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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是41-50 订阅

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New Scheme of image Matching for the Power Transmission Line Icing

New Scheme of Image Matching for The Power Transmission Line...

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Nalini Rizkyta Nusantika Jin Xiao Xiaoguang Hu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing P.R China

ISBN: (纸本)9781665409858

Line icing disaster on power transmission is a significant problem that causes severe problems in system security. therefore, a reliable system is needed to monitor the Power Transmission Line Icing (PTLI). Nowadays, binocular cameras mounted on Unmanned Aerial vehicles (UAVs) have been widely used to observe cable conditions during icing. Existing three-dimensional measurement methods based on binocular stereo vision were recently introduced to measure the ice thickness in PTLI. An essential step in three-dimensional measurement is image matching. image matching has been widely used with various methods, such as SIFT, SURF, and ORB. Compared with SIFT and SURF algorithms, ORB has absolute advantages in computational speed. However, conventional ORB still has some drawbacks when processing images that have a complex background. So this paper proposes a new efficient scheme to reduce errors and save time. Our approach achieves a 90% accuracy of the average distance between the two correspondence points in the left and right image. the standard deviation error value is smaller than conventional ORB, and the number of points matching errors is less than conventional ORB, even with almost no error matching on the tested images. the average processing time is ten times faster than conventional ORB. the simulation results show that the new scheme is effective for image matching on power transmission line icing.

关键词： Power transmission lines image matching Simulation Power transmission Transmission line measurements Stereo vision Security

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Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm 18

Hyperspectral complex-domain image denoising: cube complex-d...

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18th image processing: algorithms and systems conference, ipas 2020

作者： Katkovnik, Vladimir Ponomarenko, Mykola Egiazarian, Karen Shevkunov, Igor Kocsis, Peter Tampere University TampereFIN 33101 Finland

We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. the search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design of adaptive nonlocal bases. Simulation experiments demonstrate high efficiency of developed state-of-the-art algorithms. © 2020, Society for Imaging Science and Technology

关键词： Hyperspectral imaging

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Application of Blind Signal processing in image Watermarking systems 17

Application of Blind Signal Processing in Image Watermarking...

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17th International SoC Design conference (ISOCC)

作者： Chen, Chuen-Yau Lin, Cheng-Yuan Zou, Yi-Ze Hsiao, Hung-Ming Chen, Yen-Ting Natl Univ Kaohsiung Dept Elect Engn Kaohsiung 811 Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan

ISBN: (纸本)9781728183312

In this paper, an efficient blind signal processing algorithm called relative-gradient bound component analysis algorithm that combines the concepts of the relative-gradient approach and the bound-component analysis is applied to the image watermarking systems. Unlike to the other blind-signal-processing algorithms, neither the inverse matrix nor the covariance matrix is necessary to be computed in this one. It can successfully extract the embedded watermark without the whittling process. the time complexity and the space complexity of this algorithm are both lower than those of the original bound component analysis algorithm.

关键词： blind signal bound component analysis

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Automated Summary Scoring with ReaderBench 17th

Automated Summary Scoring with ReaderBench

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17th International conference on Intelligent Tutoring systems (ITS)

作者： Botarleanu, Robert-Mihai Dascalu, Mihai Allen, Laura K. Crossley, Scott Andrew McNamara, Danielle S. Univ Politehn Bucuresti 313 Splaiul Independentei Bucharest 060042 Romania Acad Romanian Scientists Str Ilfov 3 Bucharest 050044 Romania Univ New Hampshire Durham NH 03824 USA Georgia State Univ Dept Appl Linguist ESL Atlanta GA 30303 USA Arizona State Univ Dept Psychol POB 871104 Tempe AZ 85287 USA

ISBN: (纸本)9783030804213;9783030804206

Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. this study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale. Machine learning models lever-aging Natural Language processing (NLP) techniques were trained to predict the extent to which summaries capture the main idea of the target text. the NLP models combined both domain and language independent textual complexity indices from the ReaderBench framework, as well as state-of-the-art language models and deep learning architectures to provide semantic contextualization. the models achieve low errors - normalized MAE ranging from 0.13-0.17 with corresponding R-2 values of up to 0.46. Our approach consistently outperforms baselines that use TF-IDF vectors and linear models, as well as Transfomer-based regression using BERT. these results indicate that NLP algorithms that combine linguistic and semantic indices are accurate and robust, while ensuring generalizability to a wide array of topics.

关键词： Natural language processing Text summarization Automated scoring

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An Evaluation on Effectiveness of Deep Learning in Detecting Small Object Within a Large image 1

引用

17th International conference on Biomedical Engineering, ICBME 2019

作者： Hassan, Nazirah Ming, Kong Wai Wah, Choo Keng School of Engineering Nanyang Polytechnic Nanyang Singapore

ISBN: (数字)9783030620455

ISBN: (纸本)9783030620448

Multiple Deep Learning (DL) algorithms have been developed recently and are shown to be achieving very high accuracy in object detection. However, challenges have been reported in detecting small objects within a large image (e.g. > 2000 by 2000 in resolution). Various methods have been proposed using different detection algorithms in order to detect small objects. However, these approaches require high computational resources and are not suitable for edge computing devices that are used for practical applications such as pedestrian traffic light detection. We explored two different methods of detection to evaluate which method is best at detecting small objects. the first method is a two—part procedure with the first step being image processing and the second step, a R-CNN based detection using Edge Boxes algorithm for the extraction of region proposals. the second method is solely Faster R-CNN Object Detection with Instance Segmentation, termed as Mask R-CNN. A total of 4000 streets images of Singapore with pedestrian traffic lights were used as training data. the dimensions of the images range from 1200 by 900 to 4000 by 3000. the small object to be detected is the green or red man within pedestrian traffic lights. We evaluated these methods based on training time required, detection time, accuracy as well as suitability for deployment in edge computing devices. From the results, it is shown that the HSV + R-CNN approach is preferred as it achieves an accuracy of 95.5% and can be deployed in edge devices. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Natural Language processing and Deep Learning for Blended Learning as an Aspect of Computational Linguistics 17th

Natural Language Processing and Deep Learning for Blended Le...

