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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 394 条记录，以下是111-120 订阅

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Deep Residual Network with Multi-Image Attention for Imputing Under Clouds in Satellite Imagery 26

Deep Residual Network with Multi-Image Attention for Imputin...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Yang, Xian Zhao, Yifan Vatsavai, Ranga Raju North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA BReLL Lirio Knoxville TN USA North Carolina State Univ CGA CSC Raleigh NC 27695 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Earth observations from remote sensing imagery play an important role in many environmental applications ranging from natural resource (e.g., crops, forests) monitoring to man-made object (e.g., builds, factories) recognition. Most widely used optical remote sensing data however is often contaminated by clouds making it hard to identify the objects underneath. Fortunately, with the recent advances and increased operational satellites, the spatial and temporal density of image collections have significantly increased. In this paper, we present a novel deep learning-based imputation technique for inferring spectral values under the clouds using nearby cloud-free satellite image observations. the proposed deep learning architecture, extended contextual attention (ECA), exploits similar properties from the cloud-free areas to tackle clouds of different sizes occurring at arbitrary locations in the image. A contextual attention mechanism is incorporated to utilize the useful cloud-free information from multiple images. To maximize the imputation performance of the model on the cloudy patches instead of the entire image, a two-phase custom loss function is deployed to guide the model. To study the performance of our model, we trained our model on a benchmark Sentinel-2 dataset by superimposing real-world cloud patterns. Extensive experiments and comparisons against the state-of-the-art methods using pixel-wise and structural metrics show the improved performance of our model. Our experiments demonstrated that the ECA method is consistently better than all other methods, it is 28.4% better on MSE and 31.7% better on cloudy MSE as compared to the state-of-the-art EDSR network.

关键词： Earth Deep learning Cloud computing Satellites Clouds Benchmark testing Data models

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Cross-Guided Feature Fusion with Intra-Modality Reweighting for Multi-Spectral Pedestrian Detection 26

Cross-Guided Feature Fusion with Intra-Modality Reweighting ...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Deng, Jian Li, Mingyue Chen, Yongxin Shao, Zhenzhou Qu, Ying Guan, Yong Zhang, Jun Shi, Zhiping Capital Normal Univ Coll Informat Engn Beijing Peoples R China Beijing Key Lab Light Ind Robot & Safety Verifica Beijing Peoples R China Beijing Adv Innovat Ctr Imaging Technol Beijing Peoples R China Capital Normal Univ Sch Math Sci Beijing Peoples R China Univ Tennessee Engn Coll Knoxville TN USA Beijing Smartchip Microelect Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-spectral pedestrian detection has gained extensive attention over the past decade. To alleviate the problem of modality imbalance in the multi-spectral tasks, a novel cross-guided feature fusion network based on the auto-encoder framework is proposed using RGB-thermal image pairs as inputs. To obtain the complementary features, a cross-guided loss is designed, so that the output images are balanced with both modalities in an unsupervised manner. An intra-modality reweighting module is implemented to filter the redundant features before the fusion. Finally, YOLOv3 is chosen as the detector fed by the fused features. the proposed method is verified using the public KAIST and VOT-RGBT datasets. Experimental results demonstrate that the proposed method can outperform the state-of-the-art methods, the miss rate of pedestrian detection reaches 48.57% and 4.52% using KAIST and VOT-RGBT datasets, respectively.

关键词： Redundancy Detectors Feature extraction pattern recognition Task analysis

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Adversarial Attack on Semantic Segmentation Preprocessed with Super Resolution 26

Adversarial Attack on Semantic Segmentation Preprocessed wit...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Lim, Gyeongsup Kim, Minjae Hur, Junbeom Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Computer vision tasks, such as image classification, semantic segmentation, and super resolution, are broadly utilized in many applications. Recent studies revealed that machine learning-based models for the computer vision tasks are vulnerable to adversarial attacks. Since the adversarial attack can disturb the computer vision models in real-world systems, many countermeasures have been proposed against the adversarial attacks, such as denoising, resizing, and machine learning-based super resolution model as a preprocessing. Recently, a prior work demonstrated that the super resolution model as a preprocessing can be vulnerable to the adversarial attack targeted to the preprocessing itself, only when the perturbation is inactive before the preprocessing. However, we also found that the perturbation before the preprocessing can be another serious threat if the super resolution model is used for a mitigation of adversarial attacks. In this paper, we propose Layered Adversary Generation (LAG) that generates the adversarial example by recursively injecting noises to clean image in white-box environment. We then show that LAG is effective to attack a semantic segmentation model even if the super resolution models with/without two countermeasures as auxiliary methods such as resizing and denoising are adopted to mitigate the adversarial attacks. Furthermore, we demonstrate that LAG is transferable across other super resolution models. Lastly, we discuss our attack method in gray-box and black-box environments, and suggests a mitigation for robust preprocessing.

