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检索条件"任意字段=5th IAPR Workshop on Pattern Recognition in Remote Sensing"

共 156 条记录，以下是11-20 订阅

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the First Challenge on Moving Object Detection and Tracking in Satellite Videos: Methods and Results 26

The First Challenge on Moving Object Detection and Tracking ...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Guo, Yulan Yin, Qian Hu, Qingyong Zhang, Feng Xiao, Chao Zhang, Ye Wang, Hanyun Dai, Chenguang Yang, Jian Zhou, Zhuang Guo, Weilong Qi, Xiyu Tu, Kelong Xu, Cong Zhu, Shudan Chen, Lai Lin, Bin Xue, Chaocan Zheng, Jinlei Qin, Limei Li, Ying Zhao, Manqi Ruan, Lu Cui, Mingpeng Ding, Guanchen Jiang, Guangwei Chen, Zhenzhong Sun, Yuhan Cao, Kaiyang Kong, Lingyu Chen, Shaodong Zhao, Zhicheng Shen, Qin Liu, Lei Li, Chenglong Xiao, Yun Natl Univ Def Technol Changsha Peoples R China Univ Oxford Oxford England Informat Engn Univ Zhengzhou Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing Peoples R China Chinese Acad Sci Key Lab Space Utilizat Beijing Peoples R China China Univ Geosci Wuhan Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China Guilin Univ Technol Guilin Peoples R China Northwestern Polytech Univ Xian Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Wuhan Univ Wuhan Peoples R China Anhui Univ Hefei Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this paper, we briefly summarize the first challenge on moving object detection and tracking in satellite videos (SatVideoDT). this challenge has three tracks related to satellite video analysis, including moving object detection (Track 1), single object tracking (Track 2), and multiple-object tracking (Track 3). 123, 89, and 70 participants successfully registered, while 37, 42, and 29 teams submitted their final results on the test datasets for Tracks 1-3, respectively. the top-performing methods and their results in each track are described with details. this challenge establishes a new benchmark for satellite video analysis.

关键词： Satellites Object detection Benchmark testing pattern recognition Object tracking Videos

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Critical Radical Analysis Network for Chinese Character recognition 26

Critical Radical Analysis Network for Chinese Character Reco...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Luo, Guo-Feng Yin, Hua-Yi Wang, Da-Han Zhang, Xu-Yao Zhu, Shun-Zhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit NLPR Inst Automat Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Zero-shot learning is a challenging problem in many tasks due to the lack of training samples of the unseen classes. the radical-based zero-shot Chinese character recognition methods treat Chinese characters as a combination of radicals and structures, and recognize Chinese characters by identifying the radicals and structures contained in them. Current approaches generally treat the contribution of all radicals to Chinese character recognition as the same, and the recognition results rely on the network's ability to recognize radicals and their corresponding position information, ignoring the potential value of radicals themselves in eliminating the uncertainty of Chinese characters. In this paper, we model the problem of radical-based Chinese character recognition as an uncertainty elimination problem and propose a Critical Radical Analysis Network (CRAN) to explore the Ideographic Description Sequence (IDS) information for zero-shot Chinese character recognition. Specifically, we propose a novel method to compute the critical values of radicals based on information theory using the predefined Chinese character IDS dictionary. In recognition, we use an iterative approach to translate the predicted radical sequence to target Chinese characters. that is, the radicals of the predicted sequence are sorted in descending order of the critical value, and then the radicals are continuously selected in this order as the information obtained to eliminate the uncertainty of the Chinese character until the character is recognized. We conduct experiments on the CTW, CASIA-AHCDB, and CASIA-HWDB datasets. the experimental results show that the proposed method improves the ability of recognizing unseen Chinese characters, demonstrating the effectiveness of the proposed method.

关键词： Zero-shot learning Chinese character recognition

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2018 10th iapr workshop on pattern recognition in remote sensing, PRRS 2018

2018 10th IAPR Workshop on Pattern Recognition in Remote Sen...

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10th iapr workshop on pattern recognition in remote sensing, PRRS 2018

ISBN: (纸本)9781538684795

the proceedings contain 50 papers. the topics discussed include: urban local climate zone classification with a residual convolutional neural network and multi-seasonal sentinel-2 images;an elegant end-to-end fully convolutional network (E3FCN) for green tide detection using MODIS data;preliminary investigation on single remote sensing image inpainting through a modified GAN;collaborative classification of hyperspectral and LIDAR data using unsupervised image-to-image CNN;neighbor consistency based unsupervised manifold alignment for classification of remote sensing image;feature fusion through multitask CNN for large-scale remote sensing image segmentation;inversion of heavy metal content in a copper mining area based on extreme learning machine optimized by particle swarm algorithm;a method of interactively extracting region objects from high-resolution remote sensing image based on full connection CRF;and multi-branch regression network for building classification using remote sensing images.

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11th iapr workshop on pattern recognition in remote sensing and 25th International Conference on pattern recognition

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the Photogrammetric Record 2020年第171期35卷

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5th iapr TC 9 workshop on pattern recognition of Social Signals in Human-Computer Interaction, MPRSS 2018

5th IAPR TC 9 Workshop on Pattern Recognition of Social Sign...

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5th iapr TC 9 workshop on pattern recognition of Social Signals in Human-Computer Interaction, MPRSS 2018

ISBN: (纸本)9783030209834

the proceedings contain 9 papers. the special focus in this conference is on pattern recognition of Social Signals in Human-Computer Interaction. the topics include: A first-person vision dataset of office activities;perceptual judgments to detect computer generated forged faces in social media;combining deep and hand-crafted features for audio-based pain intensity classification;deep learning algorithms for emotion recognition on low power single board computers;improving audio-visual speech recognition using gabor recurrent neural networks;evolutionary algorithms for the design of neural network classifiers for the classification of pain intensity;visualizing facial expression features of pain and emotion data.

