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Sign Language Translation 6

Sign Language Translation

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6th International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Harini, R. Janani, R. Keerthana, S. Madhubala, S. Venkatasubramanian, S. Saranathan Coll Engn Dept Comp Sci & Engn Panjappur Trichy India

ISBN: (纸本)9781728151977;9781728151960

Sign language is the way of communication for hearing impaired people. there is a challenge for common people to communicate with deaf people which makes this system helpful in assisting them. this project aims at implementing computer vision which can take the sign from the users and convert them into text in real time. the proposed system contains four modules such as: image capturing, pre-processing classification and prediction. By using image processing the segmentation can be done. Sign gestures are captured and processed using OpenCV python library. the captured gesture is resized, converted to grey scale image and the noise is filtered to achieve prediction with high accuracy. the classification and predication are done using convolution neural network.

关键词： Machine learning Gesture recognition Sign language Human computer Interaction computer vision

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Anti-Collision System for Accident Prevention in Underground Mines using computer vision 6

Anti-Collision System for Accident Prevention in Underground...

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6th International conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Imam, Mohamed Baïna, Karim Tabii, Youness Benzakour, Intissar Adlaoui, Youssef El Mostafa, Ressami El Hassan, Abdelwahed Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat Rabat Morocco Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat Rabat Morocco Reminex Research and Engineering Center MANAGEM Group Marrakech Morocco Moroccan Foundation for Advanced Science Innovation and Research MAScIR Rabat Morocco FSSM Cadi Ayyad University Marrakech Morocco

ISBN: (纸本)9781450396943

Underground prospecting operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. this can result in vehicle-to-vehicle collisions, as well as vehicle-to-pedestrian or structural-element collisions, resulting in accidents. In this article, we discuss an anti-collision system for pedestrian identification in deep mines under the premise that we are looking to prevent collisions with moving machinery. this study presents the findings from testing an image processing module and sensory system based on deep learnig in the context of "smart connected mine" project. © 2022 Association for Computing Machinery.

关键词： Accidents

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Attention guided complementary feature integration for latent image recovery from noisy/blurry pairs 21

Attention guided complementary feature integration for laten...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Rosh, K.S. Green Lomte, Sachin Krishnan, Nikhil Prasad, B.H. Pawan Samsung Research Institute Karnataka Bangalore India

ISBN: (纸本)9781450391276

Low-light imaging using a hand-held camera is a challenging task due to excessive noise in the scene. Most of the existing methods try to address this problem either by denoising an image captured using high ISO or by deblurring an image captured using long exposure time. However these methods use a single image to estimate the latent scene and hence fail to leverage the complimentary information available in the scene. In this paper, we propose a method to estimate the latent image using a pair of images captured using high ISO and high exposure time respectively, to leverage the complimentary information present in the two captures. We propose a novel deep learning based method to efficiently extract and integrate the information present in the two images. Contrary to other methods, we use separate filters to extract the complimentary information from the two images. We also progressively integrate the extracted features using a novel attention-guided mechanism. Further, we address the spatially varying nature and localization of motion blur in real life captures by using spatial attention layers. the proposed method achieves state-of-the-art performance against single as well as other noisy/blurry approaches to the problem. We also show that the network learns spatial attention maps with strong correlation to the blur in the scene, and thus the proposed method is more interpretable and easier to analyze. © 2021 ACM.

关键词： Deep learning

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In Situ Visual Quality Control in 3D Printing 15

In Situ Visual Quality Control in 3D Printing

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15th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP) / 11th International conference on Information Visualization theory and Applications (IVAPP)

作者： Kopsacheilis, Charalampos Charalampous, Paschalis Kostavelis, Ioannis Tzovaras, Dimitrios Ctr Res & Technol Informat Technol Inst CERTH ITI 6th Km Charilaou Thermi Rd Thessaloniki 57001 Greece

ISBN: (纸本)9789897584022

In the past decade, additive manufacturing technology has gained an immense attention in numerous research areas and has already been adopted in a wide range of industries relevant to transportation, healthcare, electronics and energy. However, the presence of defects and dimensional deviations that occur during the process hinder the broad exploitation of 3D printing. In order to enhance the capabilities of this emerging technology, online quality control methodologies and verifications of the manufacturing process are necessary to be developed. In the present article, a low cost in-situ vision-based monitoring technique applied in Fused Deposition Modeling (FDM) 3D printing technology is introduced. An optical scanning system was integrated in a commercial 3D Printer in order to scan and validate the performance of the procedure. the proposed methodology monitors the FDM process and correlates the theoretical 3D model with the manufactured one. this technique can be utilized in various additive manufacturing technologies providing integrity and reliability of the process, high quality standards and reduced production costs.