关键词： Computer vision Computational modeling Semantic segmentation Perturbation methods Superresolution Noise reduction pattern recognition

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A Unified Framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization 26

A Unified Framework for Discrete Multi-kernel k-means with K...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Lu, Yihang Zheng, Xuan Wang, Rong Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multiple kernel clustering seeks to combine several kernels for boosting the clustering performance. However, most existing MKKM methods fail to evaluate kernel correlation adequately, which may inevitably select highly correlated kernels resulting in kernel redundancy. Besides, most existing methods solve the NP-hard cluster labels assignment task in two stages: first learning the relaxed labels with continuous values and then obtaining the discrete labels via other discretization methods like k-means. this two-stage strategy may result in the loss of information owing to the deviation between the genuine solution and the approximated one. In this work, we present a unified framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization (DMK-KDR). It is capable of penalizing highly correlated kernels through a well-designed matrix-induced regularization, thus allowing for improved diversity and reduced redundancy in kernel fusion. Additionally, it learns both discrete and continuous clustering indicator matrices simultaneously, thereby ensuring the integrity of the discrete solution without over-reliance on k-means or the loss of information. the efficacy of our model has been evaluated in a number of experiments using real-world datasets.

关键词： Correlation Diversity reception Redundancy Boosting pattern recognition Kernel Task analysis

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pattern recognition in Non-Stationary Environmental Time Series Using Sparse Regression 8

Pattern Recognition in Non-Stationary Environmental Time Ser...

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8th international Young Scientists conference on Computational Science (YSC)

作者： Deeva, Irina Nikitin, Nikolay O. Kaluyzhnaya, Anna, V ITMO Univ 49 Kronverksky Pr St Petersburg 197101 Russia

the various real-world tasks of environmental management make it necessary to obtain the hindcasts and forecasts of natural events (wind, ocean waves and currents, sea ice, etc.) using data-driven techniques for metocean processes simulation. the models can be fitted to specific fragments of the non-stationary multivariate time series individually to reproduce metocean environment with desired characteristics. In the paper, the approach based on the LASSO regularised regression is proposed for the environmental time series clustering. It allows the identify the situations with specific interaction between variables, that can be interpreted by the values regression coefficients. the weather generator was used to produce both synthetic time series similar to the general dataset and the identified clusters. the obtained results can be used to increase the quality of the computationally lightweight environmental models identification and interpretation. (c) 2019 the Authors. Published by Elsevier Ltd. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 8th international Young Scientist conference on Computational Science.

关键词： data-driven models metocean simulation time series clustering synthetic data pattern mining

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Simple Yet Effective Approach to Repetitive Behavior Classification based on Siamese Network 26

Simple Yet Effective Approach to Repetitive Behavior Classif...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Yoo, Cheol-Hwan Yoo, Jang-Hee Kim, Ho-Won Han, ByungOk Elect & Telecommun Res Inst ETRI Artificial Intelligence Res Lab Daejeon South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Conventional studies dealing with repetition detection have mainly focused on tasks of temporal localization or counting the number of repetitions in videos. However, direct discrimination between repetitive and non-repetitive behaviors in videos, called repetitive behavior classification (RBC), has attracted less attention despite its great potential and advantages of: 1) filling the demands in the fields such as classification of repetitive behaviors in children with autism spectrum disorder (ASD) and helping to alleviate manual and time-consuming diagnostic procedures, 2) directly learning representation of differences between repetition and non-repetition patterns along the temporal dimension, and 3) being an effective alternative to the existing repetition counting and temporal segmentation tasks that are struggling with insufficient data and laborious manual annotation effort. In this paper, to the best of our knowledge, we firstly cast the problem of the RBC using deep learning frameworks. For this, we propose a simple yet effective add-on network, SiRepNet, that exploits the Siamese network structure to learn the inherent properties of repetitive behaviors. We also composed the RBC dataset by re-purposing and re-organizing Kinetics and Countix datasets for training our method. To validate our ideas, we carried out extensive experiments on RBC datasets, which showed performance improvement over state-of-the-art video classification algorithms by simply attaching our scheme to them for the RBC task.

关键词： Training Location awareness Smoothing methods Manuals Behavioral sciences Classification algorithms pattern recognition

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Optical Music recognition as the Case of Imbalanced pattern recognition: A Study of Single Classifiers 8th

Optical Music Recognition as the Case of Imbalanced Pattern ...

