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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Ma, Ruyi Tianjin University of Technology School of Management Tianjin300384 China

ISBN: (纸本)9781665442763

Aiming at the problem of intelligent interactive 3D display image distortion, this paper designs a display computer vision system based on ARM processor. The paper realizes the 3D dimensional reconstruction by means of high-speed projector to high-speed projection of transmissive directional scattering screen. Then this paper establishes a mathemational model for distorted images and proposes a deep learning method based on CNN model to correct the distorted images in real time during projection. The experiment shows that the effect of correction is obvious. The system has the advantages of small size, low cost, wide viewing angle, high resolution and strong human-computer interaction. © 2022 IEEE.

关键词： Human computer interaction

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deep learning based Object Detection Algorithm for the Detection of Dental Diseases and Differential Treatments 19

Deep Learning based Object Detection Algorithm for the Detec...

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19th IEEE-India-Council International Conference (INDICON)

作者： Thulaseedharan, Athira Priya, Lal P. S. APJ Abdul Kalam Technol Univ Coll Engn Trivandrum Dept Elect Engn Trivandrum Kerala India

ISBN: (纸本)9781665473507

With a wide range of applications in cosmetic dentistry treatments, and treatment planning, the use of Artificial Intelligence (AI) and Machine learning is having a growing influence in the field of dentistry and is enhancing the growth of digital tools and technology. The surgical sector, automatic disease diagnosis, and recently established customized medicine are just a few of the disciplines that might benefit from AI support. Personalized medicine provides the inclination to diseases, diagnosis, and selection of the best treatment for a specific individual. In fields like image processing, deep learning has been able to match and improve human performance for exceedingly complicated tasks such as object detection models with classification, and prediction capabilities and for the identification of numerous dental diseases and their differential treatments. Many of these neural network applications face significant challenges due to the lack of labeled medical datasets, and oral disease datasets are no exception. This pilot study makes it possible to identify various dental diseases and the treatments for them by utilizing a dataset of 664 dental panoramic X-ray images which is considered a small number. These images have been used for training after being manually labeled for 9 separate classes. You Only Look Once (YOLO) version 5, a one-step search method primarily utilized for real-time object detection, is the deep neural network that was used in this study. Bounding boxes helped identify six differential treatments and three classes of dental diseases. Results show that this strategy was able to attain a Precision of 1.00, F1 score of 0.74, Recall of 0.83, and mAP of 72.4 percent by training with this smaller number of images, which shows tremendous potential for dentists and radiologists.

关键词： Darknet LabelImg panoramic X-rays Roboflow Yolo v5

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Enhancing Student Attendance Systems Using Palmprint Biometrics with EfficientNet and Vision Transformer Models

Enhancing Student Attendance Systems Using Palmprint Biometr...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2024

作者： Ennajar, Slimane Bella, Kamal Ait Ben Hamou, Khalid Ait Lahcen, Yassine Bouarifi, Walid Mathematical Team and Information Processing National School of Applied Sciences SAFI Cadi AYYAD University Marrakech Morocco Technology Higher School University of Cadi Ayyad Essaouira Morocco LISI Laboratory Faculty of Sciences University of Cadi Ayyad Marrakech Morocco

ISBN: (纸本)9783031909207

In this study, we propose an enhanced student attendance system leveraging palmprint biometrics combined with advanced deep learning models, specifically EfficientNet and Vision Transformer (ViT). Traditional attendance methods, such as manual roll calls, are prone to errors and inefficiencies. By integrating palmprint recognition, known for its high accuracy and non-intrusive nature, with EfficientNet optimized performance and Vision Transformer self-attention mechanisms, we aim to improve the reliability and scalability of attendance systems. Data augmentation techniques are applied to enhance model robustness against variations in palm orientations and lighting conditions. Our system is evaluated using the CASIA Palmprint image Database, where EfficientNet outperforms ViT B32 across key metrics, achieving a high accuracy of 97.88%. These results demonstrate the system's effectiveness for real-time applications in educational settings, offering a scalable and accurate solution for tracking student attendance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Educational Technology EfficientNet Palmprint biometric recognition Student Attendance Vision Transformer

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A COMPARATIVE STUDY OF SEVERAL SLFN-BASED CLASSIFICATION ALGORITHMS FOR URBAN AND RURAL LAND USE 24

