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An improved algorithm of pattern matching for information security audit system

Journal of Computational Information Systems 2008年第5期4卷 2221-2228页

作者： Ding, Lei Yu, Fei Xu, Cheng Yue, Guangxue School of Physics Science and Information Engineering Jishou University Jishou 416000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Post and Telecommunications Nanjing 210003 China College of Computer and Communication Hunan University Changsha 410082 China

At present, network information audit system is almost based on text information filtering. Text categorization is the basic technology of text information filtering. Pattern matching algorithms are very important for the rule based information audit system. It directly influences the accuracy and real-time performance of the system. The KMP and BM algorithms are introduced in this paper. Research is carried out to improve the BM algorithm and a better BM algorithm is proposed According to the situation that the system characters group is bigger and that characters of the pat tern are less, the thesis puts forward an improved string-matching algorithm. The results of our experiments prove that it promotes the precision of text categorization. © 2008 Binary Information Press.

关键词： Pattern matching

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Object tracking in hyperspectral videos with convolutional features and kernelized correlation filter

arXiv

引用

arXiv 2018年

作者： Qian, Kun Zhou, Jun Xiong, Fengchao Zhou, Huixin Du, Juan Lab of Optoelectronic Imaging and Image Processing Xidian University Xi’an China School of Information and Communication Technology Griffith University Brisbane Australia College of Computer Science Zhejiang University Hangzhou China

Target tracking in hyperspectral videos is a new research topic. In this paper, a novel method based on convolutional network and Kernelized Correlation Filter (KCF) framework is presented for tracking objects of interest in hyperspectral videos. We extract a set of normalized three-dimensional cubes from the target region as fixed convolution filters which contain spectral information surrounding a target. The feature maps generated by convolutional operations are combined to form a three-dimensional representation of an object, thereby providing effective encoding of local spectral-spatial information. We show that a simple two-layer convolutional networks is sufficient to learn robust representations without the need of offline training with a large dataset. In the tracking step, KCF is adopted to distinguish targets from neighboring environment. Experimental results demonstrate that the proposed method performs well on sample hyperspectral videos, and outperforms several state-of-the-art methods tested on grayscale and color videos in the same scene. Copyright © 2018, The Authors. All rights reserved.

关键词： Target tracking

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Possibilistic similarity based image classification

Possibilistic similarity based image classification

引用

2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UE Brest France

ISBN: (纸本)9789898565419

In this study, an approach for image classification based on possibilistic similarity is proposed. This approach, due to the use of possibilistic concepts, enables an important flexibility to integrate both contextual information and a priori knowledge. Possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability density functions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. Several measures of similarity between classes were tested in order to improve the discrimination between classes. The classification is then performed based on the principle of possibilistic similarity. Synthetic and real images are used in order to evaluate the performances of the proposed model.

关键词： Probability density function

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A method of pixel unmixing by classes based on the possibilistic similarity

A method of pixel unmixing by classes based on the possibili...

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3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en Sciences et Technologies de L'information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UEB Brest France

ISBN: (纸本)9789897580185

In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert's semantic knowledge (a priori knowledge) and the contextual information. Dubois-Prade's probability-possibility transformation is used to construct these possibility distributions starting from statistical information (learning areas delimitated by an expert for each thematic class in the analyzed scene) which serve, first, for the estimation of the probability density functions using the kernel density estimation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined thematic classes. The obtained similarity values are used in order to obtain the abundances of different classes in the considered pixel. Accuracy analysis of pixels unmixing demonstrates that the proposed approach represents an efficient estimator of their abundances of the predefined thematic classes and, in turn, higher classification accuracy is achieved. Synthetic images are used in order to evaluate the performances of the proposed approach. Copyright © 2014 SCITEPRESS.

关键词： Classification (of information)

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Iterative possibility distributions refining in pixel-based images classification framework

Iterative possibility distributions refining in pixel-based ...

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2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en Sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UEB Brest France

ISBN: (纸本)9789898565419

In this study, an incremental and iterative approach for possibility distributions estimation in pixel-based images classification context is proposed. This approach is based on the use of possibilistic reasoning in order to enrich a set of samples serving for the initial estimation of possibility distributions. The use of possibilistic concepts enables an important flexibility for the integration of a context-based additional semantic knowledge source formed by pixels belonging with high certainty to different semantic classes (called possibilistic seeds), into the available knowledge encoded by possibility distributions. Once possibilistic seeds are extracted, possibility distributions are incrementally updated and refined. Synthetic images composed of two thematic classes are generated in order to evaluate the performances of the proposed approach. Initial possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability distributions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. The possibilistic seeds extraction process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure.

关键词： Pixels

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Millimeter-wave radar image analysis for the traffic sensing 2

Millimeter-wave radar image analysis for the traffic sensing

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2nd Spring International Conference on Defence Technology, ICDT 2020

作者： Wu, H.M. Qi, F. Wang, J.K. Wang, Y. School of Communication Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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LDPC based error resilient audio signal processing for wireless communication

LDPC based error resilient audio signal processing for wirel...

