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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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MATCHING AS COLOR imageS: THERMAL image LOCAL FEATURE DETECTION AND DESCRIPTION

MATCHING AS COLOR IMAGES: THERMAL IMAGE LOCAL FEATURE DETECT...

引用

IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Deshpande, Bhavesh Hanamsheth, Sourabh Lu, Yawen Lu, Guoyu Rochester Inst Technol Intelligent Vis & Sensing Lab Rochester NY 14623 USA

ISBN: (纸本)9781728176055

Feature detection and extraction is considered to be one of the most important aspects when it comes to any computer vision application, especially the autonomous driving field that is highly dependent on it. Thermal imaging is less explored in the field of autonomous driving mainly due to the high cost of the cameras and inferior techniques available for detection. Due to advances in technology the former does not hold true anymore and there lies tremendous scope for improvement in the latter. Autonomous driving relies heavily on multiple and sometimes redundant sensors, for which thermal sensors are a preferred addition. Thermal sensors being completely dependent on the infrared radiation emitted are able to frame and recognize objects even in the complete absence of light. However detecting features persistently through subsequent frames is difficult due to the lack of textures in thermal images. Motivated by this challenge, we propose a triplet based Siamese CNN for feature detection and extraction for any given thermal image. Our architecture is able to detect larger number of good feature points on thermal images than other best performed feature detection algorithms with superb matching performance based on our extracted descriptors.

关键词： Thermal Imaging Triplet Siamese CNN Unsupervised Network Feature Detection and Description

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YOLODrone: Improved YOLO Architecture for Object Detection in Drone images 44

YOLODrone: Improved YOLO Architecture for Object Detection i...

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44th international conference on Telecommunications and signal processing (TSP)

作者： Sahin, Oyku Ozer, Sedat Bilkent Univ Ozer Lab Dept Comp Sci Ankara Turkey

ISBN: (纸本)9781665429337

Recent advances in robotics and computer vision fields yield emerging new applications for camera equipped drones. One such application is aerial-based object detection. However, despite the recent advances in the relevant literature, object detection remains as a challenging task in computer vision. Existing object detection algorithms demonstrate even lower performance on drone (or aerial) images since the object detection problem is a more challenging problem in aerial images, when compared to the detection task in ground-taken images. There are many reasons for that including: (i) the lack of large drone datasets with large object variance, (ii) the larger variance in both scale and orientation in drone images, and (iii) the difference in shape and texture features between the ground and the aerial images. In this paper, we introduce an improved YOLO algorithm: YOLODrone for detecting objects in drone images. We evaluate our algorithm on VisDrone2019 dataset and report improved results when compared to YOLOv3 algorithm.

关键词： Object Detection UAV image analysis YOLO

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Fast Thermal Infrared image Ground Object Detection Method based on Deep Learning Algorithm 6

Fast Thermal Infrared Image Ground Object Detection Method b...

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6th international conference on Communication, image and signal processings, CCISP 2021

作者： Zhou, Guopeng Wu, Falin He, Jiaqi Li, Haolun Zhang, Chunxiao Yang, Gongliu Beihang University Beijing China Beijing Institute of Space Mechanics & Electricity Beijing China

ISBN: (纸本)9781665432795

Nowadays, there is an increasing demand for wild searching and nature reserve monitoring in nighttime environment. Combined with the advantages of thermal infrared (TIR) imaging technology and the flexibility of unmanned aerial vehicle (UAV), the UAV thermal infrared remote sensing can provide sufficient image data as the information source of decision-making and judgment for multi scene object detection task at night. In order to process these image data quickly and accurately, and to realize the purpose of ground object detection, this paper investigates a fast thermal infrared image ground object detection method based on deep learning algorithm. The proposed method achieves good accuracy and speed performance on the dataset, which shows 64.35% mAP with 41.83 FPS. This method is superior to the other general detection algorithms in detection accuracy and detection speed. © 2021 IEEE

关键词： Object detection

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Expression Recognition Based on Attention Mechanism and Length Feature of Facial Landmark 13

Expression Recognition Based on Attention Mechanism and Leng...

