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检索条件"主题词=Change Detection Algorithms"

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An Evolutionary Approach for Modeling Time Series

An Evolutionary Approach for Modeling Time Series

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International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Elena Bautu Andrei Bautu Henri Luchian Ovidius University of Constanta Constanta Romania Mircea cel Bătrân Naval Academy Constanta Romania Alexandru Ioan Cuza슠University Iasi Romania

change points in time series appear due to variations in the data generation process. We consider the problem of modeling time series generated by dynamic processes, and we focus on finding the change points using a specially tailored genetic algorithm. The algorithm employs a new representation, described in detail in the paper. Suitable genetic operators are also defined and explained. The results obtained on computer generated time series provide evidence that the approach can be used for change point detection, and has good potential for time series modeling.

关键词： Genetic algorithms Statistics Testing Time series analysis Neural networks Scientific computing change detection algorithms Pervasive computing Time measurement Petroleum

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Impact of Bit-Width Specification on the Memory Hierarchy for a Real-Time Video Processing System 06

Impact of Bit-Width Specification on the Memory Hierarchy fo...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： B. Thornberg M. O'Nils Mid-Sweden University Sundsvall SWEDEN

ISBN: (纸本)9783981080100

Bit-width specification will affect the total memory storage requirement of a video processing system. However, what is not so obvious is that the bit-width specification will also affect the design of the memory hierarchy. Experiments with a real-life surveillance system show how the optimal allocation of shift registers for the storage of intermediate results is sensitive to bit-widths. It is shown that the total on-chip memory storage requirement can be reduced by 61 percent compared to a non-optimal design

关键词： Real time systems Surveillance Digital signal processing Pixel Delay Cameras change detection algorithms Signal processing algorithms Clocks Pipelines

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A Virus detection System Based on Artificial Immune System

A Virus Detection System Based on Artificial Immune System

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International Conference on Computational Intelligence and Security

作者： Rui Chao Ying Tan Key Laboratory of Machine Perception Ministry of Education Peking University China Peking University Beijing Beijing CN

ISBN: (纸本)9781424454112

A virus detection system (VDS) based on artificial immune system (AIS) is proposed in this paper. VDS at first generates the detector set from virus files in the dataset, negative selection and clonal selection are applied to the detector set to eliminate autoimmunity detectors and increase the diversity of the detector set in the non-self space respectively. Two novel hybrid distances called hamming-max and shift r bit-continuous distance are proposed to calculate the affinity vectors of each file using the detector set. The affinity vectors of the training set and the testing set are used to train and test classifiers respectively. VDS compares the detection rates using three classifiers, k-nearest neighbor (KNN), RBF networks and SVM when the length of detectors is 32-bit and 64-bit. The experimental results show that the proposed VDS has a strong detection ability and good generalization performance.

关键词： Artificial immune systems Detectors Testing change detection algorithms Data mining Intrusion detection Computer security Computational modeling Computational intelligence Data security

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Cross-comparison and validation of MODIS AQUA cloud mask by using CLOUDSAT and CALIPSO datasets

Cross-comparison and validation of MODIS AQUA cloud mask by ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Daniela Di Rosa Claudia Notarnicola Francesco Posa Universita degli Studi di Bari Aldo Moro Bari Puglia IT Institute of Applied Remote Sensing EURAC Bolzano Italy

This paper presents a cross-comparison of the data acquired by the MODIS, CLOUDSAT and CALIPSO sensors in order to understand the limit of the developed cloud-mask algorithm and to provide a quantitative validation assessment by using exclusively remotely sensed data. The comparison has been carried out by considering both the cloud mask and the intermediate levels such as the brightness temperatures and the reflectance values for different channels from which the cloud mask is derived. The preliminary analysis indicates a general good agreement among the different sources. A main underestimation of cloud cover is present on the sea and especially for high thin clouds. First results indicate that in order to increase the cloud cover accuracy the threshold for the intermediate levels (brightness temperature and reflectance values) may be changed by taking into account also the cloud vertical profiles.

关键词： MODIS Clouds Testing Brightness temperature Reflectivity Atmosphere Earth Meteorology Snow change detection algorithms

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Face pose estimation and tracking using automatic 3D model construction

Face pose estimation and tracking using automatic 3D model c...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Pedro Jimenez Jesus Nuevo Luis M. Bergasa Department of Electronics University of Alcala Alcala de Henares Spain

This paper presents a method for robustly tracking and estimating the face pose of a person in both indoor and outdoor environments. The method is invariant to identity and that does not require previous training. A face model is automatically initialized and constructed on-line, when the face is frontal to the stereo camera system. To build the model, a fixed point distribution is superposed over the frontal face, and several appropriate points close to those locations are chosen for tracking. Using the stereo correspondence of the two cameras, the 3D coordinates of these points are extracted, and the 3D model is created. RANSAC and POSIT are used for tracking and 3D pose calculation at each frame. The approach runs in real time, and has been tested on sequences recorded in the laboratory and in a moving car.

关键词： Face detection Cameras Testing Eyes Robustness Support vector machines Computer vision change detection algorithms Application software Stereo vision

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Moving target tracking algorithm based on the confidence measure of motion vectors

Moving target tracking algorithm based on the confidence mea...

