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National Conference on Radio Science (NSRC)

作者： S.E. El-Khamy M.A. Mokhtar N.O. El-Ganainy Department of Electrical Engineering Faculty of Engineering Alexandria University Alexandria Egypt Alexandria Higher Institute of Engineering and Technology Alexandria Egypt

In this paper, two new techniques for differential detection of changes and motion in a set of images are proposed. The suggested techniques adopt an efficient parallel complementary encoding stage applied to the image frames under study. These techniques profit from the optimum correlation properties of complete complementary code arrays. Simulation results are provided to demonstrate the efficient performance of the proposed techniques when applied to various types of images.

关键词： Motion detection Motion analysis Image sequences Image coding Image processing Electronic mail Image motion analysis Object detection change detection algorithms Image segmentation

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Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery

Generalized minimum-error thresholding for unsupervised chan...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： G. Moser S.B. Serpico Department of Biophysical and Electronic Engineering University of Genoa Genoa Italy

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Robust and efficient change detection algorithm based on 3D line segments

Robust and efficient change detection algorithm based on 3D ...

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IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： Tom Zohar Ido Ariav Meir Bar-Zohar Signal and Image Processing Laboratory Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel

In this paper we examine and improve a new approach for change detection (introduced in [1]) which is based on the appearance and disappearance of 3D line segments as seen in a new image. These 3D line segments are estimated from a set of learning images taken from arbitrary viewpoints and under arbitrary light conditions in an unsupervised manner. The main advantage of the proposed method lies in the fact that the change detection is performed by comparing line segments, and not surfaces or gray levels. Computing 3D surfaces in an image can be computationally intensive, and other methods such as image subtraction or cross-correlation are sensitive to lighting conditions and changes in viewpoints. Moreover, most man-made objects such as buildings, cars, and even cities viewed from above consist mainly of straight linesman-made objects, and therefore this method is highly applicable for such structures. The proposed algorithm first focuses on the reconstruction of a set of 3D line segments forming a certain 3D scene using a set of 2D line segments obtained from the learning images in an unsupervised manner, without any prior knowledge on the cameras' positions or relative distance. In the change detection stage, we use the reconstructed 3D scene of line segments to check if changes, such as appearance or disappearance of objects, have occurred in a given test image. This test image can be taken from arbitrary viewpoint and under arbitrary lighting conditions. Our change detection algorithm not only distinguishes between the states of "changed" and "not-changed" line segments, it also classifies the "changed" line segments as appeared - objects that entered the scene in the test image, and disappeared - objects that left the 3D scene reconstructed from the lines of the learning images.

关键词： Image segmentation Image reconstruction Cameras Lighting change detection algorithms Solid modeling Clutter

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Video scene change detection method using unsupervised segmentation and object tracking

Video scene change detection method using unsupervised segme...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shu-Ching Chen Mei-Ling Shyu Cheng-Cui Zhang R.L. Kashyap Distributed Multimedia Information System Laboratory School of Computer Science Florida International University Miami FL USA Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

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Double change detection method for moving-object segmentation based on clustering

Double change detection method for moving-object segmentatio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Haihua Liu Xinhao Chen Yaguang Chen Changsheng Xie College of Electronics and Information Engineering South-Central University for Nationalities Wuhan China College of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

In this paper, an efficient moving object segmentation algorithm in the wavelet domain is proposed using three successive frames. The change detection method, which employs fuzzy C-means clustering technique to classify motion features of four wavelet sub-bands, is used twice to separate significant change pixels in the wavelet domain from the background. After applying the intersect operation, the change detection masks are obtained in wavelet domain. Finally, further object shape information and accurate extraction of the moving object is obtained in original resolution according to current object edge map. The experimental results demonstrate the algorithm effective

关键词： Object detection change detection algorithms Wavelet domain Motion detection Shape Layout Data mining Video sequences Motion estimation Educational institutions

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Motion-based video representation for scene change detection

Motion-based video representation for scene change detection

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International Conference on Pattern Recognition

作者： Chong-Wah Ngo Ting-Chuen Pong Hong-Jiang Zhang R.T. Chin Department of Computer Science Hong Kong University of Science and Technology Hong Kong China Microsoft Research China Beijing China

ISBN: (纸本)0769507506

We present a scheme for automatically partitioning videos into scenes. A scene is generally referred to as a group of shots taken at the same site. We first propose a motion annotation algorithm based on the analysis of spatiotemporal image volumes. The algorithm characterizes the motions within shots by extracting and analyzing the motion trajectories encoded in the temporal slices of image volumes. A motion-based keyframe computing and selection strategy is thus proposed to compactly represent the content of shots. With these techniques, we further present a scene change detection algorithm by measuring the similarity of the representative keyframes in shots.