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13th international conference on Wireless Communications and signal processing, WCSP 2021

作者： Yang, Qian Liu, Feng Zhao, Zhenglai Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Image Processing Image Communications Nanjing China

ISBN: (纸本)9781665407854

In view of the fact that the proportion of face in the whole image is small and the face posture is skew, which leads to the low accuracy of facial expression recognition, this paper proposes a two-branch neural network based on attention mechanism and length feature of facial landmark. The first branch is based on the modified VGG19 and the integration of the convolutional block attention module (CBAM), which focuses on the extraction of important features of expression. The second branch extracts 68 feature points from the face image, and calculates the Euler distance between these feature points to acquire the length information of 2278 dimensions, and is finally normalized and fed into the full connection layer. The network composed of the first branch and the second branch is trained by joint fine-tuning, where L2 regularization is added to prevent overfitting. The proposed method is tested and verified on Jaffe, CK + and FER +, and the highest accuracy is 93.48%,96.12% and 84.73% respectively. The performance of the proposed method is more advance than many existing algorithms, which shows that the proposed model has good accuracy and robustness. © 2021 IEEE.

关键词： Face recognition

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Classification model of optical character recognition failures in unrecovered slider serial numbers in hard disk drive manufacturing and image capture processes 13

Classification model of optical character recognition failur...

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13th international conference on signal processing Systems, ICSPS 2021

作者： Chousangsuntorn, Chousak Tongloy, Teerawat Chuwongin, Santhad Boonsang, Siridech Department of Electrical Engineering School of Engineering Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang 10270 Thailand College of Advanced Manufacturing Innovation King Mongkut's Institute of Technology Ladkrabang 10270 Thailand

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

In hard disk drive (HDD) manufacturing processes, there are unrecovered serial number images about 0.01% from the standard optical character recognition (OCR) reading and deep learning approach. We found several failures from two main causes, i.e. manufacturing process and image capture process during standard OCR reading. We proposed classification model used for recognizing the serial number reading failures based on object detection You-Only-Look- Once (YOLO) algorithm and EfficientNet-B0 classification network as well as histogram analysis. The 1000 images captured by digital camera were used for training (600 images) and validation (400 images) the ROI detection model. The other 2100 captured images were used for training and testing classification OCR failure from manufacturing process model. The model testing was performed in 900 images contained 9 causes (classes) of failures. The proposed model reaches F1 score = 0.94. © COPYRIGHT SPIE.

关键词： Object detection

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Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based image Coding

Large-Scale Crowdsourcing Subjective Quality Evaluation of L...

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IEEE international conference on Visual Communications and image processing (VCIP) - Visual Communications in the Era of AI and Limited Resources

作者： Upenik, Evgeniy Testolina, Michela Ascenso, Joao Pereira, Fernando Ebrahimi, Touradj Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp Lausanne Switzerland Inst Super Tecn Inst TelecomunicacOes Lisbon Portugal

ISBN: (纸本)9781728185514

Learning-based image codecs produce different compression artifacts, when compared to the blocking and blurring degradation introduced by conventional image codecs, such as JPEG, JPEG 2000 and HEIC. In this paper, a crowdsourcing based subjective quality evaluation procedure was used to benchmark a representative set of end-to-end deep learning-based image codecs submitted to the MMSP'2020 Grand Challenge on Learning-Based image Coding and the JPEG AI Call for Evidence. For the first time, a double stimulus methodology with a continuous quality scale was applied to evaluate this type of image codecs. The subjective experiment is one of the largest ever reported including more than 240 pair-comparisons evaluated by 118 naive subjects. The results of the benchmarking of learning-based image coding solutions against conventional codecs are organized in a dataset of differential mean opinion scores along with the stimuli and made publicly available.

