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16th IEEE International symposium on Broadband Multimedia systems and Broadcasting (IEEE BMSB)

作者： Zhao, Lili Zhu, Zezhi Lin, Xuhu Guo, Xuezhou Yin, Qian Wang, Wenyi Chen, Jianwen Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665449083

LiDAR point cloud frame interpolation, which synthesizes the intermediate frame between the captured frames, has emerged as an important issue for many applications. Especially for reducing the amounts of point cloud transmission, it is by predicting the intermediate frame based on the reference frames to upsample data to high frame rate ones. However, due to high-dimensional and sparse characteristics of point clouds, it is more difficult to predict the intermediate frame for LiDAR point clouds than videos. In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process. Considering the inherited characteristics of RIs differ from that of color images, we introduce spatially adaptive convolutions to extract range features adaptively, while a high-efficient flow estimation method is presented to generate optical flows. the proposed model then warps the input frames and range features, based on the optical flows to synthesize the interpolated frame. Extensive experiments on the KITTI dataset have clearly demonstrated that our method consistently achieves superior frame interpolation results with better perceptual quality to that of using state-of-the-art video frame interpolation methods. the proposed method could be integrated into any LiDAR point cloud compression systems for inter prediction.

关键词： Point cloud frame interpolation range image

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image processing and pattern recognition-based car classification for intelligent transportation system

Image processing and pattern recognition-based car classific...

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6th Smart Cities symposium (SCS 2022)

作者： V. Dange S. Pophale N. Raut F. D. Shadrach S. Chowdhury G. Ramya P. Dutta Vishwakarma Institute of technology Pune India KPR Institute of Engineering and Technology Coimbatore India Dept. of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India MCA Department Sri Venkateswara College of Engineering and Technology Chittoor India SITE School VIT Vellore Vellore India School of Engineering and Technology Amity University Kolkata Kolkata India

the combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image processing-based car classification methods have been proposed in recent years to improve the performance of smart highway toll collection and monitoring of traffic systems. Unfortunately, all these algorithms are trained on a inadequate number of features derived from tiny datasets, which do not considered for real-time road traffic circumstances. To address these difficulties, proposed system presents a classification model for vehicle based on convolutional neural networks to enhance the resilience of car classifier model in real-time applications. To achieve real-time robustness in classification systems, this proposed system offers a Stanford car dataset of 16,185 images classified into 157 common vehicle classes. To begin, the car dataset was trained using Inception-v3, VGG16, and ResNet50 to evaluate their accuracy based on performance. As a result, a comparative study was carried out to assess the effectiveness of the proposed system.

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Temporal Constraints and Block Weighting Judgement Based High Frame Rate and Ultra-Low Delay Mismatch Removal System

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IEICE TRANSACTIONS ON INFORMATION AND systems 2020年第6期E103D卷 1236-1246页

作者： Du, Songlin Wang, Zhe Ikenaga, Takeshi Southeast Univ Sch Automat Nanjing 210096 Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactions, because it guarantees high-quality experiences for users. Existing image matching algorithms always generate mismatches which heavily weaken the performance the human-machine-interactive systems. Although many mismatch removal algorithms have been proposed, few of them achieve real-time speed with high frame rate and low delay, because of complicated arithmetic operations and iterations. this paper proposes a temporal constraints and block weighting judgement based high frame rate and ultra-low delay mismatch removal system. the proposed method is based on two temporal constraints (proposal #1 and proposal #2) to firstly find some true matches, and uses these true matches to generate block weighting (proposal #3). Proposal #1 finds out some correct matches through checking a triangle route formed by three adjacent frames. Proposal #2 further reduces mismatch risk by adding one more time of matching with opposite matching direction. Finally, proposal #3 distinguishes the unverified matches to be correct or incorrect through weighting of each block. Software experiments show that the proposed mismatch removal system achieves state-of-the-art accuracy in mismatch removal. Hardware experiments indicate that the designed image processing core successfully achieves real-time processing of 784fps VGA (640 x 480 pixels/frame) video on field programmable gate array (FPGA), with a delay of 0.858 ms/frame.

