This letter puts forward a new algorithm, ensemble strategy multiobjective fuzzy clustering method (ESMOFCM). To fully combine the gray information and spatial information of neighbor pixels, a new dividing fluctuant ...
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This letter puts forward a new algorithm, ensemble strategy multiobjective fuzzy clustering method (ESMOFCM). To fully combine the gray information and spatial information of neighbor pixels, a new dividing fluctuant parameter is proposed for producing a difference image. Then, we use a frame based on multiobjective fuzzy clustering to alleviate the contradiction between removing noise and preserving details in images. Ensemble strategy is adopted to integrate all Pareto optimal solutions. The experimental results show that the proposed algorithm is superior to comparison algorithms.
Image changedetection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used tec...
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ISBN:
(纸本)9781509046010
Image changedetection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised changedetection. However, traditional fuzzy clustering algorithms are easy to be trapped into a local optimum due to the limits of their optimization processes. To tackle the problem, a novel differential evolution algorithm with an automatically learning selection strategy is proposed in this paper. Different from the selection rules of classical differential evolution algorithm, this method firstly pre-classifies all original individuals and trial individuals according to the scope of the individual fitness at each generation, which will preliminarily determine whether they are selected for the next generation. Secondly, in order to increase the diversity of the population, we choose a few individuals from the non-selected population with a low probability into selected ones. Finally, the samples including partial individuals from the selected and non-selected lists are used to train the neural networks that will learn the selection strategy. This method will learn different selection strategies in every generation, which will significantly accelerate the convergence speed. The proposed changedetection method, combining fuzzy clustering with newly designed differential evolution algorithm, show excellent performance. Experiments conducted on Synthetic Aperture Radar images have demonstrated the superiority of the proposed method.
This publication describes an application of a Truncated Signed Distance Mapping approach for disaster intervention in underground mine shafts through geometrical changedetection of the shaft walls. The paper describ...
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ISBN:
(纸本)9781728107783
This publication describes an application of a Truncated Signed Distance Mapping approach for disaster intervention in underground mine shafts through geometrical changedetection of the shaft walls. The paper describes two main problems of such an approach (aligning two potentially huge point clouds and automatic changedetection by comparing the reconstructed volumes) and explains in detail the proposed solution.
Online changedetection involves monitoring a stream of data for changes in the statistical properties of incoming observations. A good change detector will detect any changes shortly after they occur, while raising f...
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ISBN:
(数字)9781728113982
ISBN:
(纸本)9781728113982
Online changedetection involves monitoring a stream of data for changes in the statistical properties of incoming observations. A good change detector will detect any changes shortly after they occur, while raising few false alarms. Although there are algorithms with confirmed optimality properties for this task, they rely on the exact specifications of the relevant probability distributions and this limits their practicality. In this work we describe a kernel-based variant of the Cumulative Sum (CUSUM) changedetection algorithm that can detect changes under less restrictive assumptions. Instead of using the likelihood ratio, which is a parametric quantity, the Kernel CUSUM (KCUSUM) algorithm compares incoming data with samples from a reference distribution using a statistic based on the Maximum Mean Discrepancy (MMD) non-parametric testing framework. The KCUSUM algorithm is applicable in settings where there is a large amount of background data available and it is desirable to detect a change away from this background setting. Exploiting the random-walk structure of the test statistic, we derive bounds on the performance of the algorithm, including the expected delay and the average time to false alarm.
The problem of quickest changedetection in anonymous heterogeneous sensor networks is studied. The sensors are clustered into K groups, and different groups follow different data generating distributions. At some unk...
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ISBN:
(纸本)9781538682098
The problem of quickest changedetection in anonymous heterogeneous sensor networks is studied. The sensors are clustered into K groups, and different groups follow different data generating distributions. At some unknown time, an event occurs in the network and changes the data generating distribution of the sensors. The goal is to detect the change as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where at each time step, the fusion center receives unordered samples without knowing which sensor each sample comes from, and thus does not know its exact distribution. In In an optimal algorithm was provided, which however is not computational efficient for large networks. In this paper, a computationally efficient test is proposed and a novel theoretical characterization of its false alarm rate is further developed.
