2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective

Discrete-wavelet-transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix

The recursive-least-squares (RLS) algorithm was introduced as an alternative to LMS algorithm with enhanced performance. Computational complexity and instability in updating the autocolleltion matrix are some of the drawbacks of the RLS algorithm that were among the reasons for the intrduction of the second-order recursive inverse (RI) adaptive algorithm. The 2nd order RI adaptive algorithm suffered from low convergence rate in certain scenarios that required a relatively small initial step-size. In this paper, we propose a newsecond-order RI algorithm that projects the input signal to a new domain namely discrete-wavelet-transform (DWT) as pre step before performing the algorithm. This transformation overcomes the low convergence rate of the second-order RI algorithm by reducing the self-correlation of the input signal in the mentioned scenatios. Expeirments are conducted using the noise cancellation setting. The performance of the proposed algorithm is compared to those of the RI, original second-order RI and RLS algorithms in different Gaussian and impulsive noise environments. Simulations demonstrate the superiority of the proposed algorithm in terms of convergence rate comparedto those algorithms

On adaptive filter structure and performance

SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The Use of Recursive Least Squares Method for Vehicle Dynamics Analysis

Tato diplomová práce nastiňuje teoretické základy potřebné pro návrh algoritmu rekurzivní metody nejmenších čtverců a následně jeho aplikaci na experimentální data naměřená při testovacím manévru uskutečněném v roce 2001. Analyzována byla příčná dynamika jednostopého rovinného modelu vozidla. Práce také obsahuje srovnání výsledků získaných jednak rekurzivním algoritmem a dále i algoritmem Kalmanova filtru.This diploma thesis amplies the theoretical bases required to design the recursive least squares algorithm and, in consequence, its application to the experimental data measured during test manoeuvre realized in 2001. A lateral dynamics of single-track planar model of vehicle was analyzed. It contains also a comparing of the results obtained by the recursive algorithm and Kalman lter algorithm.

Visualization on colour based flow vector of thermal image for movement detection during interactive session

Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine

Narrow-band interference rejection in spread spectrum using an eigen analysis based approach

A new adaptive technique is suggested for rejecting narrow-band interferences in spread spectrum communications. When data is coded using a pseudo-noise code, the received signal consists of a wide-band signal with almost white spectral properties, thermal noise, and correlated narrow-band interferences. A new approach is proposed which exploits the statistical properties of the received signal via eigenanalysis of the received data. While the energy of the wide-band signal is distributed over all the eigenvalues of the signal autocorrelation matrix, the energy of the interference is concentrated in a few large eigenvalues. Hence, the eigenvectors corresponding to the large eigenvalues are termed the interference subspace. The proposed method derives a. weight vector residing in the subspace spanned by the rest of the eigenvectors termed the noise subspace. Consequently, it is orthogonal to the interference subspace. The eigenanalysis based interference cancellation is sub-optimal in a known signal environment, but is superior to the Wiener-Hopf filter when the signal statistics are estimated from a limited amount of data. A fast and effective adaptive algorithm is derived using the power method