关键词： 3D Printing Additive Manufacturing Quality Control Optical Monitoring image processing

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Two Novel Methods for Multiple Kinect v2 Sensor Calibration 6th

Two Novel Methods for Multiple Kinect v2 Sensor Calibration

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6th International conference on computer vision and image processing, CVIP 2021

作者： Hazra, Sumit Pisipati, Manasa Puhan, Amrit Nandy, Anup Scherer, Rafal Machine Intelligence and Bio-Motion Research Lab Department of Computer Science and Engineering NIT Rourkela Odisha Rourkela769008 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad500075 India Department of Intelligent Computer Systems Czestochowa University of Technology al. Armii Krajowej 36 Czestochowa42-200 Poland

ISBN: (纸本)9783031113482

Camera calibration is an essential step for measuring an instrument’s accuracy by using its parameters. In this paper, we propose two methods for calibrating eight Kinect v2.0 sensors, namely, pairwise and simultaneous single-camera calibration. In the first method, an experimental setup is managed so that the eight Kinect cameras can view a 3D object on a treadmill in consecutive pairs. the novelty of this method infers pairwise calibration of eight Kinects using six steps, specifically: precalibration process, acquiring images from Kinect, exporting the parameters, generation of independent live point clouds, feeding exported parameters for point cloud matching and ensuring successful calibration through merged point clouds. In the second method, a novel octagonal model is developed to calibrate each of the eight cameras on the same experimental setup. We obtain an overall mean reprojection error of 1.37 pixels and 0.42 pixels for the first method and second method, respectively. the smallest reprojection error for the first method is reported to be 0.63 pixels and 0.27 pixels for the second method. the efficiency of the proposed methods is compared with the state-of-art techniques using the root mean square and the reprojection error metrics. We observed that the proposed methods outperform the existing techniques. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

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A Review: Isolated Arabic Words Recognition Using Artificial Intelligent Techniques 6

A Review: Isolated Arabic Words Recognition Using Artificial...

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6th International Scientific conference for Iraqi Al Khwarizmi Society, FISCAS 2021

作者： Shareef, S.R. Irhayim, Y.F. Department of Computer Science College of Computer Science and Mathematics University of Mosul Iraq

In recent few years, deep learning has fast growing in many fields as natural language processing, image recognition, handwriting recognition, computer vision, and speech recognition. Automatic speech recognition (ASR) is a technique that refers to translating spoken words from an acoustic waveform into a text equivalent to what the speaker says. More recently, the advances in deep learning can support ASR in improving the performance of systems accuracies. Arabic is a Semitic language, one of the oldest used and most communicated languages in the world. But, it least concentrated in the case of Arabic speech recognition and under-resourced languages. this paper presents a survey that focuses on an automatic speech recognition system based on isolating words technique for Arabic speech. It also highlights the facilities and tools for developing speech recognition systems. this work is intended to be a useful starting point for those who are interested in ASR. © Published under licence by IOP Publishing Ltd.

关键词： Speech recognition

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Ice Detection on Edge Device Based on Most Significant Digit First SVM 22

Ice Detection on Edge Device Based on Most Significant Digit...

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Proceedings of the 2022 6th International conference on Video and image processing

作者： Najafi Mohammadreza Saeid Gorgin Danial Javaheri Jeong-A Lee computer engineering Chosun University south korea computer engineering Chosun University south korea and Department of Electrical Engineering and Information Technology IROSt Iran

ISBN: (纸本)9781450397568

Abstract- the present notion of smart cities inspires urban planners and academics to provide citizens with modern, secure, and sustainable infrastructure and a decent standard of living. To meet this demand, video surveillance cameras have been installed to improve the safety and well-being of the populace. Despite technological advances in modern science, detecting anomalous events in surveillance video systems is difficult and takes extensive human effort. Due to the context-dependent nature of the anomalous concept, we identify several objects of interest and freely accessible datasets for anomaly detection. Iced surface in highways is one of the serious anomaly which causes a large number of accidents on the highway every year. CCTVs installed on highways can be a good tool to detect iced surface. Detection is a time-sensitive application of computer vision; hence, this paper has concentrated on investigating iced surface detection using edge devices and methods. We have leveraged Most-Significant Digit First arithmetic to improve the performance and resource utilization of the Support Vector Machine. We have applied our proposed method to address the problem of ice detection on highways, where the experimental results indicated a significant improvement in terms of accuracy, speedup, and energy consumption. these metrics are essential for edge computing and real-time intelligent surveillance applications.

关键词： computer arithmetic FPGA computer vision Machine learning

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Evaluation of Deep Learning Algorithms for Instance Segmentation on Cassava Root

Evaluation of Deep Learning Algorithms for Instance Segmenta...

