Search results for: Foveation Filtering

Authors: S.K. Satpathy, S.K. Nayak, K. K. Nagwanshi, S. Panda, C. Ardil

Abstract:

Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. The proposed method, which makes use of an a priori noise model, has been evaluated on various types of images. Bayesian based algorithms and technique of image processing have been described and substantiated with experimentation using MATLAB.

Keywords: Image Restoration, Probability DensityFunction (PDF), Neural Networks, Bayesian Classifier.

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Authors: S. B. Kotsiantis, D. Kanellopoulos, P. E. Pintelas

Abstract:

Many factors affect the success of Machine Learning (ML) on a given task. The representation and quality of the instance data is first and foremost. If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. It is well known that data preparation and filtering steps take considerable amount of processing time in ML problems. Data pre-processing includes data cleaning, normalization, transformation, feature extraction and selection, etc. The product of data pre-processing is the final training set. It would be nice if a single sequence of data pre-processing algorithms had the best performance for each data set but this is not happened. Thus, we present the most well know algorithms for each step of data pre-processing so that one achieves the best performance for their data set.

Keywords: Data mining, feature selection, data cleaning.

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Authors: R. Suszynski, K. Wawryn, R. Wirski

Abstract:

The new concept of two–dimensional (2D) image processing implementation for auto-guiding system is shown in this paper. It is dedicated to astrophotography and operates with astronomy CCD guide cameras or with self-guided dual-detector CCD cameras and ST4 compatible equatorial mounts. This idea was verified by MATLAB model, which was used to test all procedures and data conversions. Next the circuit prototype was implemented at Altera MAX II CPLD device and tested for real astronomical object images. The digital processing speed of CPLD prototype board was sufficient for correct equatorial mount guiding in real-time system.

Keywords: DSO astrophotography, image processing, twodimensionalconvolution method, two-dimensional filtering.

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Authors: S. Park, J. Hwang, K. Rou, E. Kim

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In this paper, we consider a new particle filter inspired by biological evolution. In the standard particle filter, a resampling scheme is used to decrease the degeneracy phenomenon and improve estimation performance. Unfortunately, however, it could cause the undesired the particle deprivation problem, as well. In order to overcome this problem of the particle filter, we propose a novel filtering method called the genetic filter. In the proposed filter, we embed the genetic algorithm into the particle filter and overcome the problems of the standard particle filter. The validity of the proposed method is demonstrated by computer simulation.

Keywords: Particle filter, genetic algorithm, evolutionary algorithm.

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Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

Abstract:

The edges of low contrast images are not clearly distinguishable to human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: Chebyshev polynomials, Fractional order differentiator, Laplacian of Gaussian (LoG) method, Low contrast image.

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Authors: S. A. Banani, M. A. Masnadi-Shirazi

Abstract:

This paper presents a new algorithm which yields a nonlinear state estimator called iterated unscented Kalman filter. This state estimator makes use of both statistical and analytical linearization techniques in different parts of the filtering process. It outperforms the other three nonlinear state estimators: unscented Kalman filter (UKF), extended Kalman filter (EKF) and iterated extended Kalman filter (IEKF) when there is severe nonlinearity in system equation and less nonlinearity in measurement equation. The algorithm performance has been verified by illustrating some simulation results.

Keywords: Extended Kalman Filter, Iterated EKF, Nonlinearstate estimator, Unscented Kalman Filter.

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Authors: Mounira RouaÐùnia, Noureddine Doghmane

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In this paper we consider the problem of change detection and non stationary signals tracking. Using parametric estimation of signals based on least square lattice adaptive filters we consider for change detection statistical parametric methods using likelihood ratio and hypothesis tests. In order to track signals dynamics, we introduce a compensation procedure in the adaptive estimation. This will improve the adaptive estimation performances and fasten it-s convergence after changes detection.

Keywords: Change detection, Hypothesis test, likelihood ratioleast square lattice adaptive filters.

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Authors: Jobby K. Chacko, Deepu Oommen, Kevin K. Mathew, Noble Sunny, N. Babu

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A new cost effective, eye controlled method was introduced to guide and control a wheel chair for disable people, based on Electrooculography (EOG). The guidance and control is effected by eye ball movements within the socket. The system consists of a standard electric wheelchair with an on-board microcontroller system attached. EOG is a new technology to sense the eye signals for eye movements and these signals are captured using electrodes, signal processed such as amplification, noise filtering, and then given to microcontroller which drives the motors attached with wheel chair for propulsion. This technique could be very useful in applications such as mobility for handicapped and paralyzed persons.

