Preface

Pattern recognition is an important field during this time, due to its use in companies, business, and real life. Each time more and more data are generated, there are more identifications of patterns. The automatic interpretation of big data is by the extraction of patterns, and the field of pattern recognition has a great role in extracting information and decision-making. It is key to the development of a society and our quality of life, based on the knowledge gained from this data.

This book will show various applications for the improvement and use of data analysis and the automatic system available as tools for security, biology, and biometrics.

In fields with the use of security, molecular biology, modeling, improvement of data, and biometrics, new and advanced techniques can be applied to facilitate or provide tools for the detection of patterns. A great number of tools are being developed in this sense. This book presents works of high quality, developed on a scientific methodology, giving validation to the present proposals. The content of the book focuses on automatic systems and data analysis; therefore, it will be a very attractive read for the reader.

"Applications of Pattern Recognition" comprises seven chapters, which have been divided into two sections: "Data Analysis" and "Automatic Systems". The section "Data Analysis" has three chapters. The analysis is applied on an approach that is composed of two types of depth restoration methods based on fixation tremor: differential type method and integral type method. The first method is based on the change in image brightness between frames, and the other type is based on image blurring due to movement. In this section, an approach is focused on the discussion of identifying inconsistencies associated with patterns. And finally, a 3D abstraction method receives input from camera intrinsic parameters and several pictures of the scene. This approach introduces the geometrical relations, which have to be exploited for structure from motion sketch or abstraction based on line segments, the optimization methods for its optimization, and how to compare the experimental results with ground truth measurements. The section "Automatic Systems" contains four chapters. The automatic systems are shown by an approach, which introduces recent methods for processing missing values. For this approach, four types of commonly used algorithms were applied, namely, k-nearest neighbors, regression, tree-based algorithms, and latent component-based approaches. This book also presents a system based on different approaches using the retina of the human eye to evaluate individual parameters for human recognition. Furthermore, a method is developed for the feature selection using discrete cosine transform to extract the feature, and then, the sparse principal component analysis is used for the selection of significant attributes, as the feature technique in offline Arabic signature verification. Finally, clustering algorithms and statistical approaches are shown for grouping similar gene expression profiles that can be applied to RNA-seq data analysis.

As editor of this book, I would like to thank the authors for their effort and dedication that they have made to achieve some works of great quality. The sum of this effort has produced this book, which has become an inescapable read for all those who want to know the latest advances in pattern recognition.

**Carlos M. Travieso-Gonzalez**

Section 1

Data Analysis

Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain

Section 1 Data Analysis

**Chapter 1**

**Abstract**

operation.

**3**

**1. Introduction**

*Norio Tagawa*

Stereoscopic Calculation Model

Based on Fixational Eye Movements

Fixational eye movement is an essential function for watching things using the retina, which has the property of responding only to changes in incident light. However, since the rotation of the eyeball causes the translational movement of the crystalline lens, it is possible in principle to recover the depth of the object from the moving image obtained in this way. We have proposed two types of depth restoration methods based on fixation tremor; differential-type method and integral-type method. The first is based on the change in image brightness between frames, and the latter is based on image blurring due to movement. In this chapter, we introduce them and explain the simulations and experiments performed to verify their

**Keywords:** motion stereoscopic, fixational eye movements, differential-type method, integral-type method, optical flow, gradient equation, image blur

When humans stare at a target, an irregular involuntary movement called fixational eye movements occur [1]. The human retina can maintain reception sensitivity by finely vibrating the image of the target on the retina, so in order to see something, first fixation motion is required. It has been reported that the vibrations may work not only as such the intrinsic function to preserve photosensitivity but also as an assistance in image analysis, the mechanism of which can be interpreted as an instance of stochastic resonance (SR) [1]. SR is inspired by biology, more specifically by neuron dynamics [2], and based on it, the Dynamic Retina (DR) [3] and the Resonant Retina (RR) [4], which are new vision devices taking advantage of random camera vibrations, were proposed for contrast enhancement and edge detection respectively. It has been reported that the movement of the retinal image due to fixation eye movements can be an unconscious clue to depth perception, and an actual vision system based on fixational eye movements has been proposed [5]. On the other hand, binocular stereopsis is vigorous and plays an essential role in depth perception of a human vision system [6]. In general, binocular stereopsis detects relatively large disparities, hence it can recognize high accurate depth. However this causes an occlusion problem, and a lot of solutions of it have been proposed. Wang et al. have proposed a local detector for occlusion based on deep learning [7]. In [8], a robust depth restoration method has been proposed that integrates line-field imaging technology that simultaneously observes multiple angle views with stereo vision. Therefore, we expect that primitive depth
