Contents


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

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

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

"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.

from this data.

biometrics.

attractive read for the reader.
