The Experts below are selected from a list of 17451 Experts worldwide ranked by ideXlab platform
Zhenjiang Miao - One of the best experts on this subject based on the ideXlab platform.
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A moving object segmentation algorithm for Static Camera via active contours and GMM
Science in China Series F: Information Sciences, 2009Co-Authors: Chengkai Wan, Baozong Yuan, Zhenjiang MiaoAbstract:Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for Static Camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.
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a new algorithm for Static Camera foreground segmentation via active coutours and gmm
International Conference on Pattern Recognition, 2008Co-Authors: Chengkai Wan, Baozong Yuan, Zhenjiang MiaoAbstract:Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for Static Camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Because of the integration of GMM background model, shadow elimination term and curve evolution edge stopping term into energy function, it achieves more accurate segmentation than existing method of the same type. Promising results on real images demonstrate the potential of the presented method.
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ICPR - A new algorithm for Static Camera foreground segmentation via active coutours and GMM
2008 19th International Conference on Pattern Recognition, 2008Co-Authors: Chengkai Wan, Baozong Yuan, Zhenjiang MiaoAbstract:Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for Static Camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Because of the integration of GMM background model, shadow elimination term and curve evolution edge stopping term into energy function, it achieves more accurate segmentation than existing method of the same type. Promising results on real images demonstrate the potential of the presented method.
C U Keller - One of the best experts on this subject based on the ideXlab platform.
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calibrating a high resolution wavefront corrector with a Static focal plane Camera
Applied Optics, 2013Co-Authors: Visa Korkiakoski, Niek Doelman, Johanan L Codona, Matthew A Kenworthy, G P P L Otten, C U KellerAbstract:We present a method to calibrate a high-resolution wavefront (WF)-correcting device with a single, Static Camera, located in the focal-plane; no moving of any component is needed. The method is based on a localized diversity and differential optical transfer functions to compute both the phase and amplitude in the pupil plane located upstream of the last imaging optics. An experiment with a spatial light modulator shows that the calibration is sufficient to robustly operate a focal-plane WF sensing algorithm controlling a WF corrector with 40,000 degrees of freedom. We estimate that the locations of identical WF corrector elements are determined with a spatial resolution of 0.3% compared to the pupil diameter.
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calibrating a high resolution wavefront corrector with a Static focal plane Camera
arXiv: Instrumentation and Methods for Astrophysics, 2013Co-Authors: Visa Korkiakoski, Niek Doelman, Johanan L Codona, Matthew A Kenworthy, G P P L Otten, C U KellerAbstract:We present a method to calibrate a high-resolution wavefront-correcting device with a single, Static Camera, located in the focal plane; no moving of any component is needed. The method is based on a localized diversity and differential optical transfer functions (dOTF) to compute both the phase and amplitude in the pupil plane located upstream of the last imaging optics. An experiment with a spatial light modulator shows that the calibration is sufficient to robustly operate a focal-plane wavefront sensing algorithm controlling a wavefront corrector with ~40 000 degrees of freedom. We estimate that the locations of identical wavefront corrector elements are determined with a spatial resolution of 0.3% compared to the pupil diameter.
Yanyan Gao - One of the best experts on this subject based on the ideXlab platform.
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CDC - Velocity and range identification of a moving object using a Static-moving Camera system
2016 IEEE 55th Conference on Decision and Control (CDC), 2016Co-Authors: Kaixiang Zhang, Jian Chen, Bingxi Jia, Yanyan GaoAbstract:In this paper, an observer-based approach is proposed to asymptotically identify the velocity and range of feature points on a moving object using a Static-moving Camera system. Specifically, the system is composed of a Static Camera and a moving Camera, and the approach is divided into two steps. Firstly, utilizing the Static Camera, a nonlinear observer is designed to identify the up-to-a-scale velocity of feature points. Secondly, with the moving Camera and the estimated scaled velocity, the range of feature points is identified by an adaptive estimator. Owing to the introduction of the Static-moving Camera system, no motion constraint or a priori geometric knowledge is required for the moving object. Lyapunov-based analyses are used to prove that the estimators asymptotically identify the velocity and range of feature points. The performance of the proposed strategy is demonstrated by simulation results.
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velocity and range identification of a moving object using a Static moving Camera system
Conference on Decision and Control, 2016Co-Authors: Kaixiang Zhang, Jian Chen, Bingxi Jia, Yanyan GaoAbstract:In this paper, an observer-based approach is proposed to asymptotically identify the velocity and range of feature points on a moving object using a Static-moving Camera system. Specifically, the system is composed of a Static Camera and a moving Camera, and the approach is divided into two steps. Firstly, utilizing the Static Camera, a nonlinear observer is designed to identify the up-to-a-scale velocity of feature points. Secondly, with the moving Camera and the estimated scaled velocity, the range of feature points is identified by an adaptive estimator. Owing to the introduction of the Static-moving Camera system, no motion constraint or a priori geometric knowledge is required for the moving object. Lyapunov-based analyses are used to prove that the estimators asymptotically identify the velocity and range of feature points. The performance of the proposed strategy is demonstrated by simulation results.
Mubarak Shah - One of the best experts on this subject based on the ideXlab platform.
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probabilistic modeling of scene dynamics for applications in visual surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009Co-Authors: Imran Saleemi, Khurram Shafique, Mubarak ShahAbstract:We propose a novel method to model and learn the scene activity, observed by a Static Camera. The proposed model is very general and can be applied for solution of a variety of problems. The motion patterns of objects in the scene are modeled in the form of a multivariate nonparametric probability density function of spatiotemporal variables (object locations and transition times between them). Kernel Density Estimation is used to learn this model in a completely unsupervised fashion. Learning is accomplished by observing the trajectories of objects by a Static Camera over extended periods of time. It encodes the probabilistic nature of the behavior of moving objects in the scene and is useful for activity analysis applications, such as persistent tracking and anomalous motion detection. In addition, the model also captures salient scene features, such as the areas of occlusion and most likely paths. Once the model is learned, we use a unified Markov Chain Monte Carlo (MCMC)-based framework for generating the most likely paths in the scene, improving foreground detection, persistent labeling of objects during tracking, and deciding whether a given trajectory represents an anomaly to the observed motion patterns. Experiments with real-world videos are reported which validate the proposed approach.
Chengkai Wan - One of the best experts on this subject based on the ideXlab platform.
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A moving object segmentation algorithm for Static Camera via active contours and GMM
Science in China Series F: Information Sciences, 2009Co-Authors: Chengkai Wan, Baozong Yuan, Zhenjiang MiaoAbstract:Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for Static Camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.
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a new algorithm for Static Camera foreground segmentation via active coutours and gmm
International Conference on Pattern Recognition, 2008Co-Authors: Chengkai Wan, Baozong Yuan, Zhenjiang MiaoAbstract:Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for Static Camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Because of the integration of GMM background model, shadow elimination term and curve evolution edge stopping term into energy function, it achieves more accurate segmentation than existing method of the same type. Promising results on real images demonstrate the potential of the presented method.
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ICPR - A new algorithm for Static Camera foreground segmentation via active coutours and GMM
2008 19th International Conference on Pattern Recognition, 2008Co-Authors: Chengkai Wan, Baozong Yuan, Zhenjiang MiaoAbstract:Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for Static Camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Because of the integration of GMM background model, shadow elimination term and curve evolution edge stopping term into energy function, it achieves more accurate segmentation than existing method of the same type. Promising results on real images demonstrate the potential of the presented method.
