This paper proposes a novel kernel changedetection algorithm (KCD). The input vectors from two images of different times are mapped into a potential much higher dimensional feature space via a nonlinear mapping, whic...
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This paper proposes a novel kernel changedetection algorithm (KCD). The input vectors from two images of different times are mapped into a potential much higher dimensional feature space via a nonlinear mapping, which will usually increase the linear margin of change and no-change regions. Then a simple linear distance measure between two high dimensional feature vectors is defined in features space, which corresponds to the complicated nonlinear distance measure in input space. Furthermore the distance measure's dot product is expressed in the combination of kernel functions and large numbers of dot product processed in input space by combined kernel tactic, which avoids the computational load. Finally this paper takes the soft margin single-class support vector machine (SVM) to select the optimal hyper-plane with maximum margin. Preliminary results show the kernel changedetection algorithm (KCD) has excellent performance in accuracy.
A hidden semi-Markov model is proposed to describe a trend signal for non-invasive mean blood pressure. Based on the model, a Viterbi beam search algorithm working with an adaptive cumulative sum test is proposed to d...
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A hidden semi-Markov model is proposed to describe a trend signal for non-invasive mean blood pressure. Based on the model, a Viterbi beam search algorithm working with an adaptive cumulative sum test is proposed to detect change points and recognize change patterns online. Testing results on the simulated signals and clinical signals demonstrate that the algorithm has improved performance over the standard cumulative sum test for changedetection, and has the potential to provide a clinically relevant description of trend changes
The paper proposes a novel change-detection algorithm for automated video surveillance applications. The algorithm is based on the idea of incorporating into the background model a set of simple low-level features cap...
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The paper proposes a novel change-detection algorithm for automated video surveillance applications. The algorithm is based on the idea of incorporating into the background model a set of simple low-level features capable of capturing effectively "structural" (i.e. robust with respect to illumination variations) information. Thanks to this approach, and unlike most conventional change-detectionalgorithms, the proposed algorithm is capable of handling correctly still and slow objects as well as of working properly throughout very long time spans. Moreover, the algorithm can naturally interact with the higher-level processing modules found in advanced video-based surveillance systems in order to allow for flexible and intelligent background maintenance.
Post-Classification Comparison(PCC) method is widely used in changedetection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during...
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ISBN:
(纸本)9781479979301
Post-Classification Comparison(PCC) method is widely used in changedetection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during changedetection, which leads to the excessive evaluation of changed types and quantity. To solve this problem, this paper proposes a changedetection method for remote sensing images based on Adaptive Resonance Theory Mapping (ARTMAP) neural network. Similarity matrix is constructed by spectral feature vectors. Then the threshold value of similarity is obtained, which is used to control the joint-classification classifier based on the ARTMAP neural network. In addition, an adaptive algorithm of vigilance parameter is introduced to the classification process of fuzzy ARTMAP neural network. The experimental results obtained on remote sensing images show that the proposed method not only accurately classifies the unchanged geographical information in different temporal images into the same class, but also reduces the cumulative error and improves the accuracy of changedetection compared with other methods.
We present a novel approach to the changedetection problem based on a coarse-to-fine strategy. The basic idea consists in assigning to an efficient preliminary coarse-level detection the task to filter out the well k...
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We present a novel approach to the changedetection problem based on a coarse-to-fine strategy. The basic idea consists in assigning to an efficient preliminary coarse-level detection the task to filter out the well known possible false changes (e.g., those due to camera noise and small displacements, or to scene illumination changes). This provides the subsequent fine-level detection with reliable supermasks of the true changed areas in the scene. In this way, the fine-level detection can "focus the attention" on limited parts of the frames, thus yielding remarkable advantages in terms of computational efficiency. Here, just a coarse-level detection algorithm based on background subtraction and on the concept of structure is presented, to stress that any pixel-level algorithm can be used afterwards and benefit in terms of robustness as well as of computational efficiency.
In this paper, a novel one-pass, real-time approach to video scene changedetection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Scene changedetection is cr...
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In this paper, a novel one-pass, real-time approach to video scene changedetection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Scene changedetection is crucial in that it enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. We use statistical sequential analysis to provide a unified framework for robust and effective detection of both abrupt and gradual scene changes.
Because each pixel of a hyperspectral image contains so much information, many (successful) algorithms treat those pixels as independent samples, despite the evident spatial structure in the imagery. One way to exploi...
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ISBN:
(纸本)9781479902460
Because each pixel of a hyperspectral image contains so much information, many (successful) algorithms treat those pixels as independent samples, despite the evident spatial structure in the imagery. One way to exploit this structure is to incorporate spatial processing into pixel-wise anomalous change detection algorithms. But if this is done in the most straightforward way, a contaminated cross-covariance is produced. A spatial processing framework is proposed that avoids this contamination and enhances the performance of anomalous change detection algorithms in hyperspectral imagery.
Internet of Things (IoT) is one of the main technological trends in the recent years. It allows machine-to-machine communication over the internet. Almost each device may transmit information from its sensors over the...
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ISBN:
(纸本)9781479999897
Internet of Things (IoT) is one of the main technological trends in the recent years. It allows machine-to-machine communication over the internet. Almost each device may transmit information from its sensors over the web to enable centralized insights derivation in an appropriate cloud architecture. In this paper we review analytical aspects of the sensory information processing. We emphasize the importance of multisensory approach, in which the joint distribution of all sensors values of a device is used to derive insights out of the stream of sensory data. We introduce a novel information theoretic multivariate changedetection method based on k-nearest neighbor (kNN) estimation. The algorithm is designed and implemented to satisfy the requirements of IoT for fast online parallel multisensory information processing. We provide a numerical evidence of the validity of the proposed method on simulated and real world data.
Automatic localization of singular points in fingerprints is of critical importance in many algorithms. Existing methods of detecting singular points often require tedious ad-hoc parameter tuning, particularly in the ...
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Automatic localization of singular points in fingerprints is of critical importance in many algorithms. Existing methods of detecting singular points often require tedious ad-hoc parameter tuning, particularly in the presence of degraded quality fingerprints. In this paper we present an approach towards singular point detection in fingerprints that operates on the quadrant change information and is largely insensitive to the degradation of fingerprint quality
Several changedetection methods have been developed over the last decades and an even higher number during the last years due to the opening of the Landsat archive. Some changedetection methods aim at all types of l...
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ISBN:
(纸本)9781479979301
Several changedetection methods have been developed over the last decades and an even higher number during the last years due to the opening of the Landsat archive. Some changedetection methods aim at all types of land surface alterations while others target specific types such as inundations, urbanization or forest cover change or even more specifically forest cover loss. Many methods were developed and tested for temperate regions where most cloud-free data are obtained during the growing season. In the tropics, however, cloud cover is highest during the period when vegetation is most active. This study tests two common approaches, the Vegetation change Tracker (VCT) and the Iterative Multivariate Alteration detection (IMAD) in the northwestern portion of the Yucatan peninsula using Landsat images. Results from changedetection algorithm were compared to reference samples and reference polygons. Various parameter sets for the VCT algorithm never reach the accuracy level of IMAD.
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