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Neighbor Consistency Baced Unsupervised Manifold Alignment for Classification of remote sensing Image 10

Neighbor Consistency Baced Unsupervised Manifold Alignment f...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Luo, Chuang Ma, Li China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Hubei Peoples R China

ISBN: (纸本)9781538684795

We perform unsupervised domain adaptation for classification of remote sensing images by exploiting unsupervised manifold alignment approach. Manifold alignment method utilized corresponding points between domains to align data manifolds of source and target domains, where the corresponding points can be constructed by labeled information. Supposing labeled samples are not available in target domain, we proposed neighbor consistency (NC) constraint to select some target points that have reliable predictions. these points and labeled source data are then used to construct corresponding relationships, resulting in unsupervised manifold alignment. the neighbor consistency based unsupervised manifold alignment is denoted as NCUMA in this paper. Both multispectral and hyperspectral remote sensing data have been used to demonstrate the effectiveness of the NCUMA approach.

关键词： domain adaptation classification remote sensing manifold alignment neighbor consistency

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Multi-modal remote sensing Image Registration Based on Multi-scale Phase Congruency 10

Multi-modal Remote Sensing Image Registration Based on Multi...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Cui, Song Zhong, Yanfei Wuhan Univ WHU State Key Lab Informat Engn Surveying Mapping & R Wuhan Hubei Peoples R China

ISBN: (纸本)9781538684795

Automatic matching of multi-modal remote sensing images remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. this paper introduces the phase congruency model with illumination and contrast invariance for image matching, and extends the model to a novel image registration method, named as multi-scale phase consistency (MS-PC). the Euclidean distance between MS-PC descriptors is used as similarity metric to achieve correspondences. the proposed method is evaluated with four pairs of multi-model remote sensing images. the experimental results show that MS-PC is more robust to the radiation differences between images, and performs better than two popular method (i.e. SIFT and SAR-SIFT) in both registration accuracy and tie points number.

关键词： Multi-modal remote sensing Image Multi-scale Image Registration Phase Congruency

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Feature Fusion through Multitask CNN for Large-scale remote sensing Image Segmentation 10

Feature Fusion through Multitask CNN for Large-scale Remote ...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Sun, Shihao Yang, Lei Liu, Wenjie Li, Ruirui Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781538684795

In recent years, Fully Convolutional Networks (FCN) has been widely used in various semantic segmentation tasks, including multi-modal remote sensing imagery. How to fuse multi-modal data to improve the segmentation performance has always been a research hotspot. In this paper, a novel end-to-end fully convolutional neural network is proposed for semantic segmentation of natural color, infrared imagery and Digital Surface Models (DSM). It is based on a modified DeepUNet and perform the segmentation in a multi-task way. the channels are clustered into groups and processed on different task pipelines. After a series of segmentation and fusion, their shared features and private features are successfully merged together. Experiment results show that the feature fusion network is efficient. And our approach achieves good performance in ISPRS Semantic Labeling Contest (2D).

关键词： remote sensing feature fusion DeepUNet ISPRS semantic segmentation pixel-wise classification

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Reconstructing Lattices from Permanent Scatterers on Facades 10

Reconstructing Lattices from Permanent Scatterers on Facades

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Michaelsen, Eckart Soergel, Uwe Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany Univ Stuttgart Chair Remote Sensing Geschwister Scholl Str 24D D-70174 Stuttgart Germany

ISBN: (纸本)9781538684795

In man-made structures regularities and repetitions prevails. In particular in building facades lattices are common in which windows and other elements are repeated as well in vertical columns as in horizontal rows. In very-high-resolution space-borne radar images such lattices appear saliently. Even untrained arbitrary subjects see the structure instantaneously. However, automatic perceptual grouping is rarely attempted. this contribution applies a new lattice grouping method to such data. Utilization of knowledge about the particular mapping process of such radar data is distinguished from the use of Gestalt laws. the latter are universally applicable to all kinds of pictorial data. An example with so called permanent scatterers in the city of Berlin shows what can be achieved with automatic perceptual grouping alone, and what can be gained using domain knowledge.

关键词： perceptual grouping SAR permanent scatterers facade recognition

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Multi-focus Image Fusion with PCA Filters of PCANet 1

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5th iapr TC 9 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

作者： Song, Xu Wu, Xiao-Jun Jiangnan Univ Jiangsu Prov Engn Laborator Pattern Recognit & Co Wuxi 214122 Jiangsu Peoples R China

ISBN: (数字)9783030209841

ISBN: (纸本)9783030209841;9783030209834

As is well known to all, the training of deep learning model is time consuming and complex. therefore, in this paper, a very simple deep learning model called PCANet is used to extract image features from multi-focus images. First, we train the two-stage PCANet using ImageNet to get PCA filters which will be used to extract image features. Using the feature maps of the first stage of PCANet, we generate activity level maps of source images by using nuclear norm. then, the decision map is obtained through a series of post-processing operations on the activity level maps. Finally, the fused image is achieved by utilizing a weighted fusion rule. the experimental results demonstrate that the proposed method can achieve state-of-the-art fusion performance in terms of both objective assessment and visual quality.

关键词： Multi-focus image fusion PCA filters Nuclear norm

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