Keywords: Electrooculography, Microcontroller, Signal processing, Wheelchair.

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Authors: Mehran Yazdi, Zahra Adelpour, Batoul Bahraini, Yasaman Keshtkar Jahromi

Abstract:

In this paper we use the property of co-occurrence matrix in finding parallel lines in binary pictures for fingerprint identification. In our proposed algorithm, we reduce the noise by filtering the fingerprint images and then transfer the fingerprint images to binary images using a proper threshold. Next, we divide the binary images into some regions having parallel lines in the same direction. The lines in each region have a specific angle that can be used for comparison. This method is simple, performs the comparison step quickly and has a good resistance in the presence of the noise.

Keywords: Parallel lines detection, co-occurrence matrix, fingerprint identification.

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Authors: Efstratios Doukakis, Nikolaos Petrelis

Abstract:

All climate models agree that the temperature in Greece will increase in the range of 1° to 2°C by the year 2030 and mean sea level in Mediterranean is expected to rise at the rate of 5 cm/decade. The aim of the present paper is the estimation of the coastline displacement driven by the climate change and sea level rise. In order to achieve that, all known statistical and non-statistical computational methods are employed on some Greek coastal areas. Furthermore, Kalman filtering techniques are for the first time introduced, formulated and tested. Based on all the above, shoreline change signals and noises are computed and an inter-comparison between the different methods can be deduced to help evaluating which method is most promising as far as the retrieve of shoreline change rate is concerned.

Keywords: Climate Change, Coastal Displacement, KalmanFilter

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Authors: Ephraim Gower, Thato Tsalaile, Monageng Kgwadi, Malcolm Hawksford.

Abstract:

In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products.

Keywords: Circular Convolution, linear Convolution, mixture density function.

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Authors: Rajoo Pandey

Abstract:

The performance of an image filtering system depends on its ability to detect the presence of noisy pixels in the image. Most of the impulse detection schemes assume the presence of salt and pepper noise in the images and do not work satisfactorily in case of uniformly distributed impulse noise. In this paper, a new algorithm is presented to improve the performance of switching median filter in detection of uniformly distributed impulse noise. The performance of the proposed scheme is demonstrated by the results obtained from computer simulations on various images.

Keywords: Switching median filter, Impulse noise, Imagefiltering, Impulse detection.

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Authors: Khaled M. EL-Naggar, Khaled A. AL-Rumaih

Abstract:

This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.

Keywords: Forecasting, Least error squares, Least absolute Value, Genetic algorithms

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Authors: V. Praveena, N. Kiruthika

Abstract:

In this paper, we explore a new scheme for filtering spoofed packets (DDOS attack) which is a combination of path fingerprint and client puzzle concepts. In this each IP packet has a unique fingerprint is embedded that represents, the route a packet has traversed. The server maintains a mapping table which contains the client IP address and its corresponding fingerprint. In ingress router, client puzzle is placed. For each request, the puzzle issuer provides a puzzle which the source has to solve. Our design has the following advantages over prior approaches, 1) Reduce the network traffic, as we place a client puzzle at the ingress router. 2) Mapping table at the server is lightweight and moderate.

Keywords: Client puzzle, DDOS attack, Egress, Ingress, IP Spoofing, Spoofed Packet.

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Authors: Nikolay Nikolaev, Evgueni Smirnov

Abstract:

This paper develops an unscented grid-based filter and a smoother for accurate nonlinear modeling and analysis of time series. The filter uses unscented deterministic sampling during both the time and measurement updating phases, to approximate directly the distributions of the latent state variable. A complementary grid smoother is also made to enable computing of the likelihood. This helps us to formulate an expectation maximisation algorithm for maximum likelihood estimation of the state noise and the observation noise. Empirical investigations show that the proposed unscented grid filter/smoother compares favourably to other similar filters on nonlinear estimation tasks.

Keywords:

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Authors: Young-Seok Choi

Abstract:

We present a new subband adaptive filter (R-SAF) which is robust against impulsive noise in system identification. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion against impulsive noise, the R-SAF comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to the robustness against the impulsive noise and the capable convergence performance. Experimental results clearly demonstrate that the proposed R-SAF outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.

