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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Anandha Prakash, S. Denisha, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

Blood vessel segmentation is the process of identifying and describing blood vessels from retinal pictures, which is required for quantitative analysis and anomaly detection. However, the accuracy and efficiency of the current blood vessel segmentation approach are frequently inadequate. By using deep learning techniques for blood vessel segmentation, it aims to increase retinal analysis's effectiveness and precision. Based on our findings, UNet outperforms FCN (94.02%) and SegNet (95.89%) in terms of accuracy, reaching a whopping 98.89%. UNet is well-suited for biomedical image segmentation tasks because to its symmetric encoder-decoder structure and skip connections, which allow for good feature extraction and spatial preservation. A user interface is used to provide the segmented image when a retinal picture is injected. It helps in the creation of computer-aided diagnostic tools for retinal diseases and showing in the application by providing a trustworthy method for correct blood vessel segmentation, which is essential for early detection and monitoring of various ocular pathologies. © 2024 IEEE.

关键词： User interfaces

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Adaptive Traffic Signal Control System Using Object Detection Based Approach 4

Adaptive Traffic Signal Control System Using Object Detectio...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Khan, Irfan Hasan, Mehedi Islam, Shamsul Reza, Suman Reza, Tuhin Efaz, Erteza Tawsif Faridpur Engineering College Department of Computer Science & Engineering Faridpur Bangladesh Louisiana State University Division of Electrical & Computer Engineering Baton RougeLA United States

ISBN: (纸本)9798331530938

In Bangladesh, most four-legged intersections use a static-timed or manually-controlled traffic signaling approach, which creates irrefutable congestion and requires human involvement. This paper proposes an adaptive traffic control system utilizing the YOLO object detection framework within CNNs to address this issue. The system captures and frames images using cameras facing every road at a roundabout to detect and count vehicles in real-time for providing lights based on the traffic conditions, prioritizing emergency vehicles such as ambulances, firetrucks, and police-car while also maintaining queues. It sets the signal duration considering road condition. Using a dataset of 10,462 images across 12 classifications of automobiles and a transfer learning approach in YOLOv8, the model achieved a mAP50 score of 0.90. This automated method reasonably enhances traffic flow management by dynamically adjusting signal lights without human intervention. © 2025 IEEE.

关键词： Adaptive control systems

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FaceSpeak: Expressive and High-Quality Speech Synthesis from Human Portraits of Different Styles 39

FaceSpeak: Expressive and High-Quality Speech Synthesis from...

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39th Annual AAai Conference on Artificial Intelligence, AAai 2025

作者： Zhang, Tian-Hao Zhang, Jiawei Wang, Jun Qian, Xinyuan Yin, Xu-Cheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Tencent AI Lab Shenzhen China

ISBN: (纸本)157735897X

Humans can perceive speakers’ characteristics (e.g., identity, gender, personality and emotion) by their appearance, which are generally aligned to their voice style. Recently, vision-driven Text-to-speech (TTS) scholars grounded their investigations on real-person faces, thereby restricting effective speech synthesis from applying to vast potential usage scenarios with diverse characters and image styles. To solve this issue, we introduce a novel FaceSpeak approach. It extracts salient identity characteristics and emotional representations from a wide variety of image styles. Meanwhile, it mitigates the extraneous information (e.g., background, clothing, and hair color, etc.), resulting in synthesized speech closely aligned with a character’s persona. Furthermore, to overcome the scarcity of multi-modal TTS data, we have devised an innovative dataset, namely Expressive Multi-Modal TTS (EM2TTS), which is diligently curated and annotated to facilitate research in this domain. The experimental results demonstrate our proposed FaceSpeak can generate portrait-aligned voice with satisfactory naturalness and quality. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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SlowFast Based Real-Time Human Motion Recognition with Action Localization

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computer Systems science & engineering 2023年第11期47卷 2135-2152页

作者： Gyu-Il Kim Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is ***,various issues such as data size limitations and low processing speeds make real-time extraction of video data *** analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic *** recognition is key in this *** recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various *** deep learning-based human skeleton detection algorithm is a representative motion recognition ***,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent ***,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution *** proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure *** input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit *** on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in *** detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,11

关键词： Artificial intelligence convolutional neural network video analysis human action recognition skeleton extraction

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Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming 28th

Facial Geometric Feature Extraction for Dimensional Emotion...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fu, Wenlong Chen, Qi Xue, Bing Zhang, Mengjie The Center for Data Center and AI and School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand

ISBN: (纸本)9783031900617

Geometric features derived from single static images have the potential to be highly effective for facial emotion analysis, as shape, structure, and spatial relationships are key factors. However, these aspects are rarely explored in existing research. In this paper, we propose a novel approach that utilizes Genetic Programming (GP) to automatically extract geometric features for more effective emotional representation. The proposed GP system uses various evaluation strategies, evolving either a single feature per run or multiple features within a single run. These GP-evolved features capture critical angular and distance-based relationships between facial landmarks, which are then integrated with an existing deep learning model to enhance performance. The results show that the proposed method achieves improved performance in dimensional emotion analysis, providing a more comprehensive understanding of emotional expressions in static images. In addition, our approach is effective in improving the accuracy of emotion predictions, establishing a foundation for more precise facial emotion analysis using geometric information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