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IEEE International conference on Knowledge Innovation and Invention (ICKII)

作者： Chanowat Tanasaksakul Khajonpong Akkarajitsakul Tobias Wojciechowski Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Big Data Experience Center and Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Bio- and Geosciences – Plant Sciences (IBG-2) Forschungszentrum Jülich GmbH Jülich Germany

Different algorithms were developed and evaluated for segmenting images of cassava roots. Cassava CSRs (CSR) have different shapes and sizes. Existing methods rely on image processing algorithms to segment CSR, but these often have problems with overlapping fibrous roots. In this study, deep learning architectures for instance segmentation HTC, SOLOv2, and Mask R-CNN were applied to CSR data to investigate whether these models can overcome the specific challenges of this domain. We created a custom dataset with 1,227 training, 100 validation, and 100 test images of CSRs with different root systems and high-quality segmentation masks. then, we compared the result of the SOLOv2 model to the Mask R-CNN and HTC models using the same network architecture. the standard metric for comparing instance segmentation models in computer vision such as mean average precision, was used for the evaluation. the results showed that all tested models applied to the considered area showed good prediction accuracy. SOLOv2 showed the best results on the test set with the computation of the model less than that of HTC. Increasing the model complexity and size did not impact the prediction accuracy in the instance segmentation of CSR. HTC achieved almost the same result as that of the SOLOv2 model but required almost doubling the number of parameters. the visualization of the predictions showed quality differences in terms of the accuracy of the segmentation masks. the SOLOv2 model showed the best masks overall. However, each model had problems in assigning the segmentation masks due to the overlap of a CSR. Many root systems still need to be measured for gene discovery and subsequent breeding. the results demonstrated the potential of deep learning instance segmentation models for CSRs needs to be increased with further research.

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Edge Computing-Enabled Lightweight Deep Neural Network for Real-Time Video Surveillance in Maritime Cyber-Physical Systems

Edge Computing-Enabled Lightweight Deep Neural Network for R...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Caofei Luo Fumin Wu Renhao Xiong Guanhua Xu Wen Liu CETC (Ningbo) Maritime Electronics Research Institute Co. Ltd. Ningbo China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Navigation Wuhan University of Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid developments of low-end IoT devices and artificial intelligence (AI), the heterogeneous and dynamic data has been increasing drastically, especially in the era of AI-enabled maritime cyber-physical systems (CPS). It is intractable to provide the efficient and flexible computer vision applications through traditional cloud-centric data processing strategies, which commonly suffer from the limitations, e.g., high cost, high- latency communication, and high occupied storage, etc. To meet these challenges, edge computing has meaningfully evolved into a flexible and powerful distributed architecture. It contributes to improvements in bandwidth savings and response time in practical applications. To guarantee efficient video surveillance in AI-powered maritime CPS, the edge computing-based lightweight deep network (termed ShipNet-YOLOv3), incorporated with Kalman filtering and Hungarian algorithm, is proposed to implement real-time detection and tracking of moving ships. In particular, ShipNet-YOLOv3 adopts Darknet-53 as the backbone network (i.e., feature extractor) with three prediction boxes. the channel pruning and fine-tuning strategy are further introduced to balance the trade-off between computational cost, network size, and detection (or tracking) accuracy. Experimental results on both synthetic and realistic scenarios have illustrated that the proposed method could provide superior ship detection and tracking results under different adverse imaging conditions. It thus has the capacity of promoting real-time video surveillance in AI -enabled maritime CPS.

关键词： Performance evaluation Filtering image edge detection Imaging Video surveillance Feature extraction Real-time systems Kalman filters Marine vehicles Artificial intelligence

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Tuning image Descriptors and Classifiers: the Case of Emotion Recognition 6th

Tuning Image Descriptors and Classifiers: The Case of Emoti...

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6th International conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2020

作者： Greche, Latifa Taamouch, Abdelhak Akil, Mohamed Es-Sbai, Najia Laboratoire SIGER FST USMBA Fès Morocco LIGM A3SI ESIEE Paris CNRS Universitè Gustave Eiffel Marne-la-Vallée77454 France

ISBN: (纸本)9789813368927

In general, the recognition involves several steps as follows: data acquisition, pre-processing, segmentation, feature extraction and classification. Automatic facial expression recognition has become a crucial technology in the computer vision field and its applications including identification and security, Medicine and Monitoring. the facial expression recognition system requires an algorithmic pipeline that involves two main blocks: feature extraction and classification. A large experimental session must lead to the adequate algorithmic pipeline, notably for identifying the best methods for feature extraction and classification to achieve robust facial expression recognition with high accuracy. thus, it is essential to analyse data using multiple methods of feature extraction and classification. In this paper, we propose an approach to automate the analysis of data by repeating tests made to tune and compare feature extraction and classification methods. We evaluate our proposed data analysis approach using video sequences with fundamental emotion states: neutral expression, disgust, fear, happiness, sadness, anger and surprise. To transform the face images into vectors of features, we use shape, texture, and contour descriptors. this enables storing images in a table of vectors. Each table related to every descriptor is analysed with Five classifiers have been used, which are support vector machine, linear discriminant analysis, k-nearest neighbors, naïve Bayes, and binary tree classifiers. the techniques 10-fold and Leave-One-Out Cross-validations and the grid search have been used to tune the hyperparameters methods and compare them, computing the average recognition rate F-measure as evaluation metric. Experimentation on ChonKanade image database (CK+) show that the proposed data analysis approach can find out the optimal combination to separate the data classes and identify the expression with a an F-score of 96.44%. © 2022, Springer Nature Singapore Pte Lt

关键词： Feature extraction

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