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Multi-scale pedestrian detection based on receptive field matching 21

Multi-scale pedestrian detection based on receptive field ma...

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5th international conference on Graphics and signal processing, ICGSP 2021

作者： Yan, Chaoqi Zhang, Hong Li, Xuliang Chen, Hao Yuan, Ding Image Processing Center Beihang University Beijing102206 China

ISBN: (纸本)9781450389419

Pedestrian detection is a hot topic in both academia and industry. However, the pedestrian in the image presents different scales due to the different distance from the fixed cameras. Thus, how to detect pedestrians of different scales has become an attractive problem in pedestrian detection field. In this paper, we propose a simple and compact multi-scale pedestrian detection architecture based on receptive field matching (denoted as RFMNet), which can cover continuous multi-scale pedestrians with 100% in theory. In this model, the receptive field is regarded as an invisible "anchor", so that the feature points with different scale receptive field can detect different scale pedestrian targets without any bells and whistles. Based on the above analysis, the proposed approach has an anchor-free setting. The extensive experiments on Caltech-USA benchmark demonstrate that our method outperforms the state-of-the-art pedestrian detection algorithms. © 2021 ACM.

关键词： Receptive field Pedestrian detection Caltech Anchor-free

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OCT image processing System For Numerical Simulation of Fundus Temperature Distribution

OCT Image Processing System For Numerical Simulation of Fund...

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18th international Scientific and Technical conference on Optical Technologies for Communications

作者： Shirokanev, Alexander S. Ilyasova, Nataly Yu Andriyanov, Nikita A. Demin, Nikita S. Samara Natl Res Univ Moskovskoye Shosse 34 Samara 443086 Russia IPSI RAS Branch FSRC Crystallog & Photon RAS Molodogvardeyskaya 151 Samara 443001 Russia Financial Univ Govt Russian Federat Leningradskiy Av 49 Moscow 125167 Russia

ISBN: (数字)9781510644250

ISBN: (纸本)9781510644250

The safety of laser coagulation in the treatment of diabetic retinopathy depends on the correct selection of laser exposure parameters. In this paper, it was proposed to use technology for numerical modeling of the temperature distribution in the fundus based on its structure formed from optical coherence tomography (OCT) images. For such a formation, it is required to process each image and then interpolate the three-dimensional surfaces. Further, the formed structure is used for numerical simulation. This approach will potentially allow automating the extremely time-consuming procedure for choosing a coagulate placement plan. The paper considers algorithms that improve performance when solving the problem of mathematical modeling using the maximum load of CPU and GPU devices, i.e. parallel and high-performance algorithms.

关键词： mathematical modeling image processing digital signal processing laser coagulation retinopathy high-performance algorithms

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MDIFNet: Multiscale distant information fusion network for thyroid segmentation in 3D ultrasound image 21

MDIFNet: Multiscale distant information fusion network for t...

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6th international conference on Multimedia Systems and signal processing, ICMSSP 2021

作者： Feng, Chen Yin, Tinghui Song, Hong Tang, Songyuan Yang, Jian Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology China Imaging Third Affiliated Hospital of Sun Yat-Sen University China School of Computer Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9781450390378

The thyroid segmentation in 3D ultrasound image can provide necessary volumetric information in diagnosis and treatment. Clinically, 3D thyroid US images are usually stacks of 2D frames acquired by freehand scanning, which results in good performance of 2D segmentation methods. However, thyroid nodules cause difficulty to these algorithms because of strong anisotropy, whereas current approaches are mostly for healthy thyroid images. This paper proposes a multiscale distant information fusion network to segment clinical thyroid images with nodules. Our fully convolutional network consists of the following: (1) deep supervision of normalized boundary distance map, (2) multi-scale fusion module combined with attention mechanism, and (3) dense dilated refinement module to refine segmentation probability in a multi-scale way. We conducted ablation study and cross-validation on clinical dataset provided by The Third Affiliated Hospital of Sun Yat-Sen University and obtained the best dice result of 0.9070. Notably, our method also outperformed other methods on ultrasonography dataset and obtained a dice score of 0.93. © 2021 ACM.

关键词： Ultrasonics

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Secure QIM-Based image Watermarking in JPEG Compression Domain 12th

Secure QIM-Based Image Watermarking in JPEG Compression Doma...