关键词： high frame rate ultra-low delay image matching mismatch removal temporal constraints block weighting judgement

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-the Yu, Jie Nguyen, thi-thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

this study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. the relief technique selects the feature subset, and the kernel parameters used as input for the system. the feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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image Denoising Using a Conditional Generative Adversarial Network with Residual Dense Blocks 16th

Image Denoising Using a Conditional Generative Adversarial N...

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作者： Li, Yuqin Zhang, Chengcheng Jiang, Zhengang School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9789813367562

image denoising is an important preprocessing step in image analysis. the image denoising models based on deep learning have been recently attracting considerable attention owing to its excellent performance. However, most existing methods are only suitable for certain types of noise, and these methods often cause the missing of detail information in images. In this paper, an image denoising method with generative adversarial network is proposed. In this framework, a conditional GAN with an embedment of residual dense blocks (RDBs) in the generator is used for image denoising, in which the images with some artificial noises are used as condition. Meanwhile, a sum of Wasserstein distance, perceptual loss and reconstruction loss is employed to act as our final objective loss function to train the model. By comparing with several experiments with different denoising models on several datasets, the generated results show that our proposed model can reduce the noise and recover the noise images effectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： image denoising

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Usage of Psychophysiological Data as an Improvement in the Context of Software Engineering: A Systematic Mapping Study 20

Usage of Psychophysiological Data as an Improvement in the C...

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16th Brazilian symposium on Information systems - Information systems on Digital Transformation and Innovation (SBSI)

作者： Vieira, Roger Denis Farias, Kleinner Univ Vale Rio dos Sinos Sao Leopoldo RS Brazil

ISBN: (纸本)9781450388733

A systematic mapping had been developed seeking for answering six research questions regarding using of psychophysiological data in the Software Engineering domain. Employing it was selected 2084 studies from 7 digital databases, reaching a final amount of 27 articles. Mostly of experimental nature, marked by usage of the sensing devices and integrated development environments (IDEs), looking for analysis of developers' psychophysiological attributes applied to comprehension and debugging of source code. It was not identified as native mechanisms to support psychophysiological data integration and analysis within the integrated development environment.

关键词： Data Analysis Software Engineering Neurosciences Bioinformatics Data processing

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Partial Facial Identification using Transfer Learning Technique

Partial Facial Identification using Transfer Learning Techni...

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International Joint symposium on Artificial Intelligence and Natural Language processing (iSAI-NLP)

作者： Sarin Watcharabutsarakham Sanparith Marukatat Supphachoke Suntiwichaya Chanchai Junlouchai Artificial Intelligence Research Group National Electronics and Computer Technology Center Pathum Thani Thailand Leveraging Technology Solutions Section National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9781665409483

In today’s world, people go outside wearing a face mask, so face detection and face recognition models need to take this into account. Facial recognition has been researched widely with various algorithms. Since the coronavirus disease of 2019 (COVID-19) outbreak has spread across thailand, our use of face recognition models has reminded people to wear a face mask. this is because when people go outside, they are likely to be exposed to facial image detection and classification methods which are used for authentication and authorization. In this paper, we use transfer learning such as YOLOv3 and training with public datasets and donation datasets. Our models can recognize faces with a 98.7% accuracy rate and identify faces including those with face masks-with a 92.7% accuracy rate.

