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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是181-190 订阅

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Spectral Clustering of Events in Social Media Flood images based on Multimodal Analysis

Spectral Clustering of Events in Social Media Flood Images b...

引用

International conference on Electrical Energy Systems (ICEES)

作者： S S Pavithra S Chitrakala C M Bhatt CEG Campus Anna University Chennai Tamilnadu India Disaster Management Studies Indian Institute of Remote Sensing Dehradun Uttarakhand India

ISBN: (数字)9781728176123

ISBN: (纸本)9781728176130

In Data Science, Clustering is one of the most popular techniques. It has wide potential application in data analysis, market research, pattern recognition, image processing, etc. Spectral Clustering is a clustering algorithm shows improved performance than the conventional clustering algorithms. To handle Social media multimodal data in Event Clustering and for better understanding of the context of the post, Spectral Clustering of Events in Flood images Based on Multimodal Analysis (SCEFIMA) approach is proposed. This approach makes use of Post's image and textual content (Multimodal data) in Clustering the events. Also considering Spatial and Temporal Information in understanding the post. The existing approaches lacks on multimodal analysis in social media data. Use of Multimodal data gives the better understanding of the context of the post in event clustering. In addition to this, Using Multimodal data in Event Clustering significantly boost performance than the existing methods. Detecting and Clustering events from multimodal data in social networks helpful in monitoring events by public organizations and authorities. Also helpful in early planning for preventive measures.

关键词： Visualization Social networking (online) Clustering algorithms Tagging Real-time systems Planning Floods

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image Segmentation Technology and Its Application in Digital image processing

Image Segmentation Technology and Its Application in Digital...

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Advance in Ambient Computing and Intelligence (ICAACI), International conference on

作者： Min Tang Dalian Vocational & Technical College Dalian China

ISBN: (数字)9781728197531

ISBN: (纸本)9781728197548

With the development of electronic technology, computer software and hardware technology, image processing and technology related to human vision, the technology of machine vision image processing has also achieved rapid development. With the rapid development of science and technology, digital image processing technology has been widely used in various fields, among which image segmentation is an extremely important technology. image segmentation is an important intermediate technology in digital image processing. It relies on the underlying technology of image digital processing to serve high-level applications such as pattern recognition. processing the information interaction between machine and the real world is the first step in the study of artificial intelligence, and the goal of computer vision research is to make the computer have the ability to perceive and understand image information similar to human. This paper expounds the basic principle of image segmentation technology, analyzes and discusses the methods of image segmentation, and studies the application of image segmentation technology in digital image processing.

关键词： image segmentation Digital images image edge detection Reliability theory Software pattern recognition remote sensing

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Monte-Carlo Siamese Policy on Actor for Satellite image Super Resolution

Monte-Carlo Siamese Policy on Actor for Satellite Image Supe...

引用

IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Litu Rout Saumyaa Shah S Manthira Moorthi Debajyoti Dhar Signal and Image Processing Group Indian Space Research Organisation Space Applications Centre

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the plausible usage of RL in super resolution of remote sensing imagery. Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning. We argue that a straightforward implementation of RL is not adequate to address ill-posed super resolution as the action variables are not fully known. To tackle this issue, we propose to parameterize action variables by matrices, and train our policy network using Monte-Carlo sampling. We study the implications of parametric action space in a model-free environment from theoretical and empirical perspective. Furthermore, we analyze the quantitative and qualitative results on both remote sensing and non-remote sensing datasets. Based on our experiments, we report considerable improvement over state-of-the-art methods by encapsulating supervised models in a reinforcement learning framework.

关键词： image resolution remote sensing Learning (artificial intelligence) Mathematical model Task analysis Machine learning Feature extraction

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image Analysis by Structural Dissimilarity Estimation 9

Image Analysis by Structural Dissimilarity Estimation

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9th International conference on image processing Theory, Tools and Applications (IPTA)

作者： Akl, Adib Yaacoub, Charles Holy Spirit Univ Kaslik USEK Sch Engn POB 446 Jounieh Lebanon

ISBN: (纸本)9781728139753

image analysis using dissimilarity estimation methods is a crucial process in several image processing applications. For instance, it is used in frame reconstruction, pattern recognition and image inpainting. Dissimilarity estimation methods based on intensity information lead to impressive results with different image types, but failure cases still exist especially when dealing with local structural variations. In this paper, a metric for dissimilarity estimation, based on structural information, is proposed. Results show that using the proposed metric, local dissimilarities can be better estimated comparing to traditional intensity-based metrics, thus helping the analysis of images in remote sensing images.

