Kambiz Rahbar

Blind correction of lens aberration using Zernike moments

The quality of the image formed by an optical system is reduced by aberrations. This paper points out and attempts to correct blind of lens aberration. To this end Zernike moments introduced for presenting lens aberration model within which their coefficients are estimated through polyspectral analysis. The model parameters are divided into asymmetric and symmetric which are estimated through bicoherence and tricoherence respectively. The obtained precision compares favorably to the aberration given by state of the art ployspectral analysis and reaches a RMSE of 0.1 pixels.

Local Orthogonal Discriminate Bases to Hybrid SVM/Self-adaptive HMM Classifier for Discrete Word Speech Recognition

This research addresses speech recognition with discrete words and speaker independent by using hybrid support vector machine/self-adaptive hidden Markov model (SVM/self-adaptive HMM) classifier. Our proposed method includes two main units: preprocessing unit, and classification unit. In the first unit speech wave is converted into frames and then extracted time-frequency relevant features in the way that maximizing relative entropy of time-frequency energy distribution among frames. In the second unit a one-versus-one self-adaptive HMM calculates words likelihoods to each existed class and then a support vector machine (SVM) takes all class likelihood as an input vector and classifies it into proper class. To validate our proposed method, we test it on our IAUM dataset that contains Persian digits uttered by Persian speakers. The results are compared with similar method using original HMM

Phase wavefront aberration modeling using Zernike and pseudo-Zernike polynomials

Orthogonal polynomials can be used for representing complex surfaces on a specific domain. In optics, Zernike polynomials have widespread applications in testing optical instruments, measuring wavefront distributions, and aberration theory. This orthogonal set on the unit circle has an appropriate matching with the shape of optical system components, such as entrance and exit pupils. The existence of noise in the process of representation estimation of optical surfaces causes a reduction of precision in the process of estimation. Different strategies are developed to manage unwanted noise effects and to preserve the quality of the estimation. This article studies the modeling of phase wavefront aberrations in third-order optics by using a combination of Zernike and pseudo-Zernike polynomials and shows how this combination may increase the robustness of the estimation process of phase wavefront aberration distribution.

Increasing the stability of the estimation of phase wavefront aberration distribution function through nonlinear adaptive filtering

Small amounts of aberration can strongly influence optical systems’ performance. Thus, reducing the noise in the process of estimating the aberration distribution function is of prime importance. This article provides a solution for nonlinear adaptive filtering of wavelet coefficients with the aim of increasing the stability of the estimation process of phase aberration distribution function. It should be noted that the estimation process is based on the wavelet profilometry. The basic idea in this technique is based on the assumption that noise spectrum is widely distributed with small amounts. Thus, using nonlinear filtering may increase the stability of the extraction process of aberration function’s parameters. Considering the importance of determining the true threshold, based on the correlation of third-order cumulants, a new utility function is defined. Using this function, it is expected that the threshold would adapt itself to its optimum value. Simulation results confirmed the accuracy and efficiency of the proposed solution.