EYE DETECTION USING OPTIMAL WAVELET PACKETS AND RADIAL BASIS FUNCTIONS (RBFs)

The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to the anchoring on model-based schemes. This paper introduces a novel approach for the eye detection task using optimal wavelet packets for eye representation and Radial Basis Functions (RBFs) for subsequent classification ("labeling") of facial areas as eye versus non-eye regions. Entropy minimization is the driving force behind the derivation of optimal wavelet packets. It decreases the degree of data dispersion and it thus facilitates clustering ("prototyping") and capturing the most significant characteristics of the underlying (eye regions) data. Entropy minimization is thus functionally compatible with the first operational stage of the RBF classifier, that of clustering, and this explains the improved RBF performance on eye detection. Our experiments on the eye detection task prove the merit of this approach as they show that eye images compressed using optimal wavelet packets lead to improved and robust performance of the RBF classifier compared to the case where original raw images are used by the RBF classifier.

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INTERACTIVE REGISTRATION OF INTRACELLULAR VOLUMES WITH RADIAL BASIS FUNCTIONS

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026810002847 ◽

2010 ◽

Vol 09 (03) ◽

pp. 207-224 ◽

Cited By ~ 2

Author(s):

SHIN YOSHIZAWA ◽

SATOKO TAKEMOTO ◽

MIWA TAKAHASHI ◽

MAKOTO MUROI ◽

SAYAKA KAZAMI ◽

...

Keyword(s):

Image Registration ◽

Radial Basis Functions ◽

Plasma Membranes ◽

Mapping Function ◽

Basis Functions ◽

Cell Geometry ◽

Registration System ◽

Barycentric Interpolation ◽

Novel Approach ◽

Radial Basis

We propose a novel approach to 3D image registration of intracellular volumes. The approach extends a standard image registration framework to the curved cell geometry. An intracellular volume is mapped onto another intracellular domain by using two pairs of point set surfaces approximating their nuclear and plasma membranes. The mapping function consists of the affine transformation, tetrahedral barycentric interpolation, and least-squares formulation of radial basis functions for extracted cell geometry features. An interactive volume registration system is also developed based on our approach. We demonstrate that our approach is capable of creating cell models containing multiple organelles from observed data of living cells.

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Optimal Control of Time-Delay Fractional Equations via a Joint Application of Radial Basis Functions and Collocation Method

Entropy ◽

10.3390/e22111213 ◽

2020 ◽

Vol 22 (11) ◽

pp. 1213

Author(s):

Shu-Bo Chen ◽

Samaneh Soradi-Zeid ◽

Hadi Jahanshahi ◽

Raúl Alcaraz ◽

José Francisco Gómez-Aguilar ◽

...

Keyword(s):

Optimal Control ◽

Time Delay ◽

Collocation Method ◽

Radial Basis Functions ◽

Trajectory Optimization ◽

Basis Functions ◽

Fractional Equations ◽

Joint Application ◽

Novel Approach ◽

Radial Basis

A novel approach to solve optimal control problems dealing simultaneously with fractional differential equations and time delay is proposed in this work. More precisely, a set of global radial basis functions are firstly used to approximate the states and control variables in the problem. Then, a collocation method is applied to convert the time-delay fractional optimal control problem to a nonlinear programming one. By solving the resulting challenge, the unknown coefficients of the original one will be finally obtained. In this way, the proposed strategy introduces a very tunable framework for direct trajectory optimization, according to the discretization procedure and the range of arbitrary nodes. The algorithm’s performance has been analyzed for several non-trivial examples, and the obtained results have shown that this scheme is more accurate, robust, and efficient than most previous methods.

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Performance of Different Atrial Conduction Velocity Estimation Algorithms Improves with Knowledge about the Depolarization Pattern

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0026 ◽

2019 ◽

Vol 5 (1) ◽

pp. 101-104

Author(s):

Claudia Nagel ◽

Nicolas Pilia ◽

Laura Unger ◽

Olaf Dössel

Keyword(s):

Radial Basis Functions ◽

Conduction Velocity ◽

Estimation Algorithm ◽

Velocity Estimation ◽

Basis Functions ◽

Mean Velocity ◽

Estimation Algorithms ◽

Polynomial Fit ◽

Novel Approach ◽

Radial Basis

AbstractQuantifying the atrial conduction velocity (CV) reveals important information for targeting critical arrhythmia sites that initiate and sustain abnormal electrical pathways, e.g. during atrial flutter. The knowledge about the local CV distribution on the atrial surface thus enhances clinical catheter ablation procedures by localizing pathological propagation paths to be eliminated during the intervention. Several algorithms have been proposed for estimating the CV. All of them are solely based on the local activation times calculated from electroanatomical mapping data. They deliver false values for the CV if applied to regions near scars or wave collisions. We propose an extension to all approaches by including a distinct preprocessing step. Thereby, we first identify scars and wave front collisions and provide this information for the CV estimation algorithm. In addition, we provide reliable CV values even in the presence of noise. We compared the performance of the Triangulation, the Polynomial Fit and the Radial Basis Functions approach with and without the inclusion of the aforementioned preprocessing step. The evaluation was based on different activation patterns simulated on a 2D synthetic triangular mesh with different levels of noise added. The results of this study demonstrate that the accuracy of the estimated CV does improve when knowledge about the depolarization pattern is included. Over all investigated test cases, the reduction of the mean velocity error quantified to at least 25 mm/s for the Radial Basis Functions, 14 mm/s for the Polynomial Fit and 14 mm/s for the Triangulation approach compared to their respective implementations without the preprocessing step. Given the present results, this novel approach can contribute to a more accurate and reliable CV estimation in a clinical setting and thus improve the success of radio-frequency ablation to treat cardiac arrhythmias.

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