关键词： COVID-19 Training image recognition Face recognition Transfer learning Learning (artificial intelligence) Glass

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image Feature Detection and Clustering for UAV Multiple Obstacles Avoidance 1

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作者： Zhao, Baohua Sung, Tien-Wen Zhang, Xin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In the past decade, a lot of researches have been done on the topic of real-time obstacle avoidance of Unmanned Aerial Vehicle (UAV). For example, the use of Light Detection and Ranging (LIDAR), ultrasonic sensors, and now more popular visual sensors can effectively avoid obstacles. Although these methods are simple and effective, there are few of UAV real-time obstacle avoidance for complex environments, and most of them are used to avoid a single obstacle. When facing multiple obstacles, they cannot choose the optimal strategy, which inevitably consumes more power and time. In view of the complex environment that UAV may face multiple obstacles, in order to avoid multiple obstacles with minimum efforts in the process of real obstacle avoidance, this paper proposes a new method to detect multiple obstacles. Firstly, it extracts obstacle feature points, and then uses unsupervised clustering algorithm in machine learning to divide the feature points of different obstacles into different categories, and finally detects the obstacles represented by convex hull of same category feature points. the method is simple and effective. this paper compares the time of several popular feature point detection and unsupervised clustering algorithms through experiments and estimates the approximate relationship between the number of feature points detected and the distance when the UAV approaches the obstacle, which is a priori study for the future practical obstacle avoidance link. It summarizes which algorithm can save more time and is more suitable for real-time obstacle avoidance of multiple obstacles. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR images 16

Unsupervised Domain Adaptation via CycleGAN for White Matter...

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16th International symposium on Medical Information processing and Analysis

作者： Alberto Palladino, Julian Fernandez Slezak, Diego Ferrante, Enzo Univ Buenos Aires CONICET Lab Inteligencia Artificial Aplicada Inst Ciencias Comp Buenos Aires DF Argentina Univ Nacl Litoral CONICET Sinc I Res Inst Signals Syst & Computat Intelligence Santa Fe Argentina

ISBN: (数字)9781510639928

ISBN: (纸本)9781510639928

Automatic segmentation of white matter hyperintensities in magnetic resonance images is of paramount clinical and research importance. Quantification of these lesions serve as a predictor for risk of stroke, dementia and mortality. During the last years, convolutional neural networks (CNN) specifically tailored for biomedical image segmentation have outperformed all previous techniques in this task. However, they are extremely data-dependent, and maintain a good performance only when data distribution between training and test datasets remains unchanged. When such distribution changes but we still aim at performing the same task, we incur in a domain adaptation problem (e.g. using a different MR machine or different acquisition parameters for training and test data). In this work, we explore the use of cycle-consistent adversarial networks (CycleGAN) to perform unsupervised domain adaptation on multicenter MR images with brain lesions. We aim at learning a mapping function to transform volumetric MR images between domains, which are characterized by different medical centers and MR machines with varying brand, model and configuration parameters. Our experiments show that CycleGAN allows us to reduce the Jensen-Shannon divergence between MR domains, enabling automatic segmentation with CNN models on domains where no labeled data was available.

关键词： image segmentation Magnetic resonance imaging Data modeling Brain mapping Clinical research Convolutional neural networks Data acquisition Dementia Magnetism Neuroimaging

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Single-image Super-Resolution: A Survey

Single-Image Super-Resolution: A Survey

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7th International Conference on Communications, Signal processing, and systems (CSPS)

作者： Yao, Tingting Luo, Yu Chen, Yantong Yang, Dongqiao Zhao, Lei Dalian Maritime Univ Coll Informat Sci & Technol Collaborat Innovat Res Inst Autonomous Ship Dalian Peoples R China Dalian Maritime Univ Coll Environm Sci & Engn Inst Environm Syst Biol Dalian Peoples R China

ISBN: (纸本)9789811365041;9789811365034

Single-image super-resolution has been broadly applied in many fields such as military term, medical imaging, etc. In this paper, we mainly focus on the researches of recent years and classify them into non-deep learning SR algorithms and deep learning SR algorithms. For each classification, the basic concepts and algorithm processes are introduced. Furthermore, the paper discusses the advantages and disadvantages of different algorithms, which will offer potential research direction for the future development of SR.

关键词： Single-image super-resolution Learning-based Deep learning Survey

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