关键词： deep learning image coding learning-based compression subjective evaluation visual quality crowdsourcing

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ENHANCED CONVERGENT PNP algorithms FOR image RESTORATION

ENHANCED CONVERGENT PNP ALGORITHMS FOR IMAGE RESTORATION

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IEEE international conference on image processing (ICIP)

作者： Terris, Matthieu Repetti, Audrey Pesquet, Jean-Christophe Wiaux, Yves Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Heriot Watt Univ Dept Actuarial Math & Stat Edinburgh EH14 4AS Midlothian Scotland Univ Paris Saclay INRIA Cent Supelec Ctr Visual Comp F-91190 Gif Sur Yvette France

ISBN: (纸本)9781665441155

image restoration has long been one of the key research topics in image processing. Many mathematical approaches have been developed to solve this problem, e.g., variational methods, wavelet techniques, or Bayesian methods. With the widespread of neural network (NN) models in all the subdomains of data science, the performance limits of these methods are further pushed. One of the most successful strategies consists of plugging NNs in existing optimization algorithms. However, so doing raises several mathematical and practical challenges. One of the main issues is to secure the convergence of the resulting iterative scheme. Further questions concerning the characterization of the reached limit are also worth being addressed. In this paper, we show that the theory of maximally monotone operators allows us to bring insightful answers to these problems and to design firmly nonexpansive NNs;combining these with postprocessing NNs leads to excellent global restoration quality.

关键词： Neural networks forward-backward algorithm plug-and-play algorithm image restoration

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EXPOSING GAN-GENERATED FACES USING INCONSISTENT CORNEAL SPECULAR HIGHLIGHTS

EXPOSING GAN-GENERATED FACES USING INCONSISTENT CORNEAL SPEC...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hu, Shu Li, Yuezun Lyu, Siwei Univ Buffalo State Univ New York Buffalo NY 14260 USA Ocean Univ China Qingdao Peoples R China

ISBN: (纸本)9781728176055

Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work, we show that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes. The inconsistency is caused by the lack of physical/physiological constraints in the GAN models. We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN synthesized faces.

关键词： Media Forensics GAN image Detection

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Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF Algorithm

Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF...

引用

international conference on Guidance, Navigation and Control, ICGNC 2022

作者： Chen, Wantong Wu, Duo Ren, Shiyu Diao, Tianru Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300300 China School of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789811966125

At present, inversing wind vector from aircraft trajectory captured by automatic dependent surveillance-broadcast (ADS-B) is one of the main solutions to obtain wind field in aviation meteorology. However, some algorithms in this scheme still have some shortcomings, especially the poor performance when the aircraft turns at a small angle. In this paper, the geometric structure of ADS-B data is used to improve particle swarm optimized particle filter algorithm (PSO-PF) to inverse wind vectors in small area of single aircraft data and large area of multi-aircraft data respectively. The mean absolute error of wind estimation under different turning angles is evaluated. Experimental results show that the algorithm is feasible, and it still has a good inversion effect when the aircraft turns at a small angle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aircraft

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PERCEPTUAL QUALITY ASSESSMENT FOR RECOGNIZING TRUE AND PSEUDO 4K CONTENT

PERCEPTUAL QUALITY ASSESSMENT FOR RECOGNIZING TRUE AND PSEUD...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhu, Wenhan Zhai, Guangtao Min, Xiongkuo Yang, Xiaokang Zhang, Xiao-Ping Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781728176055

To meet the imperative demand for monitoring the quality of Ultra High-Definition (UHD) content in multimedia industries, we propose an efficient no-reference (NR) image quality assessment (IQA) metric to distinguish original and pseudo 4K contents and measure the quality of their quality in this paper. First, we establish a database including more than 3000 4K images composed of natural 4K images together with upscaled versions interpolated from 1080p and 720p images by fourteen algorithms. To improve computing efficiency, our model segments the input image and selects three representative patches by local variances. Then, we extract the histogram features and cut-off frequency features in the frequency domain as well as the natural scenes statistic (NSS) based features from the representative patches. Finally, we employ support vector regressor (SVR) to aggregate these extracted features as an overall quality metric to predict the quality score of the target image. Extensive experimental comparisons using seven common evaluation indicators demonstrate that the proposed model outperforms the competitive NR IQA methods and has a great ability to distinguish true and pseudo 4K images.

关键词： image quality assessment (IQA) noreference (NR) Ultra High-Definition (UHD) frequency domain analysis natural scene statistics (NSS)

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