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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Yazed, Muhammad Syukri Mohd Shaubari, Ezak Fadzrin Ahmad Yap, Moi Hoon Faculty of Computer Science and Information Technology Department of Multimedia Johor Batu Pahat Malaysia Manchester Metropolitan University Department of Image Vision and Computing E116b John Dalton Building United Kingdom

ISBN: (纸本)9798350353853

The interpretation of Geometric Dimensioning and Tolerancing (GD&T) in engineering drawings is a critical aspect of design and manufacturing processes. However, traditional methods of manual annotation are time-consuming and often lead to variability in understanding, which can impact product functionality and inspection outcomes. To address these challenges, this paper proposes an automated approach to recognize GD&T in engineering drawings by combining deep learning techniques. The primary objective of this paper is to develop a hybrid model that integrates the YOLOv5 object detection model and vision OCR for symbol, text, and character extraction. The purpose is to streamline the interpretation process and improve the accuracy of GD&T recognition in engineering drawings. By training the YOLOv5 model on a diverse dataset and employing vision OCR for text retrieval, the model aims to detect objects and extract relevant text efficiently. Performance evaluation metrics, including precision, recall, and mean Average Precision (mAP), are used to assess the effectiveness of the proposed hybrid model. Experimental results demonstrate promising outcomes, with the model achieving high precision and recall rates, as well as a strong mAP score. These results indicate that the hybrid model can accurately recognize objects and text within engineering drawings up to 80%, thereby addressing the problem of inefficiency and variability associated with manual GD&T interpretation. This paper offers a novel solution to automate GD&T recognition in engineering drawings, contributing to enhanced efficiency and accuracy in design interpretation. The proposed model has significant implications for engineering graphics and design practices, as it facilitates better communication and collaboration among engineers, designers, and manufacturers. By streamlining design documentation processes, the hybrid model can be integrated into manufacturing workflows to improve productivity and quality

关键词： Precision engineering

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IoT Based Sport Healthcare Monitoring System 7

IoT Based Sport Healthcare Monitoring System

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7th International Conference on Software Engineering and computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Samsuddin, Muhammad Naim Mohd Raffei, Anis Farihan Mat Rahman, Nur Shamsiah Abdul Universiti Malaysia Multimedia Computing Computer Vision Faculty of Computing College of Computing and Applied Sciences Pahang26600 Malaysia

ISBN: (纸本)9781665414074

The Internet of Things is considered now as one of the feasible solutions for any remote value tracking especially in the field of health monitoring. Monitoring the athlete in aspect of fitness and training progress performances are very important to avoid accident happened during training. The proposed system developed the IoT based sport healthcare monitoring system using Arduino and several sensors. A fuzzy logic algorithm is also developed to provide decision for the athlete health condition. A web-based application is also developed to allow the instructor to store and retrieve the athlete information as well as monitor the fitness training performance of the athlete. © 2021 IEEE.

关键词： Internet of things

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Internet of Things (IoT) Based Door Lock Security System 7

Internet of Things (IoT) Based Door Lock Security System

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7th International Conference on Software Engineering and computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Rhunn, Tommy Cha Hweay Raffei, Anis Farihan Mat Rahman, Nur Shamsiah Abdul Universiti Malaysia Pahang Multimedia Computing Computer Vision Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Malaysia Pahang Information Systems Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia

ISBN: (纸本)9781665414074

A door enables you to enter a room without breaking through a wall. Also, a door enables you for privacy, environmental or security reasons. The problem statement which is the biometric system sometimes is sensitive and will not be able to sense the biological pattern of the employer's fingerprint due to sweat and other factors. Next, people tend to misplace their key or RFID card. Apart from that, people tend to forget their pin number for a door lock. The objective of this paper is to present a secret knock intensity for door lock security system using Arduino and mobile. This project works by using a knock intensity and send the information to mobile application via wireless network to unlock or lock the door. © 2021 IEEE.

