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检索条件"主题词=Change detection algorithms"

共 1584 条记录，以下是311-320 订阅

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A novel scene change detection algorithm based on the 3D wavelet transform

A novel scene change detection algorithm based on the 3D wav...

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IEEE International Conference on Image Processing

作者： Zhi Li Guizhong Liu School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an China

This paper proposes a novel scene change detection algorithm based on the 3D wavelet transform. In scene change including cut, dissolve and fade, the frames have particular temporal and spatial layouts. The dissolve and the fade have strong temporal and spatial correlation; on the contract the correlation of the cut is weak. The 3D wavelet transform can effectively express the correlation of the several successive frames since the low-frequency and high-frequency component coefficients have the proper statistics regularities which can effectively identify the shot transitions. Three features are computed to describe the correlation of the shot transitions, which are input to support vector machines for scene change detection. Experimental results show that the method is effective for the gradual shot transition detection.

关键词： Layout detection algorithms Wavelet transforms Videos change detection algorithms Support vector machines Gunshot detection systems Histograms Object detection Motion detection

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Affine-invariant registration using orthogonal projection matrices for object-based change detection

Affine-invariant registration using orthogonal projection ma...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Huijing Fu Jing Han Maohua Ran Zheng Tian School of Mathematics and Statistics Hubei University Wuhan China College of Marine Engineering Northwestern Polytechnical University Xi'an China Department of Scientific Research PLA Second Artillery Command Institute Wuhan China Department of Applied Mathematics Northwestern Polytechnical University Xi'an China

Contour-based registration provides a feasible approach to object-based change detection with the development of segmentation techniques in remote sensing. In this paper, an affine-invariant registration algorithm based on orthogonal projection matrices is proposed for object-based change detection. First, we extract the objects of interest using segmentation technique and detect the curvature extreme points as feature points in the contour of each object. Then, for each feature point, we construct its descriptor using the orthogonal project matrix of its affine-invariant neighborhood. Finally, object registration is derived through feature point matching based on the descriptor. Experiments of reservoir change detection demonstrate the proposed algorithm is effective in change detection of remote sensing images.

关键词： Reservoirs Remote sensing Feature extraction change detection algorithms Shape Accuracy Image segmentation

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Unsupervised change detection for urban expansion monitoring: An object-based approach

Unsupervised change detection for urban expansion monitoring...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Daniele De Vecchi Daniel Aurelio Galeazzo Mostapha Harb Fabio Dell'Acqua Universita degli Studi di Pavia Pavia Lombardia IT Aerospace section EUCENTRE Foundation I-27100 Pavia Italy

change detection is by definition the capability to detect and highlight changes occurring in space and time. Earth Observation satellites represent a fundamental source of information thanks to repeatability in time and spatial resolution. In this paper, we propose an unsupervised change detection technique capable of processing a series of single-date built-up area extractions with two main goals: determining the age of different parts of an urban area and fixing errors due to the automatic extractions suggested in previous papers by our group. Results show a general stabilization of the Kappa value but further investigation is still necessary. The proposed algorithm is available to the general public as a part of a QGIS plugin named SENSUM Earth Observation (EO) tools.

关键词： Remote sensing Earth Satellites Monitoring change detection algorithms Accuracy Spatial resolution

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Step change detection for Improved ROCOF Evaluation of Power System Waveforms

Step Change Detection for Improved ROCOF Evaluation of Power...

引用

Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

作者： Alexandra Karpilow Mario Paolone Asja Derviš kadić Guglielmo Frigo Distributed Electrical Systems Laboratory EPFL Lausanne Switzerland Swissgrid Ltd. Aarau Switzerland METAS Bern Switzerland

ISBN: (数字)9781665498234

ISBN: (纸本)9781665498241

In the analysis of power grid waveforms, the presence of amplitude or phase steps can disrupt the estimation of frequency and rate-of-change-of-frequency (ROCOF). Standard methods based on phasor-models fail in the extraction of signal parameters during these signal dynamics, often yielding large frequency and ROCOF deviations. To address this challenge, we propose a technique that approximates components of the signal (e.g., amplitude and frequency variations) using dictionaries based on parameterized models of common signal dynamics. Distinct from a previous iteration of this method developed by the authors, the proposed technique allows for the identification of multiple steps in a window, as well as the presence of interfering tones. The method is shown to improve signal reconstruction when applied to real-world waveforms, as compared to standard static and dynamic phasor-based algorithms.

关键词： Power measurement Phase measurement Frequency modulation change detection algorithms Heuristic algorithms Power system dynamics Estimation

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***: A new change detection benchmark dataset

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Nil Goyette Pierre-Marc Jodoin Fatih Porikli Janusz Konrad Prakash Ishwar MOIVRE Université de Sherbrook Sherbrooke Canada Imaging Group Mitsubishi Electric Research Laboratories Inc. Cambridge MA USA ECE Department Boston University Boston MA USA

change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video dataset exists for benchmarking different methods. Presented here is a unique change detection benchmark dataset consisting of nearly 90,000 frames in 31 video sequences representing 6 categories selected to cover a wide range of challenges in 2 modalities (color and thermal IR). A distinguishing characteristic of this dataset is that each frame is meticulously annotated for ground-truth foreground, background, and shadow area boundaries - an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of change detection algorithms. This paper presents and discusses various aspects of the new dataset, quantitative performance metrics used, and comparative results for over a dozen previous and new change detection algorithms. The dataset, evaluation tools, and algorithm rankings are available to the public on a website 1 and will be updated with feedback from academia and industry in the future.