We consider the problem of changedetection in the context of finite dimensional Gaussian linear systems. In particular a known initial system will be tested for eventual changes against a known alternative using a si...
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ISBN:
(纸本)9783033039629
We consider the problem of changedetection in the context of finite dimensional Gaussian linear systems. In particular a known initial system will be tested for eventual changes against a known alternative using a simplified version of the Page-Hinkley or CUSUM detector. We show that the detector is L-mixing, implying the existence of an almost sure false alarm rate. The derivation of an explicit upper bound for the latter will be outlined.
New tools are required to support and increase the reliability and safety of unmanned aerial vehicle (UAV) formation operations. This paper poses a new formation coordination changedetection problem as a quickest det...
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ISBN:
(纸本)9781922107909
New tools are required to support and increase the reliability and safety of unmanned aerial vehicle (UAV) formation operations. This paper poses a new formation coordination changedetection problem as a quickest detection of change in signal coordination on the basis of a worst case average detection delay cost, inspired by Lorden's criteria. This paper also poses a pragmatic nested changedetection algorithm for detecting formation coordination. The proposed algorithms are evaluated on both simulated and real measurement data.
changedetection is one of the important tasks for video surveillance systems. A variety of learning-based approaches have been proposed, but class imbalance in training data degrades their learning efficiency. In thi...
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ISBN:
(纸本)9781728109909
changedetection is one of the important tasks for video surveillance systems. A variety of learning-based approaches have been proposed, but class imbalance in training data degrades their learning efficiency. In this paper, we propose a cross entropy loss with a modulating term in cosine form to handle this class imbalance. Although the original focal loss focuses only on reducing weights for well-classified data, the proposed function is designed to preserve sufficient gradients for rare hard samples as well. This property allows a network to learn mainly from a few significant samples on which the network should focus. We validate the proposed loss through various experiments on CDNet2014 dataset, and the results show that the network trained with the proposed loss achieves better performance than other state-of-the-arts in various complex scenarios.
In this paper, we present a novel scene changedetection algorithm in the H.264/AVC compressed domain directly. Three major statistical features are used: (i) bit allocation for intra mode macroblocks (Bleb, (ii) bit ...
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ISBN:
(纸本)9781424442041
In this paper, we present a novel scene changedetection algorithm in the H.264/AVC compressed domain directly. Three major statistical features are used: (i) bit allocation for intra mode macroblocks (Bleb, (ii) bit allocation for inter mode macroblocks (BPM), and (iii) the number of skip mode macroblocks in a frame. These features can be easily extracted from the H.264/AVC bitstreams. Besides the percent of skip macroblocks in a frame, an adaptive threshold based on the ratio of BIM to BPM is used to determine the abrupt and the gradual scene changes respectively. Experimental results indicate that the proposed algorithm achieves the good performance with a low computational complexity.
changedetection between images from different perspectives is a difficult problem in building changedetection. At the same time, the quality of digital surface model (DSM) is very important for building change detec...
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changedetection between images from different perspectives is a difficult problem in building changedetection. At the same time, the quality of digital surface model (DSM) is very important for building changedetection. We propose a new building changedetection algorithm based on cross-temporal stereo matching (CTSM) to process images with different viewing angles using two temporal optical spaceborne stereo images. The algorithm matches two images with different temporal points and different viewing angles pixel by pixel to obtain unchanged regions and their elevations. Since the elevation of the unchanged area should be unchanged, we use the elevation obtained by CTSM to correct the existing two temporal DSM. The proposed algorithm can correct the wrong elevation of the unchanged area, recover the matching failure area and fill the occluded area to obtain a more complete and accurate DSM. Applying the refined DSM and unchanged area mask to three existing building change detection algorithms, the performance of all algorithms has been greatly improved. The false alarm rate of the result is greatly reduced while the detection rate of changed buildings keeps almost unchanged.
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