A COMPARATIVE STUDY OF SEVERAL SLFN-BASED CLASSIFICATION ALG...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Lin, Yi Xie, Guangshun Zhang, Tinghui Yu, Jie Zhang, Hanchao Cai, Hanging Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Minist Nat Resources Key Lab Surveying & Mapping Sci & Geospatial Info Beijing 100036 Peoples R China Univ Stuttgart Inst Geodesy D-70174 Stuttgart Germany

In the study of urban sustainable development, accurate classification of land use has become an important basis for monitoring urban dynamic changes. Hence it is necessary to develop the appropriate recognition model for urban-rural land use. Although deep learning algorithms have become a research hotspot in image classification tasks in recent years, and many good results have been achieved. But other machine learning algorithms are not going away. Compared deep learning with machine learning, there are some advantages and disadvantages in data dependence, hardware dependence, feature processing, problem solving methods, execution time, and interpretability, etc. Especially in the classification for remote sensing images, the continuous research and development of traditional machine learning algorithms is still of great significance. In this paper, the performances of several SLFN-based classification algorithms were studied and compared, including ELM, RBF K-ELM, mixed K-ELM, A-ELM and SVM. Extreme learning Machine (ELM) is a new algorithm for single-hidden-layer feedforward neural network (SLFN). It has simple structure, fast speed and is easy to train. In some applications, however, standard ELM is prone to be overfitting and its performance will be affected seriously when outliers exist. In order to explore the performance of ELM and its improved algorithm for urban-rural land use classification, comparative experiments between three improved ELM algorithms (RBF K-ELM, mixed K-ELM and A-ELM), ELM and SVM with image data from several study areas were performed, and the classification accuracy and efficiency were analysed. The results show that the three improved ELM algorithms perform better than the standard ELM and SVM both in overall accuracy and Kappa coefficient. However, it is worth noting that the computation efficiency of RBF K-ELM and mixed K-ELM decreases greatly with larger image, the time cost is much more than other algorithms. Compared with othe

关键词： remote-sensing image classification land use extreme learning machine kernel function A-ELM machine learning

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A novel lightweight real-time traffic sign detection method based on an embedded device and YOLOv8

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JOURNAL OF real-time image processing 2024年第2期21卷 24-24页

作者： Luo, Yuechen Ci, Yusheng Jiang, Shixin Wei, Xiaoli Harbin Inst Technol Sch Transportat Sci & Engn Harbin Peoples R China Harbin Inst Technol Fac Comp Harbin Peoples R China

Traffic sign recognition, as one of the key steps of intelligent driving technologies, effectively avoids most traffic accidents by detecting the location and type of traffic signs in real time and providing the information to drivers or autonomous vehicles promptly. In addition, edge devices close to users has become an inevitable requirement for the development of IoT technology, real-time computing and the realization of network edge intelligence. Nowadays, the YOLO algorithm in object detection, developed to YOLOv8, is accompanied by various lightweight networks and lightweight methods to win by "fast", so this paper will propose an algorithm, fusing Ghost module and Efficient Multi-Scale Attention Module into YOLOv8, so that the model can improve the computing speed while maintaining the original characteristics. Furthermore, we choose Raspberry Pi as the object detection device due to its many characteristics such as lightweight, low power consumption. Through experiments, the model is trained on CCTSDB dataset, and the improved algorithm is tested on Raspberry Pi 4B. The results show that for three types of traffic signs, namely prohibited, indication and warning, the recognition accuracy mAP reaches 93.5% on the poor weather test set and 82.9% on the original test set, and the inference delay of Raspberry Pi reaches 0.79 s, which is effective in actual road test experiments. The improved model's accuracy has increased by 6.4% and 3.8% on two separate test sets compared to the original model, while the detection time has been reduced by 0.12 s. This research is of great significance to the maturation of assisted driving and autonomous driving technologies.

