In this paper, we propose a novel changedetection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a thresho...
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ISBN:
(纸本)9781509052073
In this paper, we propose a novel changedetection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered from changes to the network structure. Thus, nodes and links should be monitored in order to recognize changes. However, it is difficult to focus on the appropriate nodes and links when there is little information regarding the dataset. Network centrality such as PageRank measures the importance of nodes in a network based on certain criteria. Therefore, it is natural to apply network centralities in order to improve the accuracy of changedetection methods. Our analysis reveals how and when network centrality works well in terms of changedetection. Based on this understanding, we propose an aggregating algorithm that emphasizes the appropriate network centralities. Our evaluation of the proposed aggregation algorithm showed highly accurate predictions for an artificial dataset and two real datasets. Our method contributes to extending the field of changedetection in temporal networks by utilizing network centralities.
The high cost of damaging an expensive robot or injuring people or equipment in its environment make even rare failures unacceptable in many mobile robot applications. Often the objects that pose the highest risk for ...
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The high cost of damaging an expensive robot or injuring people or equipment in its environment make even rare failures unacceptable in many mobile robot applications. Often the objects that pose the highest risk for a mobile robot are those that were not present throughout previous successful traversals of an environment. changedetection, a closely related problem to novelty detection, is therefore of high importance to many mobile robotic applications that require a robot to operate repeatedly in the same environment. We present a novel algorithm for performing online changedetection based on a previously developed robust online novelty detection system that uses a learned lower-dimensional representation of the feature space to perform measures of *** then further improve this changedetection system by incorporating online scene segmentation to better utilize contextual information in the environment. We validate these approaches through extensive experiments onboard a large outdoor mobile robot. Our results show that our approaches are robust to noisy sensor data and moderate registration errors and maintain their performance across diverse natural environments and conditions.
This paper introduces an original application for detecting changes related to diverse agricultural activities through the analysis of bitemporal Sentinel-2 satellite imagery. Operating without pre-existing samples, o...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
This paper introduces an original application for detecting changes related to diverse agricultural activities through the analysis of bitemporal Sentinel-2 satellite imagery. Operating without pre-existing samples, our approach generates pseudo-labels using common rule-based Earth Observation (EO) algorithms to identify cases of abrupt loss of vegetation in pairs of consecutive cloud-free images. These artificially generated samples form the basis for training several state-of-the-art changedetection (CD) methods. Evaluation on a small ground truth sample, annotated through photo-interpretation by experts, demonstrates our semi-supervised methodology’s high predictive accuracy for agricultural events detection across diverse terrains and cropping practices (i.e., mowing, grazing, harvest, plowing, stubble burning, etc.). The proposed implementation offers a cost-effective, scalable solution for real-time monitoring, providing valuable insights for agricultural activity and facilitating informed decision-making in farm management and biodiversity strategies.
The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense mov...
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The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are transmitted to the Visual Cortex via the optic nerve. The Hybrid Sensitive Motion Detector (HSMD) algorithm proposed in this work enhances the GSOC dynamic background subtraction (DBS) algorithm with a customised 3-layer spiking neural network (SNN) that outputs spiking responses akin to the OMS-GC. The algorithm was compared against existing background subtraction (BS) approaches, available on the OpenCV library, specifically on the 2012 changedetection (CDnet2012) and the 2014 changedetection (CDnet2014) benchmark datasets. The results show that the HSMD was ranked overall first among the competing approaches and has performed better than all the other algorithms on four of the categories across all the eight test metrics. Furthermore, the HSMD proposed in this paper is the first to use an SNN to enhance an existing state of the art DBS (GSOC) algorithm and the results demonstrate that the SNN provides near real-time performance in realistic applications.
In this paper we present a family of track-before-detect (TBD). procedures for early detection of moving targets from airborne radars. Upon a sectorization of the coverage area, the received echoes are jointly process...
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In this paper we present a family of track-before-detect (TBD). procedures for early detection of moving targets from airborne radars. Upon a sectorization of the coverage area, the received echoes are jointly processed in the azimuth-range-Doppler domain and in the time domain through a Viterbi-like algorithm that exploits the physically admissible target transitions between successive illuminations, in order to collect all of the energy back-scattered during the time on target (TOT). A reduced-complexity implementation is derived assuming, at the design stage, that the target does not change resolution cell during the TOT in each scan. The constant false alarm rate (CFAR) constraint is also englobed in the proposed procedures as well as the possibility of working with quantized data. Simulation results show that the proposed algorithms have good detection and tracking capabilities even for high target velocities and low quantization rates.