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International Conference on Pervasive Computing (ICPC)

作者： Jaypal Baviskar Afshan Mulla Mugdha Gulati Punit Vaswani Amol Baviskar Department of Electrical Engineering Veermata Jijabai Technological Institute Mumbai India Department of Image Processing Intelligent Communication Lab Mumbai India Department of Electronics Engineering Universal College of Engineering Vasai India

ISBN: (纸本)9781479960545

The increasing growth of communication has fostered research on various methods to obviate noise from affecting the data transmitted over the link. Low Density Parity Check (LDPC) has gained popularity because of its property of exhibiting near Shannon limit performance when interpreted via iterative probabilistic algorithm. This paper presents a high performance and an enhanced LDPC based algorithm, for channel coding audio signals. The algorithm is designed so as to facilitate adaptive feature as a function of input audio size. It generates an encoded stream of data by arranging bits into data vectors and processing it with the designed LDPC matrix. The modulation scheme considered for transmitting the audio signal is Binary Phase Shift Keying (BPSK). The audio signal recovered at various Signal to Noise ratios (SNRs) is illustrated. The performance analysis of the algorithm delineates improved results for AWGN noise. The Bit Error Rate (BER) is estimated at different SNRs and BER Vs SNR graph is plotted to determine the efficacy of the algorithm.

关键词： Parity check codes Algorithm design and analysis Decoding Channel coding Signal processing algorithms Noise

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Incoherent receivers in alpha-stable impulsive noise

Incoherent receivers in alpha-stable impulsive noise

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MILCOM, Military communications Conference

作者： G.A. Tsihrintzis C.L. Nikias Communication Systems Lab Department of Electrical Engineering University of Virginia Charlottesville VA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We compute an incoherent receiver for demodulation of signals with random phase in additive impulsive noise modeled as a bivariate isotropic Cauchy process. Monte-Carlo simulation clearly shows that the proposed Cauchy receiver has significantly improved operating characteristic over the corresponding Gaussian receiver. Moreover, the Cauchy receiver is very robust in the entire class of bivariate isotropic symmetric alpha-stable impulsive noise. Application on real radar sea-clutter data corroborates our findings.

关键词： Interference Phase noise Demodulation Signal processing Additive noise Gaussian processes Testing Signal processing algorithms Computational modeling Noise robustness

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Signal detection in severely heavy-tailed radar clutter

Signal detection in severely heavy-tailed radar clutter

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Asilomar Conference on Signals, Systems & Computers

作者： G.A. Tsihrintzis P. Tsakalides C.L. Nikias Communication Systems Lab Department of Electrical Engineering University of Virginia Charlottesville VA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

Alpha-stable distributions have been recognized in the signal processing community as simple, yet very accurate, two-parameter statistical models for signals and noise that contain an impulsive component of various degrees of severity. On the basis of this finding, several signal processing problems have been addressed and solved within the framework of alpha-stable distributions and with the use of fractional, lower-order statistics. We attempt to popularize these new signal processing tools within the radar community. In particular, we evaluate the goodness-of-fit of alpha-stable models in the radar environment and test the performance of new signal processing algorithms for signal detection and classification on real radar, sea-clutter data.

关键词： Signal detection Radar signal processing Interference Signal processing Object detection Radar clutter Sensor arrays Signal processing algorithms Testing Statistics

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Derivatives and Inverse of Cascaded Linear+Nonlinear Neural Models

arXiv

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arXiv 2017年

作者： Martinez-Garcia, M. Cyriac, P. Batard, T. Bertalmío, M. Malo, J. Image Processing Lab. Univ. València Spain Instituto de Neurociencias CSIC Alicante Spain Information and Communication Technologies Dept. Univ. Pompeu Fabra Barcelona Spain

In vision science, cascades of Linear+Nonlinear transforms are very successful in modeling a number of perceptual experiences [1]. However, the conventional literature is usually too focused on only describing the forward input-output transform. Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse. The fundamental reason for this analytical treatment is that it offers useful analytical insight into the psychophysics, the physiology, and the function of the visual system. For instance, we show how the trends of the sensitivity (volume of the discrimination regions) and the adaptation of the receptive fields can be identified in the expression of the Jacobian w.r.t. the stimulus. This matrix also tells us which regions of the stimulus space are encoded more efficiently in multi-information terms. The Jacobian w.r.t. the parameters shows which aspects of the model have bigger impact in the response, and hence their relative relevance. The analytic inverse implies conditions for the response and model parameters to ensure appropriate decoding. From the experimental and applied perspective, (a) the Jacobian w.r.t. the stimulus is necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometrical properties, (b) the Jacobian matrices w.r.t. the parameters are convenient to learn the model from classical experiments or alternative goal optimization, and (c) the inverse is a promising model-based alternative to blind machine-learning methods for neural decoding that do not include meaningful biological information. The theory is checked by building and testing a vision model that actually follows the modular program suggested in [1]. Our illustrative derivable and invertible model consists of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking. To stress the generality of this modular setting we show exa

关键词： Mathematical transformations

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