关键词： Euclidean Distance Dissimilarity Gaussian Kernel Local Orientation Tensor Field

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A Variational Pansharpening Algorithm Based on Total Variation and Primal-Dual Optimization 4

A Variational Pansharpening Algorithm Based on Total Variati...

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4th International conference on pattern recognition and image Analysis, IPRIA 2019

作者： Khademi, Ghassem Ghassemian, Hassan Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781728116211

This paper proposes a variational framework to estimate the high-resolution (HR) multispectral (MS) image from the low-resolution (LR) MS image and the panchromatic (Pan) image. The LR MS image is modeled as a decimation of the HR MS image. Furthermore, the Pan image is considered as a linear combination of the HR MS bands. A super-resolution (SR) model is defined in accordance with the image observation model and the total variation (TV) regularization. The SR reconstruction problem is modeled as a minimization problem, which is solved by an efficient primal-dual algorithm in a Euclidean setting. The result of comparing the proposed method with some recent classical and variational pansharpening methods proves the superiority of the proposed variational pansharpening algorithm. © 2019 IEEE.

关键词： remote sensing

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remote sensing image Retrieval via Symmetric Normal Inverse Gaussian Modeling of Nonsubsampled Shearlet Transform Coefficients 8th

Remote Sensing Image Retrieval via Symmetric Normal Inverse ...

引用

8th International conference on pattern recognition and Machine Intelligence (PReMI)

作者： Baruah, Hilly Gohain Nath, Vijay Kumar Hazarika, Deepika Tezpur Univ Dept Elect & Commun Engn Sonitpur Assam India

ISBN: (纸本)9783030348724;9783030348717

This paper presents a nonsubsampled shearlet transform (NSST) based remote sensing (RS) image retrieval technique where the NSST subband coefficients are modeled with symmetric normal inverse Gaussian (SNIG) distribution. The NSST is popular for its very good directional selectivity and shift invariance. The SNIG is a four parameter distribution which can describe a wide range of heavy tailed distributions. Through Kolmogorov-Smirnov (KS) goodness of fit test, it is shown that the SNIG best approximates the shape of distribution of detail NSST coefficients. For Maximum Likelihood (ML) estimation of SNIG parameters, an Expectation-Maximization (EM) type of algorithm is used. The RS images are first decomposed with NSST and the feature vector is formed with the estimated SNIG parameters from detail NSST coefficients. Here, the UC Merced land use (UCM) and the High-resolution satellite scene (HRS) datasets are used for the experiments conducted. The results of the proposed method reveal superior performance, when compared with some well known techniques.

关键词： remote sensing image retrieval Nonsubsampled shearlet transform Statistical model Symmetric normal inverse Gaussian

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A UNIFIED OBJECT DETECTION FRAMEWORK FOR INTELLIGENT RETAIL CONTAINER COMMODITIES 26

A UNIFIED OBJECT DETECTION FRAMEWORK FOR INTELLIGENT RETAIL ...

引用

26th IEEE International conference on image processing (ICIP)

作者： Zhao, Linyu Yao, Jian Du, Hongyu Zhao, Jinjie Zhang, Ruijie Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Wuhan Univ State Key Lab Info Engn Surveying Mapping & Remot Wuhan Peoples R China

ISBN: (纸本)9781538662496

Motivated by commodity detection in retail, this paper studies the problem of generating object bounding boxes in retail images. The existing object detection algorithms are not suitable due to the characteristics of the intensive distribution of commodities in intelligent retail containers. Therefore, we present a unified object detection framework used for dense scenarios in retail images, which consists of the hierarchical labeling pattern, the Similarity recognition Network (SRN) subnetwork and the optimized NMS algorithm on the basis of YOLOv3. Three datasets are built to verify the effectiveness of the proposed framework, two of which are used for training the object detection network, and the other one is used for training the SRN subnetwork. The experimental results show that the proposed framework achieves a significant improvement for dense scenario in retail images compared with the traditional object detection algorithms.

