**7. References**


210 Bio-Inspired Computational Algorithms and Their Applications

In this chapter, a database for visual and thermal images was created and several techniques were developed to improve image quality as an effort to address the illumination challenge

Firstly, one image enhancement algorithm was designed to improve the images' visual quality. Experimental results showed that the enhancement algorithm performed well and provided good results in terms of both luminance and contrast enhancement. In the luminance enhancement part, it has been shown that the proposed algorithm worked well for both dark and bright images. In the contrast enhancement part, it was proven that the proposed nonlinear transfer functions could make unseen or barely seen features in low

Secondly, the IR and enhanced visual images taken from different sensors, viewpoints, times and resolution were registered. A correspondence between an IR and a visual image was established based on a set of image features detected by the Harris Corner detection algorithm in both images. A spatial transformation matrix was determined based on some manually chosen corners and the transformation matrix was utilized for the registration.

Finally, a continuous genetic algorithm was developed for image fusion. The continuous GA has the advantage of less storage requirements than the binary GA and is inherently faster than the binary GA because the chromosomes do not have to be decoded prior to the

Data fusion provides an integrated image from a pair of registered and enhanced visual and thermal IR images. The fused image is invariant to illumination directions and is robust under low lighting conditions. They have potentials to significantly boost the performances of face recognition systems. One of the major obstacles in face recognition using visual images is the illumination variation. This challenge can be mitigated by using infrared (IR) images. On the other hand, using IR images alone for face recognition is usually not feasible because they do not carry enough detailed information. As a remedy, a hybrid system is presented that may benefit from both visual and IR images and improve face recognition

Bowyer W., Chang K. and Flynn P., A Survey of Approaches To Three-Dimensional Face

Dasarathy B. V., Image Fusion in the Context Of Aerospace Applications, *Inform. Fusion*, Vol.

Erkanli S. and Rahman Zia-Ur., Enhancement Technique for Uniformly and Non-Uniformly

Erkanli S. and Rahman Zia-Ur., Wavelet Based Enhancement for Uniformly and Non-Uniformly Illuminated Dark Images, *ISDA 2010, Cairo, Egypt*, 2010c. Erkanli S.and Rahman Zia-Ur, Entropy Based Image Fusion With the help of Continuous

Recognition, *ICPR*, Vol. 1, pp. 358 – 361, 2004.

Illuminated Dark Images, *ISDA 2010, Cairo, Egypt*, 2010b.

Genetic Algorithm, *IEEE ISDA Conference, December* 2010.

**6. Conclusions** 

in face recognition.

contrast images clearly visible.

evaluation of the cost function.

under various lighting conditions.

**7. References** 

3, 2002.


**0**

**11**

*México*

<sup>1</sup>*Universidad de Sonora*

<sup>2</sup>*Universidad Autónoma de Baja California* <sup>3</sup>*Universidad Nacional Autónoma de México*

**Self Adaptive Genetic Algorithms for Automated**

In this work it is developed a methodological proposal to build linear models of Time Series (TS) from setting out the problem of obtaining a good linear model, such as solving a problem of nonlinear optimization with bounded variables. It is worth to mention that to build these

As product of the methodology here presented, it will be developed two heuristic algorithms for the treatment of TS, which allow building several models for the same problem, where the accuracy of these can be increased by increasing the number of terms of the model, situation that does not happen with the traditional statistical approach. Thus, with this algorithms it can be obtained several proposals of solution for the same problem, of which it can be selected the one that presents the best results in the forecasting. In addition, the algorithms proposed in this work allow building different linear versions, but equivalent to the Autoregressive (AR) and the classic Autoregressive with Moving Average (ARMS) models, with the added advantage of the possibility of obtaining models for not stationary TS, and with non stationary

Since optimization problems set out here may present multiple local minimums, it is needed to use a special technique to solve them. With this end it was developed a version of the Self Adaptive Genetic Algorithms (SAGA), encoded on real numbers that allows, without intervention of the user, to find satisfactory solutions for different problems without making

On the other hand, among the principal points of this methodology it is the fact that in many cases, these linear versions present a phenomenon that has been named 'forecasting delay', which allows to modify the linear model obtained to find a more accurate forecasting.

It is important to notice that the first AR version of the algorithms developed for the TS were

that from now on it will be called NN3, which was realized in 2006-2007 (http://www.neural-forecasting-competition.com/NN3/results.htm). This competition is

**"NN3 Artificial Networks & Computational Intelligence Forecasting Competition"**

problems are taken some ideas of the traditional statistical approach.

variance, in cases where the traditional methodology does not work.

changes in the parameters of the code.

tested in the examples of the international competition:

**1. Introduction**

**Linear Modelling of Time Series**

Pedro Flores1, Larysa Burtseva2 and Luis B. Morales3

Socolinsky D. A. and Selinger A., A Comparative Analysis of Face Recognition Performance with Visible and Thermal Infrared Imagery, *IEEE International Conference of Pattern Recognition*, Vol. 4, pp. 217 –222, 2002.

Srinivas M. and Patnaik L. M., Genetic Algorithms: a Survey, pp. 17 – 26, Vol. 27, Jun 1994.