In the area of changedetection, there were a rare number of optimization methods. Most of the optimization methods that are used by changedetection are morphological transformation or median filtering, which cannot ...
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In the area of changedetection, there were a rare number of optimization methods. Most of the optimization methods that are used by changedetection are morphological transformation or median filtering, which cannot best optimize changedetection algorithm. In this paper, a general post-processing algorithm for changedetection is proposed. We believe that some problems cannot be avoided in the area of changedetection such as 1) region of moving object generated by changedetection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the changedetection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in *** are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://***/walty19950301/CDA-contour-optimizer.
The field of quickest changedetection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place, and identify properties of the post-change behavior. I...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
The field of quickest changedetection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place, and identify properties of the post-change behavior. It is shown in this paper that approaches based on reinforcement learning (RL) can be adapted based on any “surrogate information state” that is adapted to the observations. Hence we are left to choose both the surrogate information state process and the algorithm. For the former, it is argued that there are many choices available, based on a rich theory of asymptotic statistics for QCD. Two approaches to RL design are considered: (i) Stochastic gradient descent based on an actor-critic formulation. Theory is largely complete for this approach: the algorithm is unbiased, and will converge to a local minimum. However, it is shown that variance of stochastic gradients can be very large, necessitating the need for commensurately long run times. (ii) Q-learning algorithms based on a version of the projected Bellman equation. It is shown that the algorithm is stable, in the sense of bounded sample paths, and that a solution to the projected Bellman equation exists under mild conditions. Numerical experiments illustrate these findings, and provide a roadmap for algorithm design in more general settings.
Learning-based changedetection (CD) in water scenarios is a key functionality for unmanned aerial vehicle (UAV). However, computer vision algorithms require large number of labeled datasets. Inspired by parallel inte...
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Learning-based changedetection (CD) in water scenarios is a key functionality for unmanned aerial vehicle (UAV). However, computer vision algorithms require large number of labeled datasets. Inspired by parallel intelligence, we propose a systematic framework for data generation. In this work, the framework consists of simulated scene and image generation network. In simulated scene, simulated images with pixel-level annotations are automatically generated. Then, image generation network uses paired images (real and simulated) to generate synthetic images. We use simulated and synthetic images in combination with publicly available real-world images to conduct experiments. The experimental results indicate that: 1) simulated images can be used in changedetection research; 2) synthetic images effectively improve the performance of supervised changedetection model.
We propose a simple and energy efficient distributed changedetection scheme for sensor networks based on Page's parametric CUSUM algorithm. The sensor observations are IID over time and across the sensors conditi...
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We propose a simple and energy efficient distributed changedetection scheme for sensor networks based on Page's parametric CUSUM algorithm. The sensor observations are IID over time and across the sensors conditioned on the change variable. Each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a multiple access channel (MAC) corrupted with IID noise. The fusion center which is the global decision maker, performs another CUSUM to detect the change. We provide the analysis and simulation results for our scheme and compare the performance with an existing scheme which ensures energy efficiency via optimal power selection.
Semi-automatic flood extraction procedures based on changedetection techniques appear particularly adapted to plain flood monitoring and mapping. The change detector was specifically elaborated to analyse ENVISAT ASA...
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Semi-automatic flood extraction procedures based on changedetection techniques appear particularly adapted to plain flood monitoring and mapping. The change detector was specifically elaborated to analyse ENVISAT ASAR wide swath mode data pairs, which appear very well adapted to flood monitoring over wide areas. The algorithm is based on two analysis levels: an enhanced ratio for strong changes over large homogeneous and flat areas combined with a ratio calculated from the two raw images which aims to keep the raw data's thematic precision. Slope and aspect effects are also eliminated by the use of a digital elevation model during the processing. This changedetection analysis was performed on 35 data pairs acquired within the framework of the flood DRAGON project. The first set of results is very promising and robust using HH polarization. changedetection between data with W polarization or with different polarization (HH versus W) has to be fully validated but results are promising.
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