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检索条件"任意字段=16th Image Processing: Algorithms and Systems Symposium 2018"

共 650 条记录，以下是71-80 订阅

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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 Optimizing Multilevel thresholding for image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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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： Dao, thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-the Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

this paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). the experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： image segmentation

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Putting Table Cartograms into Practice 16th

Putting Table Cartograms into Practice

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16th International symposium on Visual Computing (ISVC)

作者： Hasan, Mohammad Rakib Mondal, Debajyoti Tasnim, Jarin Schneider, Kevin A. Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9783030904395;9783030904388

Given an mx n table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m x n convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell's weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.

关键词： Cartography algorithms Optimization image processing

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Machine Learning-based Predictive Channel Modeling for 6G Wireless Communications Using image Semantic Segmentation

Machine Learning-based Predictive Channel Modeling for 6G Wi...

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IEEE International symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tong Wu Cheng-Xiang Wang Junling Li Chen Huang National Mobile Communications Research Laboratory School of Information of Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing China

the research on 6G wireless communications has become a focal point in the global technological competition. For 6G wireless systems, channel modeling is the foundation for the design and deployment of wireless communication systems. With unexplored channel characteristics expected to emerge from new frequency bands and scenarios in 6G wireless channels, predictive channel modeling becomes particularly important. this paper proposes an image-based real-time predictive channel modeling using image processing and machine learning (ML) algorithms, which extracts effective channel information from images and has been validated through channel measurement data and synthetic channel data. the proposed channel model considers large-scale identification and small-scale feature extraction. the former aspect is accomplished by training DeepLabv3+ on our image dataset and classifying the scenarios. For feature extraction, we use VGG-16 as the backbone network and validate it with the 3GPP Uma path loss model for comparison. the results show that predictive channel modeling using segmented images is superior to that of the 3GPP Uma path loss model. Even more, it performs better than the original image-based prediction.

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Uncooperative Satellite 6D Pose Estimation with Relative Depth Information 16th

Uncooperative Satellite 6D Pose Estimation with Relative Dep...

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16th International symposium on Visual Computing (ISVC)

作者： Song, Jingrui Hao, Shuling Xu, Kefeng Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9783030904364;9783030904357

the pose estimation of satellites or non-cooperative space targets is of great significance for evaluating their operating conditions, threatness and so on. With the development of ground-based Adaptive Optics (AO) systems, the imaging quality of optical systems for space targets has been greatly improved. We can estimate the space target pose in these AO images with computer vision algorithms. However, the image degradation during the imaging process of AO systems complicates the problem. this paper takes the Tiangong-1 satellite as an example to solve the simulation problem of ground-based optical imaging and the single-shot 6D pose estimation problem. We first propose a satellite image rendering pipeline based on the influence of atmospheric turbulence, built on Tiangong-1, which is used to generate spacecraft pose annotated data. through this method, we obtain image data similar to the real image, that can be used to train and evaluate neural networks. Secondly, we propose a deep learning framework based on YOLO-6D, providing fast and accurate pose estimation. In particular, we introduce a relative depth prediction branch, which predicts the relative depth while estimating the satellite pose. Preliminary experimental results indicate that this method is suitable for the Tiangong-1 dataset and has achieved good performance.

关键词： Uncooperative satellites image simulation 6D pose estimation Convolutional neural networks Depth information

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Mechanisms For Using image Properties And Neural Networks In Identification Of Micro-Objects

Mechanisms For Using Image Properties And Neural Networks In...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Isroil I Jumanov Rustam A Safarov Olimjon I Djumanov Applied Mathimatics and Informatics Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9781665451635

the problem of visualization, recognition, classification of images of micro-objects, in particular, pollen grains, unicellular organisms, fingerprints based on the definition of their variety, belonging to a class, the use of information of geometric shapes, morphology, dynamic, specific characteristics, unique features of neural networks has been investigated, in control systems of industrial and technological complexes, environmental monitoring, ecology, and medical diagnoses. Methods, learning algorithms, component computational schemes of neural networks have been developed, which provide the best quality of image identification in conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing. Mathematical expressions are obtained for estimating identification errors associated with information distortions at the measurement, input, and transmission stages due to nonstationarity, the inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of pollen grains has been built and implemented, which includes algorithms for a three-layer, loosely coupled neural network, Hopfield’s network, bidirectional associative memory, Kohonen. Results are obtained for correct, incorrect recognition, and rejected pollen samples based on with-teacher and unsupervised learning algorithms, which are synthesized with cubic, biquadratic, and interpolation spline functions.

