Interest in image compression for internet and other multimedia applications has spurred research into compression techniques that will increase storage capabilities and transmission speed. This tutorial provides a practical guide to fractal and wavelet approaches--two techniques with exciting potential. It is intended for scientists, engineers, researchers, and students. It provides both introductory information and implementation details. Three Windows-compatible software systems are included so that readers can explore the new technologies in depth. Complete C/C++ source code is provided, enabling readers to go beyond the accompanying software. The mathematical presentation is accessible to advanced undergraduate or beginning graduate students in technical fields.

One half of the book is authored by Yuval Fisher himself, while articles from another 12 experts in the field present material from different points of view. The focus here is solely on fractal image encoding, with the aim of providing a working code that is usable in applications, while containing the complete details of how to encode and decode images. An indispensable "how to" guide, combining the very latest results in the field. Of interest to a very wide audience, ranging from experts in image processing to high school students.

The book focuses on soft computing and its applications to solve real-world problems occurring in different domains ranging from medicine and healthcare, and supply chain management to image processing and cryptanalysis. It includes high-quality papers presented in the International Conference on Soft Computing: Theories and Applications (SoCTA 2017), organized by Bundelkhand University, Jhansi, India. Offering significant insights into soft computing for teachers and researchers alike, the book inspires more researchers to work in the field of soft computing.

Digital image processing is a field that is constantly improving. Gaining high-level understanding from digital images is a key requirement for computing. One aspect of study that is assisting with this advancement is fractal theory. This new science has gained momentum and popularity as it has become a key topic of research in the area of image analysis. Examining Fractal Image Processing and Analysis is an essential reference source that discusses fractal theory applications and analysis, including box-counting analysis, multi-fractal analysis, 3D fractal analysis, and chaos theory, as well as recent trends in other soft computing techniques. Featuring research on topics such as image compression, pattern matching, and artificial neural networks, this book is ideally designed for system engineers, computer engineers, professionals, academicians, researchers, and students seeking coverage on problem-oriented processing techniques and imaging technologies.

Fractal image presents both the mathematical ideas and the practical techniques involved in compressing images by means of fractals. With claims of compression ratios in excess of 10 000 to 1, fractal compression is certainly a promising new technology. However, the method is in fact quite controversial, with some people claiming it doesn't work well, while others claim it works wonderfully; as the technology is fast developing, but as yet unproven. At the moment, very little is known about fractal compression and what is known is difficult to ascertain. This paper attempts to clarify the key ideas behind the methodology; and includes an overview of the competition, which includes the JPEG standard and vector quantization. Fractal image compression relies on the fact that all real-world images are rich in affine redundancy; that is, under suitable affine transformations, large bits of real-world images look like smaller bits of the same real-world image. The basic idea behind the method is to express the image as a set of local iterated function systems (IFSs).

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. The Handbook of Research on Pattern Engineering System Development for Big Data Analytics is a critical scholarly resource that examines the incorporation of pattern management in business technologies as well as decision making and prediction process through the use of data management and analysis. Featuring coverage on a broad range of topics such as business intelligence, feature extraction, and data collection, this publication is geared towards professionals, academicians, practitioners, and researchers seeking current research on the development of pattern management systems for business applications.