关键词： Traffic sign recognition GhostNet Efficient multi-scale attention Yolov8 Raspberry Pi deep learning

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IntelligentDine: Advanced Food Identification and Caloric Assessment through deep learning for Smart Diet Tracking

IntelligentDine: Advanced Food Identification and Caloric As...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： M. Vaithiyanathan CH. Krishna Prasad Abhipsa Lenka Sasmita Nayak P. Maniraj Arun Kumar Arigela Department of Information Technology Sri Sairam Engineering College Chennai Department of Computer Science And Engineering Anurag Engineering College Kodad Telangana Department of Electronics and Instrumentation Engineering Odisha University of Technology and Research Bhubaneswar Bhubaneswar Odisha Department of Mechanical Engineering Government College Of Engineering Kalahandi Bhawani Patna Odisha Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Thalavapalayam Karur Department of CSIT Marri Laxman Reddy Institute of Technology and Management (MLRITM) Hyderabad Telangana

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The IntelligentDine initiative introduces an inventive approach to enhance diet tracking and promote healthy eating habits by applying advanced deep learning techniques. The primary goal is to precisely identify various food items and offer real-time estimates of their calorie content, providing users with intelligent and personalized diet monitoring capabilities. Using the Python programming language, we employed the ResNet150 architecture model for food recognition and calorie estimation, utilizing the extensive Food 101 dataset with 37,046 food images spanning 101 distinct food classes. Following rigorous training and evaluation, our model demonstrated notable success, achieving a training accuracy of 97.30% and a validation accuracy of 98.02%. These compelling results underscore the efficacy of our proposed approach in accurately recognizing and categorizing diverse food items. Smart Bite, a key component of this initiative, harnesses deep learning algorithms to swiftly analyze input images within a web framework, identifying specific food items and estimating their calorie content. This valuable information empowers users to effectively monitor their dietary *** intelligent diet monitoring capabilities of Smart Bite enable users to make informed decisions about their food choices. By tracking and analyzing daily food intake, users can gain insights into their nutritional habits, set personalized goals, and make necessary adjustments for a balanced and healthy diet. SmartBite showcases the successful integration of deep learning techniques, specifically employing the ResNet150 architecture for food recognition and calorie estimation. With its impressive accuracy, real-time processing capabilities, and intelligent diet monitoring features, SmartBite has the potential to transform how individuals track and manage their dietary habits, fostering healthier lifestyles.

关键词： deep learning Training Adaptation models image recognition Estimation User interfaces real-time systems

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Monitoring the Growth of Tomatoes in real time with deep learning-based image Segmentation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第12期12卷 353-358页

作者： Widiyanto, Sigit Nugroho, Dheo Prasetyo Daryanto, Ady Yunus, Moh Wardani, Dini Tri Gunadarma Univ Dept Comp Sci Jakarta Indonesia Gunadarma Univ Dept Agrotechnol Jakarta Indonesia Gunadarma Univ Dept Informat Syst Jakarta Indonesia

Increasing agricultural productivity such as tomatoes needs to be increased, considering the consumption growth reaches 6.34% per year. Efforts to increase productivity can be made through several methods, such as counting and predicting the time of fruit to be harvested. This information is a a visual problem, so computer vision should solve it as an automation method in the industry world. With this information, the farmer can monitor the tomato fruit growth. The proposed method is a framework that has been implemented in real-time processing. To obtain growth information of tomatoes, the tomato area can be used as a region of interest (ROI) every week or another scheduled time. As the challenge of this research, this ROI can be extracted using segmentation analysis. The segmentation method used is Mask Region-Convolutional Network (R-CNN) with ResNet101 architecture. The accuracy of this method is obtained from the similarity value between the proposed method and the ground truth used, namely 97.34% using the Dice Coefficient and 94.83% using the Jaccard Coefficient. This result indicates that the method can extract the ROI information with high accuracy. So, the result can be used as a reference for the farmer to treat each tomato plant.

关键词： deep learning Mask R-CNN segmentation tomato growth

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Segmentation of Uterine Fibroids Ultrasound images in Focused Ultrasound Ablation Surgery Based on YOLACT

Segmentation of Uterine Fibroids Ultrasound Images in Focuse...