关键词： Wavelet transforms Interpolation image recognition Software algorithms Artificial neural networks Fingerprint recognition Wavelet analysis

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Determining Optimal Frame processing Strategies for Real-Time Document Recognition systems 16th

Determining Optimal Frame Processing Strategies for Real-Tim...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Bulatov, Konstantin Arlazarov, Vladimir V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia Smart Engines Serv LLC Moscow Russia

ISBN: (纸本)9783030863319

Mobile document analysis technologies became widespread and important, and growing reliance on the performance of critical processes, such as identity document data extraction and verification, lead to increasing speed and accuracy requirements. Camera-based documents recognition on mobile devices using video stream allows to achieve higher accuracy, however in real time systems the actual time of individual image processing needs to be taken into account, which it rarely is in the works on this subject. In this paper, a model of real-time document recognition system is described, and three frame processing strategies are evaluated, each consisting of a per-frame recognition results combination method and a dynamic stopping rule. the experimental evaluation shows that while full combination of all input results is preferable if the frame recognition time is comparable with frame acquisition time, the selection of one best frame based on an input quality predictor, or a combination of several best frames, with the corresponding stopping rule, allows to achieve higher mean recognition results accuracy if the cost of recognizing a frame is significantly higher than skipping it.

关键词： Mobile recognition Real-time recognition Video stream Anytime algorithms Document recognition

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Hierarchical DNN-Based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

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International IEEE Conference on Signal-image Technologies and Internet-Based System

作者： Cheng-Hsuan Yu Jian-Jiun Ding Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both large- scaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for over-segmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： Deep learning Training image segmentation Histograms image color analysis Shape Object oriented modeling

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Vibration-Based Status Identification of Power Transmission Poles 9

Vibration-Based Status Identification of Power Transmission ...

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9th IFAC symposium on Mechatronic systems (MECHATRONICS) / 16th International Conference on Motion and Vibration Control (MoViC)

作者： Aoki, Emi do Cabo, Celso T. Mao, Zhu Ding, Yangmin Ozharar, Sarper Univ Massachusetts Lowell Lowell MA 01854 USA Worcester Polytech Inst Worcester MA 01609 USA NEC Labs Amer Princeton NJ 08540 USA

Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging task and knowing the operating status and its structural integrity drastically speeds up the routine inspection. Applying a data-driven approach using accelerometer data to analyze the power line-induced vibration to classify different poles within different operational conditions is proposed. Feature creation is the important aspect to improve an accuracy of data-driven algorithms. For this purpose, a time-frequency domain classifier is developed, based on the data collected from two tri-axial accelerometers installed on the wood poles before and after streetlights are on. Data are explored both in time and frequency domain using techniques such as data augmentation and segmentation, averaging, filtering, and principal component analysis. Results of the machine learning classifier clearly shows distinct characteristics among the data collected from different work conditions and different poles. Further exploration of the applied algorithm will be pursued to construct more sophisticated features based on supervised learning to enhance the identification accuracy. Copyright (C) 2022 the Authors.

关键词： Structural Health Monitoring Vibration Power Transmission Line Utility Pole Signal processing Principal Component Analysis Data Clustering

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IVS3D: An Open Source Framework for Intelligent Video Sampling and Preprocessing to Facilitate 3D Reconstruction 16th

IVS3D: An Open Source Framework for Intelligent Video Sampli...

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16th International symposium on Visual Computing (ISVC)

作者： Hermann, Max Pollok, thomas Brommer, Daniel Zahn, Dominic Fraunhofer Inst Optron Syst Technol & Image Explo Video Exploitat Syst Karlsruhe Germany Karlsruhe Inst Technol Inst Photogrammetry & Remote Sensing Karlsruhe Germany

ISBN: (纸本)9783030904395;9783030904388

the creation of detailed 3D models is relevant for a wide range of applications such as navigation in three-dimensional space, construction planning or disaster assessment. However, the complex processing and long execution time for detailed 3D reconstructions require the original database to be reduced in order to obtain a result in reasonable time. In this paper we therefore present our framework iVS3D for intelligent pre-processing of image sequences. Our software is able to down sample entire videos to a specific frame rate, as well as to resize and crop the individual images. Furthermore, thanks to our modular architecture, it is easy to develop and integrate plugins with additional algorithms. We provide three plugins as baseline methods that enable an intelligent selection of suitable images and can enrich them with additional information. To filter out images affected by motion blur, we developed a plugin that detects these frames and also searches the spatial neighbourhood for suitable images as replacements. the second plugin uses optical flow to detect redundant images caused by a temporarily stationary camera. In our experiments, we show how this approach leads to a more balanced image sampling if the camera speed varies, and that excluding such redundant images leads to a time saving of 8.1% for our sequences. A third plugin makes it possible to exclude challenging image regions from the 3D reconstruction by performing semantic segmentation. As we think that the community can greatly benefit from such an approach, we will publish our framework and the developed plugins open source using the MIT licence to allow co-development and easy extension.

关键词： 3D reconstruction Preprocessing Video sampling Open source framework

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