Keywords: Subband adaptive filter, L1-norm, system identification, robustness, impulsive interference.

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Authors: Anatoli Torokhti, Stanley Miklavcic

Abstract:

Suppose KY and KX are large sets of observed and reference signals, respectively, each containing N signals. Is it possible to construct a filter F : KY → KX that requires a priori information only on few signals, p  N, from KX but performs better than the known filters based on a priori information on every reference signal from KX? It is shown that the positive answer is achievable under quite unrestrictive assumptions. The device behind the proposed method is based on a special extension of the piecewise linear interpolation technique to the case of random signal sets. The proposed technique provides a single filter to process any signal from the arbitrarily large signal set. The filter is determined in terms of pseudo-inverse matrices so that it always exists.

Keywords: Wiener filter, filtering of stochastic signals.

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Authors: S. Al Wadi, Mohd Tahir Ismail, Samsul Ariffin Abdul Karim

Abstract:

It is well known that during the developments in the economic sector and through the financial crises occur everywhere in the whole world, volatility measurement is the most important concept in financial time series. Therefore in this paper we discuss the volatility for Amman stocks market (Jordan) for certain period of time. Since wavelet transform is one of the most famous filtering methods and grows up very quickly in the last decade, we compare this method with the traditional technique, Fast Fourier transform to decide the best method for analyzing the volatility. The comparison will be done on some of the statistical properties by using Matlab program.

Keywords: Fast Fourier transforms, Haar wavelet transform, Matlab (Wavelet tools), stocks market, Volatility.

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Authors: Guezouli Larbi, Belkacem Soundes

Abstract:

The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered as a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: Text detection, CNN, PZM, deep learning.

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Authors: Mario Mastriani

Abstract:

We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only the approximation subband coefficients and three scalars are stored and/or transmitted to the channel. Besides, with the elimination of the details subbands coefficients, we obtain a bigger compression rate. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.

Keywords: Compression, denoising, projections, wavelets.

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Authors: Vishal Awasthi, Krishna Raj

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In this paper, firstly, we present the mathematical modeling of finite impulse response (FIR) filter and Cascaded Integrator Comb (CIC) filter for sampling rate reduction and then an extension of Canonical signed digit (CSD) based efficient structure is presented in framework using hybrid signed digit (HSD) arithmetic. CSD representation imposed a restriction that two non-zero CSD coefficient bits cannot acquire adjacent bit positions and therefore, represented structure is not economical in terms of speed, area and power consumption. The HSD based structure gives optimum performance in terms of area and speed with 37.02% passband droop compensation.

Keywords: Multirate filtering, compensation theory, CIC filter, compensation filter, signed digit arithmetic, canonical signed digit.

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Authors: Nur Ilyani Ramli, Mansour Youseffi, Peter Widdop

Abstract:

This paper presents a low cost design of heart beat monitoring device using reflectance mode PhotoPlethysmography (PPG). PPG is known for its simple construction, ease of use and cost effectiveness and can provide information about the changes in cardiac activity as well as aid in earlier non-invasive diagnostics. The proposed device is divided into three phases. First is the detection of pulses through the fingertip. The signal is then passed to the signal processing unit for the purpose of amplification, filtering and digitizing. Finally the heart rate is calculated and displayed on the computer using parallel port interface. The paper is concluded with prototyping of the device followed by verification procedure of the heartbeat signal obtained in laboratory setting.

Keywords: Reflectance mode PPG, Heart beat detection, Circuitdesign, PCB design

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Authors: Apkar Salatian

Abstract:

To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: Design Pattern, filtering, compression.

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Authors: Madhav V. Chitturi, Anshu Manik, Kasthurirangan Gopalakrishnan

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The dynamic or complex modulus test is considered to be a mechanistically based laboratory test to reliably characterize the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes used in the construction of roads. The most common observation is that the data collected from these tests are often noisy and somewhat non-sinusoidal. This hampers accurate analysis of the data to obtain engineering insight. The goal of the work presented in this paper is to develop and compare automated evolutionary computational techniques to filter test noise in the collection of data for the HMA complex modulus test. The results showed that the Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is computationally efficient for filtering data obtained from the HMA complex modulus test.