关键词： Measurement change detection algorithms Cameras Positron emission tomography Benchmark testing detection algorithms Gray-scale

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Research of unsupervised image change detection algorithm based on 2-D histogram

Research of unsupervised image change detection algorithm ba...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Wenbang Sun Haiyan Tang Hexin Chen Guang Yu School of Communication Engineering Jilin University Changchun China Aviation Theory Department Aviation University슠of Air슠Force Changchun China Aviation Information Department Aviation University슠of Air슠Force Changchun China

change detection in images of a given scene acquired at different times is one of the most interesting topics of remote image processing. In this paper, a novel image change detection algorithm is proposed based on the clustering characteristic of 2-D histogram. First, the best segmentation direction of 2-D histogram is ascertained by using Fisher criterion. Secondly, a kind of new 2-D entropy is defined to search the best threshold and segment the 2-D histogram into unchanged region and change region. Then the change area in different image is detected based on the change region of 2-D histogram. Finally, the proposed algorithm is compared to traditional algorithm. The theoretical analysis and experiment results confirm the effectiveness of the proposed means.

关键词： Histograms Pixel Image segmentation Entropy change detection algorithms Algorithm design and analysis Noise

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An unsupervised change detection technique based on Ant colony Optimization

An unsupervised change detection technique based on Ant colo...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Akansha Mehrotra Krishna Kant Singh Priyanka Khandelwal Earthquake Engineering Department IIT Roorkee Roorkee India Electronics & Computer Engineering Department Dronacharya College of Engineering Gurgaon India Electronics & Communication Engineering Department Quantam School of Technology Roorkee India

In this paper, an unsupervised technique for change detection in satellite images based on Ant Colony Optimization is presented. Initially, a difference image is computed using normalized neighborhood ratio approach. The cluster centers are then computed using Ant colony based clustering algorithm. The cluster centers obtained from ant based algorithm are used to cluster the difference image into two clusters changed and unchanged. The clustered image is the change map that represents the changed and unchanged areas. The proposed method is compared with some other state of the art methods. Both visual as well as quantative results including percentage correct classification and kappa coefficient verify that the proposed method gives better results.

关键词： Remote sensing Satellites Clustering algorithms change detection algorithms Earth Ant colony optimization

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change detection of buildings in urban environment from high spatial resolution satellite images using existing cartographic data and prior knowledge

Change detection of buildings in urban environment from high...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mourad Bouziani Kalifa Goita Dong-Chen He Department of applied geomatics Centre d'applications et de recherches en télédétection University of Sherbrooke Sherbrooke Canada

Several studies in remote sensing image processing have tackled the issue of change detection for cartographic needs. Many algorithms have been developed, but very few have been applied for urban studies to update maps from high-spatial-resolution remote sensing images. The semantic richness of the image increases and makes image analysis more difficult. change detection from high spatial resolution images such as Ikonos and QuickBird is even more challenging, especially in complex environments like urban areas characterized by small objects such as houses, individual trees and roads, and by shadows. In addition, even if they are usually available, existing digital map data often are not incorporated in all steps of change detection process. This research project proposes a new object oriented method for the detection of building changes from high-spatial-resolution images in urban areas inspired by the specific problem of guiding the process by using existing cartographic data and knowledge. The use of the existing digital map and the integration of existing knowledge allow optimizing the change detection process on the image while offering the possibility to target and to accelerate the research of the changes.

关键词： Spatial resolution Satellites Remote sensing Object detection Urban areas Image processing change detection algorithms Image analysis Roads Object oriented methods

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Effective change detection in Large Repositories of Unsolicited Traffic

Effective Change Detection in Large Repositories of Unsolici...

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International Conference on Internet Monitoring and Protection, ICIMP

作者： Ejaz Ahmed Andrew Clark George Mohay Queensland University of Technology Brisbane Australia

When monitoring unsolicited network traffic automated detection and characterization of abrupt changes in the traffics statistical properties is important. These abrupt changes can either be due to a single or multiple anomalous activities taking place at the same time. The start of a new anomalous activity while another anomalous activity is in operation will result in a new change nested within the previous change. Although detection of abrupt changes to identify malicious activities has received considerable attention in the past, automated detection of nested changes has not been addressed. In this paper a dynamic sliding window cumulative sum (CUSUM) algorithm is proposed to automatically identify these nested changes. The novelty of the proposed technique lies in its ability to automatically detect nested changes, without which interesting activities may go undetected, and its effectiveness in identifying both the start and the end of the individual changes. Using an analysis of real network traces, we show that the identified nested changes were indeed due to distinct malicious behaviours taking place in parallel.

关键词： Telecommunication traffic Computerized monitoring Computer crime Internet Condition monitoring IP networks Protection Australia change detection algorithms

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FULLY CONVOLUTIONAL SIAMESE NETWORKS FOR change detection

FULLY CONVOLUTIONAL SIAMESE NETWORKS FOR CHANGE DETECTION

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IEEE International Conference on Image Processing

作者： Rodrigo Caye Daudt Bertrand Le Saux Alexandre Boulch LTCI Télécom ParisTech Paris France DTIS Université Paris-Saclay Palaiseau France Ecole Polytechnique Palaiseau FR

This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.

关键词： Cats Earth Training Computer architecture Machine learning Image analysis change detection algorithms

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