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12th international conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Fedoseev, Victor Kozlov, Daniil Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS – Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardeyskaya 151 Samara443001 Russia

ISBN: (纸本)9783030736880

Previously, we proposed two secure watermarking algorithms (IM-QIM and SIM-QIM) based on the Quantization image Modulation (QIM) technique that showed its robustness against a specific histogram attack with multiple images. Traditional QIM versions, including DM-QIM, DC-QIM, and others, are vulnerable to this attack. Our algorithms are based on a modified "correlation immune" scalar quantizer and do not introduce any correlation between the key bits and signal values. Earlier, these algorithms were implemented for grayscale images in the spatial domain. In this paper, we apply our approach in JPEG compression domain and analyze IM-QIM performance in this case. The experimental results have shown that IM-QIM maintains its security in the JPEG domain, while its robustness to typical distortions such as AGWN and JPEG compression improves, compared with spatial domain watermarking. This fact, along with the prevalence of QIM usage in the JPEG (block DCT) domain, indicates the high practical potential of the proposed algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image watermarking

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An Enhanced Bats Algorithm for Optimization Thresholds in Medical image Segmentation 17th

An Enhanced Bats Algorithm for Optimization Thresholds in Me...

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17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Dao, Thi-Kien Vu, Ngoc-Thanh Nguyen, Thi-Xuan-Huong Nguyen, Trong-The Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fujian Peoples R China Hai Phong Univ Med & Pharm Haiphong Vietnam Haiphong Univ Management & Technol Haiphong Vietnam VNU HCM Univ Informat Technol Multimedia Communicat Lab Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City 700000 Vietnam

ISBN: (纸本)9789811910531;9789811910524

The key to threshold segmentation is choosing the thresholds that will decide the segmentation's outcome-the calculation complexity increases as the number of thresholds increases, which causes challenges with traditional methods. This study introduces an enhanced bats algorithm (EBA) for selecting optimal picture segmentation thresholds and applies it to the global optimization issue of the segmentation function's objective function. The EBA blends well with picture segmentation that produces exceptional computation in global convergence and robustness and prevents trapping into local optimization. It is particularly well suited to solving complex functions with high dimensions and multiple peaks. Compared to GA and PSO, extensive theoretical study and simulation results reveal that EBA has greater efficacy, efficiency, stability of the range of thresholds, and quality in multi-image and multi-thresholds segmentation.

关键词： Enhanced bats algorithm Kapur entropy Optimization algorithms Picture threshold segmentation

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Research on the Teaching Mode of College English Based on Artificial Intelligence 4

Research on the Teaching Mode of College English Based on Ar...

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2021 4th international conference on advanced algorithms and Control Engineering, ICAACE 2021

作者： Liu, Aihua Kong, Dehua School of Foreign Language and Literature Wuhan Donghu University Wuhan China School of Computer Science Wuhan Donghu University Wuhan China

In recent years, the breakthroughs of AI have recently occurred in the fields of natural language processing, image recognition and so on. How to introduce AI technology into college English teaching and efficiently integrate with college English "teaching"and "learning"has not yet been fully studied. The AI technologies, especially in NLP, provide the possibilities of enhancing college English teaching qualities and build the new college English teaching mode. Studies on the efficient connection of teaching and learning concerning the applying of AI are scarcely found. This thesis analyses the integrate on of AI and college English teaching, especially the feedback of self-regulated learning evaluation and try to probe new teaching modes against the AI backdrop. Related researches have demonstrated that it will provide new ideas and entry points for the reform of the current college English teaching mode, and is of great significance for achieving the goals of college English teaching. © Published under licence by IOP Publishing Ltd.

关键词： image recognition

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Digital image restoration using modified richardson-lucy deconvolution algorithm

Digital image restoration using modified richardson-lucy dec...

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international conference on image processing and Capsule Networks, ICIPCN 2020

作者： Jency Rubia, J. Babitha Lincy, R. Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur602 117 India

ISBN: (纸本)9783030518585

Since the Surveillance camera came into the market, the advanced digital image processing is getting more research importance. One of the major issues of digital photographing is motion and image blur. This can be occurring when the camera is in motion, the shape of the blurring is constant, but only the spatial extent is varying. Here, a novel algorithm is proposed for deconvoluting the image samples in the filtering process. Moreover, the proposed algorithm is successfully useful for compressed domain operations such as downsampling, translation, and filtering, etc. The recommended deconvolution algorithm produces the approximated output images that are perceptually closer to the actual image with reduced computational complexity. In this paper, the proposed multiplier less convolution algorithm is employed in the Richardson-Lucy filter for the Deblurring analysis. Also, this proposed method was compared with the existing techniques and stated that the novel multiplier less deconvolution algorithm provides better performance in terms of PSNR. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Approximation algorithms

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Simplification and Compression Method Based on Classified Sparse Sampling for LiDAR Point Cloud 23