Keywords: HMA, dynamic modulus, GA, evolutionarycomputation.

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Authors: Pablo Barrera, Jose M. Canas, Vicente Matellan

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This paper addresses the problem of determining the current 3D location of a moving object and robustly tracking it from a sequence of camera images. The approach presented here uses a particle filter and does not perform any explicit triangulation. Only the color of the object to be tracked is required, but not any precisemotion model. The observation model we have developed avoids the color filtering of the entire image. That and the Monte Carlotechniques inside the particle filter provide real time performance.Experiments with two real cameras are presented and lessons learned are commented. The approach scales easily to more than two cameras and new sensor cues.

Keywords: Monte Carlo sampling, multiple view, particle filters, visual tracking.

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Authors: Sangjun Park, Jungpyo Hong, Byung-Ok Kang, Yun-keun Lee, Minsoo Hahn

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In this paper, an algorithm for detecting and attenuating puff noises frequently generated under the mobile environment is proposed. As a baseline system, puff detection system is designed based on Gaussian Mixture Model (GMM), and 39th Mel Frequency Cepstral Coefficient (MFCC) is extracted as feature parameters. To improve the detection performance, effective acoustic features for puff detection are proposed. In addition, detected puff intervals are attenuated by high-pass filtering. The speech recognition rate was measured for evaluation and confusion matrix and ROC curve are used to confirm the validity of the proposed system.

Keywords: Gaussian mixture model, puff detection and cancellation, speech enhancement.

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Authors: Robert E. Hursig, Jane X. Zhang

Abstract:

A robust still image face localization algorithm capable of operating in an unconstrained visual environment is proposed. First, construction of a robust skin classifier within a shifted HSV color space is described. Then various filtering operations are performed to better isolate face candidates and mitigate the effect of substantial non-skin regions. Finally, a novel Bhattacharyya-based face detection algorithm is used to compare candidate regions of interest with a unique illumination-dependent face model probability distribution function approximation. Experimental results show a 90% face detection success rate despite the demands of the visually noisy environment.

Keywords: Audio-visual speech recognition, Bhattacharyyacoefficient, face detection,

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Authors: Leila Graini

Abstract:

In this paper, we demonstrate basic all-optical functions for 2R regeneration (Re-amplification and Re-shaping) based on self-similar spectral broadening in low normal dispersion and highly nonlinear fiber (ND-HNLF) to regenerate the signal through optical filtering including the transfer function characteristics, and output extinction ratio. Our approach of all-optical 2R regeneration is based on those of Mamyshev. The numerical study reveals the self-similar spectral broadening very effective for 2R all-optical regeneration; the proposed design presents high stability compared to a conventional regenerator using SPM broadening with reduction of the intensity fluctuations and improvement of the extinction ratio.

Keywords: All-optical function, 2R optical regeneration, self-similar broadening, Mamyshev regenerator.

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Authors: T. Messikh, S. Mekhilef, N. A. Rahim

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This paper presents an effective technique for harmonic current mitigation using an adaptive notch filter (ANF) to estimate current harmonics. The proposed filter consists of multiple units of ANF connected in parallel structure; each unit is governed by two ordinary differential equations. The frequency estimation is carried out based on the output of these units. The simulation and experimental results show the ability of the proposed tracking scheme to accurately estimate harmonics. The proposed filter was implemented digitally in TMS320F2808 and used in the control of hybrid active power filter (HAPF). The theoretical expectations are verified and demonstrated experimentally.

Keywords: Adaptive notch filter, Active power filter, harmonic filtering, Time varying frequency.

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Authors: Saman M. Siddiqui, Fang Jiancheng

Abstract:

Micro electromechanical sensors (MEMS) play a vital role along with global positioning devices in navigation of autonomous vehicles .These sensors are low cost ,easily available but depict colored noises and unpredictable discontinuities .Conventional filters like Kalman filters and Sigma point filters are not able to cope with nonwhite noises. This research has utilized H∞ filter in nonlinear frame work both with Kalman filter and Unscented filter for navigation and self alignment of an airborne vehicle. The system is simulated for colored noises and discontinuities and results are compared with not robust nonlinear filters. The results are found 40%-70% more robust against colored noises and discontinuities.

Keywords: filtering, integrated navigation, MEMS, nonlinearfiltering, self alignment

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