关键词： Internet of things

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Extended Self-Critical Pipeline for Transforming Videos to Text (VTT Task 2021) – Team: MMCUniAugsburg

arXiv

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arXiv 2021年

作者： Harzig, Philipp Einfalt, Moritz Ludwig, Katja Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany

The multimedia and computer vision Lab of the University of Augsburg participated in the VTT task only. We use the VATEX [1] and TRECVID-VTT [2] datasets for training our VTT models. We base our model on the Transformer [3] approach for both of our submitted runs, i.e., for run 2021-01 . For our second model (2021-02), we adapt the X-Linear Attention Networks for Image Captioning [4] which does not yield the desired bump in scores. For both models, we train on the complete VATEX dataset and 90% of the TRECVID-VTT dataset for pretraining while using the remaining 10% for validation. We finetune both models with self-critical sequence training [5], which boosts the validation performance significantly. Overall, we find that training a Video-to-Text system on traditional Image Captioning pipelines [6] delivers very poor performance. When switching to a Transformer-based architecture our results greatly improve and the generated captions match better with the corresponding video (see Figure 3). Copyright © 2021, The Authors. All rights reserved.

关键词： Pipelines

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Implementation of YOLOv5 and vision OCR Hybrid Model for GD&T Recognition

Implementation of YOLOv5 and Vision OCR Hybrid Model for GD&...

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IEEE Symposium on computers & Informatics (ISCI)

作者： Muhammad Syukri Mohd Yazed Ezak Fadzrin Ahmad Shaubari Moi Hoon Yap Department of Multimedia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia (UTHM) Batu Pahat Johor Malaysia Department of Image Vision and Computing E116b John Dalton Building Manchester Metropolitan University United Kingdom

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

关键词： YOLO Training Productivity Performance evaluation Accuracy Quality assurance Text recognition

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St-Keys: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

SSRN

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SSRN 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia SM@RTS: Laboratory of Signals systeMs aRtificial Intelligence and neTworkS MIR@CL: Multimedia InfoRmation systems and Advanced Computing Laboratory Computer Vision Center Computer Science Department Universitat Aut`onoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluationon three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

arXiv

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arXiv 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Laboratory of Signals systems Artificial Intelligence and networks Sfax Tunisia Multimedia Information systems and Advanced Computing Laboratory Tunisia Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluation on three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, CC BY.

关键词： Image enhancement

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Robust Estimation of Flight Parameters for SKI Jumpers

Robust Estimation of Flight Parameters for SKI Jumpers

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IEEE International Conference on multimedia and Expo Workshops (ICMEW)

作者： Katja Ludwig Moritz Einfalt Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (数字)9781728114859

ISBN: (纸本)9781728114866

This paper presents a model that robustly estimates important flight parameters for ski jumpers during their flight phase based on several camera views from the side along the jumpers' typical flight trajectories. A convolutional neural network for pose estimation, but also trained to detect skis, serves as a base model. It identifies 98.0% of the relevant flight parameters correctly within an angle threshold of 5 degrees, improving by 11.6% over previous work. In postprocessing, a pose checker first removes all wrong poses by using comparisons of distances and relative positions of the detected keypoints. A second step executes two RANSAC variants. One robustly estimates the average pose and another one the average pose angles. This model lifts the detection performance to 99.3% of the relevant flight parameters within a threshold of 5 degrees.

关键词： Cameras Computational modeling Hip Pose estimation Videos Principal component analysis Head

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Addressing data bias problems for chest x-ray image report generation 30

Addressing data bias problems for chest x-ray image report g...

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30th British Machine vision Conference, BMVC 2019

作者： Harzig, Philipp Chen, Yan-Ying Chen, Francine Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany FX Palo Alto Laboratory 3174 Porter Drive Palo AltoCA United States

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using a dual word LSTM in a hierarchical LSTM model. In addition, we conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. Together with this analysis, we propose a way of selecting a model that generates more distinctive sentences. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation. © 2019. The copyright of this document resides with its authors.

关键词： Long short-term memory

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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