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Intelligent Automation & Soft Computing 2023年第8期37卷 2371-2386页

作者： Min-Jeong Kim Byeong-Uk Jeon Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more ***,it is necessary to develop a method that achieves this task using object and behavior information within video *** methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a *** this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is *** and motions are first extracted from continuous images by combining existing DL-based image analysis *** weight of the continuous data pattern is then analyzed through data structuring to classify the overall *** performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action *** model achieved an accuracy of 0.9817,indicating excellent ***,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

关键词： Deep learning object detection abnormal behavior recognition classification data structuring

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Decision trees

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EVA2.0:Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training

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Machine Intelligence Research 2023年第2期20卷 207-219页

作者： Yuxian Gu Jiaxin Wen Hao Sun Yi Song Pei Ke Chujie Zheng Zheng Zhang Jianzhu Yao Lei Liu Xiaoyan Zhu Minlie Huang The Conversational AI Group Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical Engineering and Computer Science York UniversityToronto M3J1P3Canada

Large-scale pre-training has shown remarkable performance in building open-domain dialogue ***,previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model,ignoring the discussion of some key factors towards a powerful human-like chatbot,especially in Chinese *** this paper,we conduct extensive experiments to investigate these under-explored factors,including data quality control,model architecture designs,training approaches,and decoding *** propose EVA2.0,a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters,and will make our models and codes publicly *** and human evaluations show that EVA2.0 significantly outperforms other open-source *** also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.

关键词： Natural language processing deep learning(DL) large-scale pre-training dialogue systems Chinese open-domain conversational model

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Mask R-CNN-based Occlusion Anomaly Detection Considering Orientation in Manufacturing Process Data

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IEIE Transactions on Smart Processing and Computing 2022年第6期11卷 393-399页

作者： Lee, Seo-El Choi, So-Eun Park, Geon Kang, Ye-Yeon Baek, Ji-Won Chung, Kyungyong Department of Public Safety BigData Kyonggi University Suwon Korea Republic of Department of Industrial Management Engineering Kyonggi University Suwon Korea Republic of Division of Computer Science and Engineering Kyonggi University Suwon Korea Republic of Department of Computer Science Kyonggi University Suwon Korea Republic of Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of

In a manufacturing process, data analysis is conducted to identify defective products in real time to lower their massive production and improve the rate of efficient production. In the production process, it is difficult to find defective products mixed with normal products. Therefore, it is necessary to detect defective products generated in the production process and reduce the risk of their production. Consequently, this study proposes the Mask R-CNN-based occlusion anomaly detection method in consideration of the orientation of manufacturing process data. The proposed method uses Mask R-CNN to find abnormal objects, such as occluded objects, in a manufacturing process line. In the manufacturing process, some products are hidden. Accordingly, preprocessing in consideration of multiple orientations is applied to generate data. The generated data is performed to detect occlusions and anomalies using Mask R-CNN. The mean of IoU was compared to evaluate the detection accuracy of YOLO and Mask R-CNN. YOLO showed excellent performance when there was a constant distance and orientation and no occluded object. However, Mask R-CNN performed excellently when there was any occluded object and the orientation was considered. Therefore, for occlusion anomaly detection in a manufacturing process, Mask R-CNN can reduce the production rate of defective products. Copyrights © 2022 The Institute of Electronics and Information Engineers.

关键词： Object detection

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Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning 38

Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Le...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Fei Wang, Peng Zhang, Lei Chen, Zhenghua Wei, Wei Ding, Chen Lin, Guosheng Zhang, Yanning School of Computer Science Northwestern Polytechnical University China School of Computer Science and Engineering University of Electronic Science and Technology of China China Institute for Infocomm Research Centre for Frontier AI Research A*STAR Singapore School of Computer Science Xi'an University of Posts & Telecommunications China School of Computer Science and Engineering Nanyang Technological University Singapore

Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain. Yet, in practical scenarios where the target task diverges from that in the source domain, meta-learning based method is susceptible to over-fitting. To overcome this, we introduce a novel framework, Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning, which is crafted to comprehensively exploit the cross-domain transferable image prior that each image can be decomposed into complementary low-frequency content details and high-frequency robust structural characteristics. Motivated by this insight, we propose to decompose each query image into its high-frequency and low-frequency components, and parallel incorporate them into the feature embedding network to enhance the final category prediction. More importantly, we introduce a feature reconstruction prior and a prediction consistency prior to separately encourage the consistency of the intermediate feature as well as the final category prediction between the original query image and its decomposed frequency components. This allows for collectively guiding the network's meta-learning process with the aim of learning generalizable image feature embeddings, while not introducing any extra computational cost in the inference phase. Our framework establishes new state-of-the-art results on multiple cross-domain few-shot learning benchmarks. © 2024 Neural information processing systems foundation. All rights reserved.

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