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17th Annual conference of the International-Association-of-Online-Engineering (IAOE) and the Global-Online-Laboratory-Consortium (GOLC) on Remote Engineering and Virtual Instrumentation (REV) - Cross Reality and Data Science in Engineering

作者： Pikhart, Marcel Univ Hradec Kralove Fac Informat & Management Hradec Kralove Czech Republic

ISBN: (纸本)9783030525750;9783030525743

Machine learning has been used for several years already in information science to create algorithms which enable computers to solve problems without giving them particular tasks and instructions to perform these tasks. this paper attempts to concentrate on possibilities of this AI (artificial intelligence) subfield inasmuch it could prove helpful for blended learning. It brings possible questions which are connected to e-learning, blended learning and machine learning and its utilization in university courses. It also brings several questions of computational linguistics which could prove extremely helpful in blended learning processes in which it could bring big data analysis. these phenomena are relatively new, therefore, neglected by blended learning scholars, thus this paper brings these new ideas together and wants to present new ideas and concepts which will be utilized in blended learning area. It also suggests new approach to blended learning, coined by the term blended learning 2.0, which implements modern approaches such as computational linguistics and corpus linguistics into the utilization of e-platforms in the educational process.

关键词： Blended learning E-learning Computational linguistics Corpus linguistics Natural language processing Deep learning

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Asteroid image Classification Using Convolutional Neural Networks 17

Asteroid Image Classification Using Convolutional Neural Net...

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17th IEEE International conference on Intelligent Computer Communication and processing, ICCP 2021

作者： Rosu, Cosmin Bacu, Victor Adobe Systems Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9781665409766

An impact of an asteroid on planet Earth could have catastrophic consequences. To eliminate this threat, the first step that needs to be done is to identify in advance all the asteroids near Earth. In this paper, we present a convolutional neural network model which is able to classify astronomical images according to the presence of asteroids. the model is trained with real data from Isaac Newton Telescope (INT) located in La Palma, Canary Islands, Spain. the goal of this work is to create an automated system for asteroids detection and to reduce as much as possible the false-negative rate. We show that the model reaches a 94% recall when it is trained with individual images. © 2021 IEEE.

关键词： Convolutional neural networks

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High-Dimensional Similarity Query processing for Data Science 21

High-Dimensional Similarity Query Processing for Data Scienc...

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27th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD)

作者： Qin, Jianbin Wang, Wei Xiao, Chuan Zhang, Ying Wang, Yaoshu Shenzhen Univ Shenzhen Guangdong Peoples R China Shenzhen Inst Comp Sci Shenzhen Guangdong Peoples R China Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China Osaka Univ Suita Osaka Japan Nagoya Univ Suita Osaka Japan Univ Technol Sydney Ultimo NSW Australia

ISBN: (纸本)9781450383325

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and recommender systems). Recently, representation learning and auto-encoding methods as well as pre-trained models have gained popularity. they basically deal with dense high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. this tutorial aims to provide a comprehensive review of high-dimensional similarity query processing for data science. It introduces solutions from a variety of research communities, including data mining (DM), database (DB), machine learning (ML), computer vision (CV), natural language processing (NLP), and theoretical computer science (TCS), thereby highlighting the interplay between modern computer science and artificial intelligence technologies. We first discuss the importance of high-dimensional similarity query processing in data science applications, and then review query processing algorithms such as cover tree, locality sensitive hashing, product quantization, proximity graphs, as well as recent advancements such as learned indexes. We analyze their strengths and weaknesses and discuss the selection of algorithms in various application scenarios. Moreover, we consider the selectivity estimation of high-dimensional similarity queries, and show how researchers are bringing in state-of-the-art ML techniques to address this problem. We expect that this tutorial will provide an impetus towards new technologies for data science.

关键词： similarity query processing nearest neighbor similarity search high-dimensional data

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Edge detection in images using energy characteristics

Edge detection in images using energy characteristics

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17th International Scientific and Technical conference on Optical Technologies for Telecommuncations

作者： Lyasheva, S. A. Shleymovich, M. P. Kazan Natl Res Tech Univ Kazan Russia

ISBN: (纸本)9781510638402;9781510638396

the paper considers a new approach to image edges detecting. It is based on the weight image model application. In this model, image pixels are evaluated in terms of their importance for perception. For this we use energy signs, the calculation of which is based on the wavelet transform. the proposed approach allows you to create an effective edge detector for use in various computer vision systems, for example, in aviation systems, traffic control systems, road surface monitoring systems, biometric identification systems, etc.

关键词： computer vision image processing weight model energy characteristics edge detector

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Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

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17th International conference on Intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. the results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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