Simplification and Compression Method Based on Classified Sp...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Jian Lin, YuFang Chen, YuanXiang Zheng, MingKui Zhu, YuYao School of Advanced Manufacturing Fuzhou University Quanzhou China College of Physics and Information Engineering Fuzhou University Fuzhou China Beijing Ctr. Fujian Br. Patent Examin. Cooperation Center of the State Intellectual Property Office Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9781665494571

As a key component of point cloud acquisition technology, LiDAR (Light Detection and Ranging) acts as an eye in autonomous driving. The vehicle-mounted LiDAR point cloud system is the prerequisite foundation for the vehicle to be able to perform environmental perception, path planning, navigation, and other behaviors. In practical applications, LiDAR point clouds have a wide distribution range and a large amount of data, which poses major challenges to existing transmission and storage technologies. Aiming at the point cloud from automotive LiDAR in autonomous driving, we propose a point cloud lossy compression framework based on range image index and classified sparse sampling. This paper takes the original data packet as the compression target and converts it into a 2D range image and a 3D point cloud based on its data characteristics. On this basis, we can segment 3D point clouds into ground point and non-ground point cloud by applying progressive morphological filters. Then, we perform de-redundant sparse processing at different intensities on the two. A sparse 2D range image is generated by referring to the range image index of the downsampling 3D point cloud and is represented as a more compact form of one-dimensional range vector combined with its corresponding occupancy image and Morton sorting. Finally, the image coding method is used to further compress the point cloud. Experimental results show that our proposed method is better than the existing algorithms, thereby effectively reducing the spatial redundancy of the point cloud and achieving higher reconstruction quality. © 2021 IEEE.

关键词： Point cloud compression image coding Laser radar Three-dimensional displays Smart cities Redundancy Prediction algorithms

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Channel Estimation for Underwater Acoustic OFDM Communications: An image Super-Resolution Approach

Channel Estimation for Underwater Acoustic OFDM Communicatio...

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IEEE international conference on Communications (ICC)

作者： Ouyang, Donghong Li, Yuzhou Wang, Zhizhan Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

ISBN: (纸本)9781728171227

In this paper, by exploiting the powerful ability of deep learning, we devote to designing a well-performing and pilot-saving neural network for the channel estimation in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. By considering the channel estimation problem as a matrix completion problem, we interestingly find it mathematically equivalent to the image super-resolution problem arising in the field of image processing. Hence, we attempt to make use of the very deep super-resolution neural network (VDSR), one of the most typical neural networks to solve the image super-resolution problem, to handle our problem. However, there still exist significant differences between these two problems, we thus elegantly modify the basic framework of the VDSR to design our channel estimation neural network, referred to as the channel super-resolution neural network (CSRNet). Moreover, instead of training an individual network for each considered signal-to-noise ratio (SNR), we obtain an unified network that works well for all SNRs with the help of transfer learning, thus substantially increasing the practicality of the CSRNet. Simulation results validate the superiority of the CSRNet against the existing least square (LS) and deep neural network (DNN) based algorithms in terms of the mean square error (MSE) and the bit error rate (BER). Specifically, compared with the LS algorithm, the CSRNet can reduce the BER by 44.74% even using 50% fewer pilots.

关键词： Deep learning OFDM Simulation Superresolution Neural networks Bit error rate Transfer learning

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Story Generation from images Using Deep Learning 6th

Story Generation from Images Using Deep Learning

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6th international conference on Information, Communication and Computing Technology (ICICCT)

作者： Alnami, Abrar Almasre, Miada Al-Malki, Norah King Abdulaziz Univ Jeddah Saudi Arabia

ISBN: (纸本)9783030883782;9783030883775

Recently, the problem of creating descriptive captions for images became a significant one. However, human languages' expressivity had been among the challenges that hindered researchers from widely experimenting with creating linguistically rich captions for images. That motivated us to utilize advanced deep learning algorithms to generate captions for images. The researchers proposed an AI model utilizing deep learning and natural language processing algorithms, which has two main components, an image-feature extractor, and a story generator. The researchers trained the first component (image-feature extractor) of the model to predict object names in images. The second component (story-generator) was trained on a custom short descriptive sentence which considered short stories. So, the output from the first component (list of words) will be entered into the second component to generate stories on input images. Thus, when testing the model's performance, a list of names will be entered from the first component so that the second generator arranges them and generates a short story from them. The proposed model developed could generate a short story expressive of an input image as shown by the results of a logical value used on the BLEU scale of 0.59, which further research is planned to improve.

关键词： Convolutional neural network Deep learning Object detection image captioning Long short-term memory Neural networks

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