关键词： Layout Motion detection Motion analysis change detection algorithms Partitioning algorithms Image analysis Image motion analysis Algorithm design and analysis Spatiotemporal phenomena detection algorithms

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An On-Line change detection Scheme for Nonlinear Time Series Data from Unknown Dynamical Systems: A Bayesian Appraoch Using Sequential Monte Carlo

An On-Line Change Detection Scheme for Nonlinear Time Series...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Y. Nakada T. Matsumoto Graduate School of Science and Engineering Waseda University Tokyo Japan

This paper attempts to perform on-line change detection given time series data from unknown nonlinear dynamical systems. In the algorithm, the probability of occurrence of an abrupt change is estimated within a Bayesian framework. The implementation is done via sequential Monte Carlo (SMC). The proposed scheme is tested against two examples with nonlinear dynamical systems

关键词： Bayesian methods Monte Carlo methods change detection algorithms Sliding mode control Nonlinear dynamical systems Signal processing algorithms Data analysis Uncertainty Nonlinear systems Testing

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Pr-Based Sar Reconstruction Autofocus Algorithm for Persistent Surveillance change detection

Pr-Based Sar Reconstruction Autofocus Algorithm for Persiste...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yue Yang Qing Zhang Xuejing Zhang Qun Wan Keyu Long Xunchao Cong University of Electronic Science and Technology of China Chengdu China The Second Research Institute of CAAC Chengdu China The 10th Research Institute of CETC Chengdu China

Random phase noises arising from frequency jitter of transmit signal and atmospheric turbulence result in corrupted synthetic aperture radar (SAR) imagery, which in turn degrades change detection (CD) performance. In this paper, a phase retrieval (PR) based SAR reconstruction autofocus framework by exploiting the hidden convexity is proposed with the goal of achieving reliable persistent surveillance CD. Firstly the original non-convex quartic SAR reconstruction is reformulated as a convex quadratic program. Under the minimum phase assumption, the auto-correlation retrieval- Kolmogorov factorization (CoRK) algorithm is then utilized to optimally and efficiently retrieve the underlying SAR reflectivity. The devised scheme possesses effective capabilities of phase noise mitigation, thus has a superior CD performance. Experimental results are provided to verify the effectiveness of the proposed method.

关键词： Phase noise Synthetic aperture radar Image reconstruction Surveillance Radar polarimetry Scattering change detection algorithms

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DEFENSE APPLICATIONS OF IMAGE UNDERSTANDING

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IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS 1995年第5期10卷 11-17页

作者： FIRSCHEIN, O Advanced Research Projects Agency

A key goal of the US Defense Department's Advanced Research Projects Agency Image Understanding program is to provide advanced vision systems to the military. This article describes two such IU projects; Radius, designed to aid intelligence image analysts, and UGV-RSTA, an IU system that enables an unmanned military ground vehicle to scout for enemy targets.

关键词： Roads Buildings Vehicle detection Frequency domain analysis Automation Focusing Vehicles Image analysis change detection algorithms Wire

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Thompson Sampling-Based Dynamic Spectrum Access in Non-Stationary Environments

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 593-603页

作者： Ye, Shuai Wang, Tianyu Wang, Shaowei Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China

In dynamic spectrum access (DSA), unlicensed secondary users (SUs) estimate the idle probabilities of primary channels by using historical sensing results and opportunistically access the channel with the highest idle probability for transmission. Due to the rapid traffic changes and irregular user mobility, primary channels can be highly dynamic and their idle probabilities are generally time-varying. In this paper, we investigate DSA in non-stationary environments. Specifically, we consider two channel state models, i.e., the non-stationary Bernoulli model with a time-varying mean and the non-stationary Markovian model with a time-varying transition matrix. For the single-SU scenario, we propose a Thompson sampling based method with a change detection technique, which is capable of detecting the variations of channel statistics and adjusting the channel access strategy accordingly. For the multi-SU scenario, we propose a Thompson sampling based collision alleviation method to coordinate the transmissions of SUs, which does not need any prior protocol or information exchange among SUs. Numerical results show that the proposed methods outperform the existing ones in terms of successful transmission ratio in various network settings.

关键词： Sensors Channel estimation change detection algorithms Dynamic spectrum access Protocols Probability Synchronization multi-armed bandit non-stationary environment Thompson sampling

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