure of time and effort on the part of the medical staff. A method harnessing the U-Net neural network, predicated on deep learning, has been introduced in this study to augment the accuracy and efficiency of liver tumor segmentation in imaging. The U-Net network has been enhanced with the incorporation of Adam optimization and callback mechanisms to bolster the training efficacy and preserve the most effective model. Furthermore, the implementation of the binary crossentropy loss function has been pivotal for optimizing the neural network's convergence and predictive accuracy, which in turn has led to enhanced segmentation results. The experimental findings suggest that the proposed method is capable of automating the segmentation of liver tumor images, including the precise delineation of tumors and those with indistinct margins, achieving a segmentation accuracy of 99.64%. © 2024 IEEE.

关键词： Computerized tomography

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Application of intelligent Schedule Management in Volunteer Service System 1

Application of Intelligent Schedule Management in Volunteer ...

引用

1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Li, Yongxin Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

As the demand for volunteer services grows, enhancing the efficiency and management of these services has become a key issue. This paper explores methods of integrating intelligent scheduling management functions with volunteer service systems to optimize volunteer time management and task allocation. By analyzing existing volunteer service systems, we propose a novel system framework that integrates intelligent scheduling management. This framework updates volunteer schedules in real- time, automatically matches tasks with volunteers' time windows, and optimizes task distribution using smart algorithms. Experimental results show that volunteer service systems with integrated intelligent scheduling management significantly improve task allocation accuracy and volunteer satisfaction. This research provides new ideas and practical basis for the intelligent development of volunteer services. © 2024 IEEE.

关键词： Digital family digital platform volunteer service

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Design of Smart Home System Based on ZigBee Technology 1

Design of Smart Home System Based on ZigBee Technology

引用

1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Liang, Yinfan Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

With the rise of the Internet of Things (loT) and digital homes, smart home technology has also advanced. Smart homes can proactively sense people's needs, providing users with a more comfortable and secure living environment, while also saving energy and enhancing safety, thereby greatly improving the quality of life. Thanks to loT technology, smart home devices can connect to the home network, enabling data sharing and centralized control. This project applies ZigBee and MQTT protocols to the field of smart homes, achieving intelligent control and remote control of devices such as lighting, appliances, and security. The ZigBee protocol is used for networking, connecting terminal nodes in the facility layer with the coordinator node, while the MQTT protocol is used to connect the coordinator node to the internet. This project has realized remote control, intelligent control, and smart security of the home system, providing users with a more convenient and comfortable home experience. © 2024 IEEE.

关键词： Smart homes

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Conformal Geometric Algebraic Description of Flight Collision Detection Algorithm Based on Cylinder Protection Zone 3

Conformal Geometric Algebraic Description of Flight Collisio...

引用

3rd International Symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Wu, Haobo Jiao, Weidong Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China

ISBN: (纸本)9798350350418

In order to provide sufficient reaction time for air traffic controllers and pilots, a Flight Conflict Detection Algorithm (FCDA) based on Cylindrical Protection Zone (CPZ) in the Conformal Geometric Algebra (CGA) is proposed. Initially, the CGA description of the CPZ is represented by using the CGA forms of the central circle and central axis segments of the cylinder. Then based on the inner and outer product and geometric product operations of CGA, the positional relationship of the two CPZs is judged to determine whether a flight conflict occurs or not. Subsequently, we compared the performance of flight conflict detection methods based on rectangular protection zones, spherical protection zones, and cylindrical protection zones through experiments in CGA and Euclidean space, respectively, the experimental results show that in CGA, the proposed method has a faster speed than the flight conflict method based on cuboids, and is basically equivalent to the method based on spherical protection zones. Significantly, all CGA-based methodologies demonstrated superior speed over their Euclidean space counterparts. Considering the propensity of rectangular and spherical zones to elicit false alarms due to their expansive coverage of airspace, the CGA-CPZ-FCDA method emerges as the superior choice. © 2024 IEEE.

关键词： Spheres

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Facial Recognition-Based Classroom Attendance System Utilizing Deep Learning 1

Facial Recognition-Based Classroom Attendance System Utilizi...

引用

1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Zhou, Cong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

In recent years, artificial intelligence technologies, especially facial recognition, have made significant strides. With its high precision and efficiency, facial recognition has shown great potential and advantages in areas such as identity verification and behavior analysis. Classroom learning is the main channel through which students acquire knowledge, develop skills, and improve their competencies. A well-organized classroom attendance system is essential for ensuring the effective implementation and smooth execution of teaching plans. This system is based on an OpenCV and PyQt5 facial recognition client, utilizing the LBPH algorithm to achieve efficient facial recognition. In addition to providing a simple check-in function, the system offers a complete registration process and data management features. In practical use, students simply need to check in by facing the camera, allowing the system to automatically recognize the student's face and record the attendance data. This significantly simplifies the check-in process for both teachers and students, thereby improving classroom management efficiency. © 2024 IEEE.

关键词： Information management

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

引用

7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

引用

17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Spatiality-Aligned Graph Convolution Hashing for Unsupervised Cross-Modal Retrieval 2

Spatiality-Aligned Graph Convolution Hashing for Unsupervise...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Li, Fang Dai, Liulian Lan, Rushi Yang, Rui Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9798331515669

Unsupervised cross-modal retrieval leveraging hash learning has attracted a lot of interest from academics, primarily due to its minimal storage requirements, swift retrieval speeds, and label-free nature, making it a prime candidate for real-world applications. Nevertheless, the inherent "heterogeneous differences"and "semantic gap"within cross-modal data features pose significant challenges. Despite various attempts by scholars to address these issues, the existing solutions often fall short in terms of effectiveness. This can be attributed to several reasons: Firstly, image features, in contrast to text features, possess a plethora of attributes, making it challenging to extract features that possess strong expressive capabilities. Secondly, each modality of data exhibits not only inter-node correlations but also intricate relationships between individual nodes and their neighboring nodes. Consequently, a straightforward semantic correlation matrix is inadequate in capturing the intricate structural relationships within the data. Thirdly, the neglect of internal structural associations results in insufficient representation capabilities of binary hash codes in the Hamming space, hampering the learning of effective hash functions. To address these limitations, we introduce a Spatiality-Aligned Graph Convolution Hashing (SAGCH) algorithm. Extensive experiments demonstrate that our SAGCH method excels in both "image-to-text retrieval"and "text-to-image retrieval"tasks. © 2024 IEEE.

关键词： Semantics

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