Biography: Prof. Vikramsingh R. Parihar is an Assistant Professor in Electrical Department, PRMCEAM, Badnera-Amravati having 6 years of experience. He has received the B.E degree in Instrumentation from Sant Gadge Baba Amravati University, India, in 2011 and the M.E degree in Electrical and Electronics Engineering, Sant Gadge Baba Amravati University, India, in 2014. He is the editorial board member of 26 recognized journals and the life member of ISTE, HKSME, ICSES, IAENG, ENZ, IJCSE and theIRED. His domain of research includes Electrical Engineering, Instrumentation, Electrical Power Systems, Electrical and Electronics Engineering, Digital Image Processing, Neuro-Fuzzy Systems and has contributed to research in a noteworthy way by publishing 42 research papers in high indexed National/International Journals and 4 papers in IEEE Conferences.
1- First of all, this book will prove as a center of knowledge for recent image segmentation and image mining techniques and their advancement and contribution in the field of Image Processing.
2- Secondly, few methods are provided with their in-depth working and simulations. Rigorous analysis is done on them and each and every happening is thoroughly explained.
3- Thirdly, an exhaustive literature review is done and detailed knowledge of various important and noteworthy contributions in the image processing field is mentioned.
4- This book is intended to give basic and detailed knowledge of image segmentation techniques to those who are willing to understand, study and contribute in digital image processing field.
About this book
Today, the medical industry, astronomy, physics, chemistry, forensics, remote sensing, manufacturing, and defense are just some of the many fields that rely upon images to store, display, and provide information about the world around us. The challenge to scientists, engineers and business people is to quickly extract valuable information from raw image data. This is the primary purpose of image processing - converting images to information.
The main aim of image segmentation is to identify meaningful objects from a given image. For example, a boy of just four years can see/detect/locate a pen on a table, as he is naturally equipped with image segmentation power. However, robots cannot do it, until they use image segmentation algorithms. To perform an image segmentation task, several techniques have been developed. The simplest and flexible techniques are discussed in this book from basics. This book explains how to segment images using state-of-the-art methods and recent innovations incorporating Deep Learning. Also, image segmentation is usually the first step towards various image processing applications like medical imaging, object detection, machine vision, recognition, image mining, image restoration, image enhancement, etc. and thus, is a critical topic to study.
In this book, each chapter introduces image segmentation topics and includes information regarding when one method may be preferred over another to obtain specific image features. Numerous step-by-step examples illustrate the processing and analysis routines, allowing you to quickly understand how to get the desired results when working with your own image data. This book is not intended to be a complete source for image processing knowledge, an advanced image processing manual or an image processing reference guide. This book is designed to teach people how to segment images effectively and does not assume that they are already experts in the field of image processing.
This book will be indexed in the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH), and the partner of PubMed, the worlds most accredited bibliographic database for the biomedical literature.
This book will be submitted to be indexed in the SCOPUS, the worlds most accredited bibliographic database.
Deep Learning Graph Theory Image Mining Image Analysis Image Processing Image Segmentation Image Segmentation Applications Image Segmentation Innovations Medical Image Segmentation Object Detection Threshold Wavelet Transform