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IEEE International Conference on Image Processing

作者： Jin-Sung Lee Kwang-Yeon Rhee Seong-Dae Kim Visual Communications Laboratory Department of EECS Korea Advanced Institute of Science and Technology Taejon South Korea

change detection using a difference picture has been used to detect the location of moving targets and to track them. This method needs the assumption of a static camera, and global motion compensation is required in the case of a moving camera. We suggest a method for finding minimum bounding rectangles (MBRs) of moving targets in the image sequences using moving region detection, especially with a moving camera. If the global motion parameter is inaccurately estimated, the estimated locations of targets will not be accurate either. To alleviate this problem, we introduce the concept of the confidence measure and achieve more accurate estimation of global motion. With a moving camera, the new global motion estimation algorithm performs more precisely and it reduces the background compensation errors for change detection. Experimental results show that the proposed method successfully removes the background region and extracts MBRs of the targets.

关键词： Target tracking Motion measurement Motion estimation Cameras change detection algorithms Motion detection Object detection Motion compensation Image sequences Real time systems

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Towards Large-Scale Urban Flood Mapping Using Sentinel-1 Data

Towards Large-Scale Urban Flood Mapping Using Sentinel-1 Dat...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jie Zhao Xiao Xiang Zhu Chair of Data Science in Earth Observation Technical University of Munich Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Within the realm of deep learning techniques, numerous remote sensing applications can be effectively addressed using deep learning algorithms. However, there is a scarcity of studies in synthetic Aperture radar (SAR)-based urban flood mapping involving deep learning techniques, primarily due to two reasons. First, SAR-based urban flood mapping is inherently rooted in change detection, resulting in a complex multi-modality problem within the imbalance data. This complexity arises from the integration of SAR intensity, InSAR coherence, and even SAR phase information acquired from different polarizations (i.e., VV and VH polarization in Sentinel-1 data) both before and after the event. The second challenge is the absence of a benchmark dataset specifically designed for SAR-based urban flood mapping. In an effort to fill this gap, a benchmark dataset for large-scale flood mapping using Sentinel-1 data, which includes not only SAR intensity but also InSAR coherence, should be created. The SAR pre-processing should be carefully checked at the very beginning. With this aim, we tested the specking filter and kernel size selection for the SAR preprocessing for deep learning models. Through this initiative, we found that despeckling SAR intensity and selecting the kernel size in InSAR coherence calculation do not significantly affect the accuracy in deep learning-based urban flood mapping using Sentinel-1 data. The curated benchmark dataset will be presented in the final paper.

关键词： Deep learning change detection algorithms Sentinel-1 Coherence Benchmark testing Complexity theory Floods

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Automated Anomaly detection Using Time-Variant Normal Profiling

Automated Anomaly Detection Using Time-Variant Normal Profil...

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Proceedings of the Biannual World Automation Congress

作者： Jung Yeop Kim Rex E. Gantenbein Utica College Utica NY USA University of Wurzburg Laramie WY USA

Anomaly detection provides automated detection of unauthorized intrusion into a computer system by creating a normal profile of the system's behavior, then raising an alert when the system's behavior does not fit the system's normal profile. Approaches to anomaly detection that focus on investigating user's behavior typically assume that a user's command sequences will not vary significantly over time and so tend to flag "unusual" but safe activities as anomalies. We propose the use of "time-variant normal" user profiles that assume a user will change activities over time. The approach combines string-matching algorithms from machine intelligence and sequence alignment algorithms from biomedical informatics to dynamically evaluate user behavior.

关键词： Intrusion detection Protection Computerized monitoring change detection algorithms Biomedical computing Condition monitoring Automation Educational institutions Machine intelligence Biomedical informatics

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Multi-Camera Scene Analysis using an Object-Centric Continuous Distribution Hidden Markov Model

Multi-Camera Scene Analysis using an Object-Centric Continuo...

引用

IEEE International Conference on Image Processing

作者： Murtaza Taj Andrea Cavallaro Multimedia and Vision Group-Queen Mary University of London UK

We propose a multi-camera event detection framework that can operate on a common ground plane as well as on the image plane. The proposed event detector is based on an object-centric state modeling that uses a continuous distribution hidden Markov model (CDHMM). Video objects are first detected using statistical change detection and then tracked using graph matching. Next, the algorithm recognizes events by estimating the most likely object state sequence using a HMM decoding strategy, based on the Viterbi algorithm. We demonstrate and evaluate the proposed framework on standard event detection datasets with single and multiple cameras, with both overlapping and non-overlapping fields of view.

关键词： Image analysis Hidden Markov models Event detection Object detection Detectors change detection algorithms State estimation Decoding Viterbi algorithm Cameras

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Fuzzy shot clustering to support networked video databases

Fuzzy shot clustering to support networked video databases

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： S. Auephanwiriyakul A. Joshi R. Krishnapuram University of Missouri Columbia MO USA

Video querying involves a lot of user interaction and feedback based query refinement, which can generate large traffic volumes on the network if full video segments are sent. For efficient video browsing, search and retrieval, one need to find good compact representations for long video sequences. Representative frames (Rframes) provide such a representation. Extant algorithms use scene change detection to segment video into shots and pick Rframes. However, scene change detection techniques fail badly in presence of gradual scene changes which are quite prevalent in most videos. We present another way of finding Rframe using fuzzy clustering without dealing with any scene change detection algorithms. Fuzzy clusters provide a more natural approach to this problem since membership of a frame in some particular scene is not binary. This allows one to handle gradual scene changes. We report on our approach, the initial experimental results and future plans.

关键词： Layout Video on demand Video compression Visual databases Video sequences change detection algorithms detection algorithms Feedback Clustering algorithms Gunshot detection systems

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