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Machine Vision, image processing and Imaging Technology (MVIPIT), International Conference on

作者： Ting Wang Zhibiao Wang Chongqing Medical and Pharmaceutical College Chongqing China State Key Laboratory of Ultrasound in Medicine and Engineering College of Biomedical Engineering Chongqing Medical University Chongqing China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Uterine fibroids (UF) are the most frequently occurring non-cancerous growths in women. The treatment of UF involves the utilization of focused ultrasound ablation surgery (FUAS), which effectively targets and treats these fibroids. Throughout the surgical procedure, ultrasound images serve as a means to closely monitor and guide the progress of the surgery. Due to the low resolution and the boundaries of the lesions are less clear in the operation. The study involved an analysis of ultrasound images from 1420 patients who underwent FUAS treatment for uterine fibroids. The aim was to identify the most effective real-time ultrasound image recognition method by comparing several deep learning algorithms. In order to accurately register the images, the segmentation results of magnetic resonance images were utilized to indirectly identify the surrounding tissues of uterine fibroids. The main objective of our study is to accurately outline key tissue information such as the bladder, uterus, ureteral balloon, and uterine contour, among others. in ultrasound images to provide data support for registration and tracking. Through the comparison of several deep learning networks for ultrasound recognition, the optimized YOLACT network was selected as the model for recognizing ultrasound images. The recognition accuracy of skin, bladder, catheter balloon and uterus are 95.010 %, 93.486 %, 88.084 % and 92.468 %, respectively. Due to the imaging characteristics of ultrasound images, we used the method of identifying the boundary of tissues and organs around uterine fibroids, and indirectly identified the bladder, ureteral balloon and skin to determine the spatial position of the uterus, which improved the accuracy and real-time performance of ultrasound image segmentation.

关键词： deep learning image segmentation Ultrasonic imaging Accuracy image recognition Uterus Surgery

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3rd International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

ISBN: (纸本)9789811604928

The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in image processing and Pattern Recognition. The topics include: Ensemble of Nested Dichotomies for Author Identification System Using Similarity-Based Textual Features;Feature Combination of Pauli and H/A/Alpha Decomposition for Improved Oil Spill Detection Using SAR;a Fast and Efficient Convolutional Neural Network for Fruit Recognition and Classification;copy-Move image Forgery Detection Using Discrete Wavelet Transform;a Comprehensive Survey of Different Phases for Involuntary System for Face Emotion Recognition;classification of Vehicle Type on Indian Road Scene Based on deep learning;indian Road Lanes Detection Based on Regression and clustering using Video processing Techniques;detection of Emotion Intensity Using Face Recognition;double Authentication System Based on Face Identification and Lipreading;fuzzy Approach to Evaluate Performance of Teaching Staff in Technical Institutions;safety Gear Check at Industries and Laboratories Using Convolutional Neural Network Based on deep learning;analysis of Changing Trends in Textual Data Representation;detection of Falsary Happening on Social Media Using image processing: Feature Extraction and Matching;Development of Multi Faces Recognition System Using HOG Features and Neural Network Classifier in real time Environment;extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation;Prediction of SO2 Air Pollution Quality Parameter of Kolhapur City Using time Series Analysis;A Big Data Prediction for Weather Forecast Using Hybrid ARIMA-ANN time Series Model;automatic Detection of Riots Using deep learning;protecting Big Data Sets from Unauthorized Users on Cloud;text Categorization: A Lazy learning-Based Approach.

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Advancing Underwater image Enhancement Using Hybrid deep learning Models

Advancing Underwater Image Enhancement Using Hybrid Deep Lea...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Upma Jain Nandini Shirish Boob Anandhi R J Archana Sehgal Munugapati Bhavana Yogendra Kumar Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Science Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Electrical Engineering GLA University Mathura India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

When attempting to make underwater images better, there are a lot of obstacles to overcome, including water's inherent color distortion, inadequate visibility, and light absorption and dispersion noise. The difficulties in accurately analyzing images caused by these problems apply to marine research, underwater robots, and environmental monitoring are only a few areas which are impacted. To address the limitations of existing picture enhancement techniques, this paper proposes a new hybrid deep learning CNN + GAN model for enhancing underwater photographs. The hybrid model extracts features with GANs and noise reduces with CNNs to produce better photos with better clarity and color correction. I train and validate the model using an underwater picture dataset and it defeats the state of the art approaches on visibility, color correctness, contrast and contrast. In quantitative and qualitative assessments our hybrid deep learning method yields superior generalization in different undersea environments relative to traditional methods. A suggested approach can potentially improve the underwater photography, marine setting data collecting with more precision, as well as better underwater exploration. This work now provides confidence in research progress into autonomous underwater exploration systems and underwater image processing.

关键词： Adaptation models image color analysis Computational modeling Transportation Generative adversarial networks Feature extraction real-time systems Internet of Things Colored noise image enhancement

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