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8th international conference on Knowledge, Information, and Creativity Support Systems (KICSS)

作者： Jastrzebska, Agnieszka Lesinski, Wojciech Warsaw Univ Technol Fac Math & Informat Sci Plac Politech 1 PL-00660 Warsaw Poland Univ Bialystok Fac Math & Comp Sci Ul Sosnowa 64 PL-15887 Bialystok Poland

ISBN: (纸本)9783319190907;9783319190891

the article is focused on a particular aspect of classification, namely the imbalance of recognized classes. the paper contains a comparative study of results of musical symbols classification using known algorithms: k-nearest neighbors, k-means, Mahalanobis minimal distance, and decision trees. Authors aim at addressing the problem of imbalanced pattern recognition. First, we theoretically analyze difficulties entailed in the classification of music notation symbols. Second, in the enclosed case study we investigate the fitness of named single classifiers on real data. Conducted experiments are based on own implementations of named algorithms with all necessary image processing tasks. Results are highly satisfying.

关键词： pattern recognition Classification Imbalanced data

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Proceedings of the 8th international conference on Computer recognition Systems CORES 2013 1

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丛书名： Advances in Intelligent Systems and computing

1000年

作者： Robert Burduk Konrad Jackowski Marek Kurzynski Michał Wozniak Andrzej Zolnierek

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Artificial Intelligence for the Classification of Neuromuscular Diseases Using Dominant MUAP 8

Artificial Intelligence for the Classification of Neuromuscu...

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8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Maheshwary, Priti Vinu, W. Velvadivu, P. Shukla, Surendra Kumar Srivastava, Prabhat Kumar Pareek, Prakash Centre for Internet of Things and Advance Computing Rabindranath Tagore University Bhopal India Annamalai University Department of Physical Education Tamilnadu Chidambaram India Coimbatore Institute of Technology Department of Computing-Data Science Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Quantum University Department of Computer Science and Engineering Uttrakhand Roorkee India Vishnu Institute of Technology Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India

ISBN: (纸本)9781665497619

Motor-unit-action-potentials in an electromyographic isolated signals shape & sounds are valuable diagnosis information for neuro-muscular disorders-treatment & management. An expert can analyse these parameters qualitatively or quantitative by pattern recognition techniques. Because of benefits of the quantitatively method of EMG, producing robust automated MUAP types is investigated, & many systems is produced for this purpose, but correctness of the previous methods isn't peak enough to use in clinical settings. the developed system uses an EMG signal decomposition mechanism to extract both time & frequency domain for M-U-A-P's retrieved from E-M-G. Un-similar type of algorithms was studied, including 1 & many types with many sub-sets characteristics. the multi-classifier methods proposed here performed well in experiments by real set EMG. the multi-classifier, which aggregates base classifiers using multiple feature sets & both trainable & non-trainable fusion techniques, performed the best of the methods studied, with an average precision is 98%. © 2022 IEEE.

关键词： Support vector machines Time-frequency analysis Shape Neuromuscular Morphology Electromyography pattern recognition

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Local Binary patterning Approach for Movement Related Potentials based Brain Computer Interface 8

Local Binary Patterning Approach for Movement Related Potent...

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8th IEEE international Workshop on Advances in Sensors and Interfaces (IWASI)

作者： Mezzina, Giovanni De Venuto, Daniela Politecn Bari Dept Elect & Informat Engn Via E Orabona 4 I-70125 Bari Italy

ISBN: (纸本)9781728105567

this paper proposes the design and the implementation of an innovative algorithm for a 2-choices synchronous Brain-Computer Interface (BCI). the proposed BCI operates on signals from eight EEG channels evenly distributed along the sensorimotor area. the acquired EEGs are then analyzed by using a symbolization method. Typically, the symbolization includes data-analysis algorithms that translate physical processes from experimental measurements into a series of discrete symbols (e.g., bit strings). For the BCI application, the chosen symbolization algorithm is the Local Binary pattern (LBP). Since the selected LBP method uses binary operations for the whole processing chain (end-to-end), the complexity and the computing timing of the features extraction (FE) and real-time classification stages have been strongly reduced. Finally, a time-continuous Support Vector Machine (tcSVM) classifies the LBP-extracted features. the here proposed BCI algorithm has been validated on 3 subjects (aged 26 +/- 1), who underwent a stimulation protocol oriented to Movement-Related Potentials (MRPs) elicitation. the in-vivo validation showed how the system is able to reach an intention recognition accuracy of 85.61 +/- 1.19 %. In addition, starting from the complete data storage, the whole implemented computing chain asks, on average, for just similar to 3ms to provide the classification. As a proof of concept, the tcSVM outcomes have been used to drive, via Bluetooth, a 3D printed robotic hand.

关键词： Brain Computer Interface EEG Local Binary pattern Symbolization Robotic Hand

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