关键词： Intelligent retail object detection nonmaximum suppression

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S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis

S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attentio...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Litu Rout Indranil Misra S Manthira Moorthi Debajyoti Dhar Signal and Image Processing Group Indian Space Research Organisation

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Intersection of adversarial learning and satellite image processing is an emerging field in remote sensing. In this study, we intend to address synthesis of high resolution multi-spectral satellite imagery using adversarial learning. Guided by the discovery of attention mechanism, we regulate the process of band synthesis through spatio-spectral Laplacian attention. Further, we use Wasserstein GAN with gradient penalty norm to improve training and stability of adversarial learning. In this regard, we introduce a new cost function for the discriminator based on spatial attention and domain adaptation loss. We critically analyze the qualitative and quantitative results compared with state-of-the-art methods using widely adopted evaluation metrics. Our experiments on datasets of three different sensors, namely LISS-3, LISS-4, and WorldView-2 show that attention learning performs favorably against state-of-the-art methods. Using the proposed method we provide an additional data product in consistent with existing high resolution bands. Furthermore, we synthesize over 4000 high resolution scenes covering various terrains to analyze scientific fidelity. At the end, we demonstrate plausible large scale real world applications of the synthesized band.

关键词： Spatial resolution Signal resolution Generators Gallium nitride remote sensing Laplace equations

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Sparsity Regularization Based Spatial-Spectral Super-Resolution of Multispectral imagery 8th

Sparsity Regularization Based Spatial-Spectral Super-Resolut...

引用

8th International conference on pattern recognition and Machine Intelligence (PReMI)

作者： Mullah, Helal Uddin Deka, Bhabesh Barman, Trishna Prasad, A. V. V. Tezpur Univ Dept Elect & Commun Engn Tezpur 784028 Assam India Natl Remote Sensing Ctr Hyderabad 500037 India

ISBN: (纸本)9783030348694;9783030348687

Multispectral (MS) remote sensing image is composed of several spectral bands of distinct wavelengths. Most earth observation satellites provide MS images consisting several low-resolution (LR) bands together with a single high-resolution (HR) image. A single image superresolution (SISR) method tries to produce a HR MS output from the given LR MS input using digital image processing algorithms. In this work, we present a patch-wise sparse representation based MS image SR using a coupled overcomplete trained dictionary. The dictionary learning is carried out from patches extracted from the given HR panchromatic (PAN) image itself. Experiments are carried out using test MS images from QuickBird satellites and results are compared with other state-ofthe-art MS image SR and pan-sharpening methods.

关键词： Super-resolution Sparse representation Dictionary learning Pan-sharpening Spectral information

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2nd International conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

2nd International Conference on Recent Trends in Image Proce...

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2nd International conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

ISBN: (纸本)9789811391804

The proceedings contain 173 papers. The special focus in this conference is on Recent Trends in image processing and pattern recognition. The topics include: Execution and Performance Evaluation of Cognitive and Expressive Event on a robotic Arm;multiple Decompositions-Based Blind Watermarking Scheme for Color images;skeleton Joint Difference Maps for 3D Action recognition with Convolutional Neural Networks;Novel Quality Metric for image Super Resolution Algorithms - Super Resolution Entropy Metric (SREM);shape Descriptor Based on Centroid with Chord Lengths for image Retrieval;learning Deep Feature Representation for Face Spoofing;ExNET: Deep Neural Network for Exercise Pose Detection;background Subtraction and Kalman Filter Algorithm for Object Tracking;A Blind Color image Watermarking Using BRISK Features and Contourlet Transform;let Vehicles Talk with images for Smart Vehicular Communication System;gray Level Face recognition Using Spatial Features;application of Gabor Filter for Monitoring Wear of Single Point Cutting Tool;performance Analysis of Log-Gabor Based Multimodal Systems and Its Fusion Techniques;Temporal Super-Pixel Based Convolutional Neural Network (TS-CNN) for Human Activity recognition in Unconstrained Videos;Content Based Video Retrieval Using SURF, BRISK and HARRIS Features for Query-by-image;An Empirical Study: ELM in Face Matching;optimal Selection of Bands for Hyperspectral images Using Spectral Clustering;Classification of Natural Flower Videos Through Sequential Keyframe Selection Using SIFT and DCNN;Hyperspectral remote sensing image Analysis with SMACC and PPI Algorithms for Endmember Extraction;real Time Hand Gesture recognition for Differently-Abled Using Deep Learning;a Novel Foreground Segmentation Method Using Convolutional Neural Network;image Fusion Technique Using Gaussian Pyramid.

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