Shih_Image_Processing_and_Mathematical_Morpholo.pdf

Introduction to Mathematical Morphology 7
1.4
Scope of This Book
This book provides a comprehensive overview of morphological image
processing and analysis. It presents the necessary fundamentals, advanced
techniques, and practical applications for researchers, scientists, and engineers
who work in image processing, machine vision, and pattern recognition disciplines.
As there have not been many books published in morphological image
processing and analysis in the past few years, this book is novel and up-todate
in providing many of the newest research advances in this discipline.
This book provides students, researchers, and professionals with technical
information pertaining to image processing and mathematical morphology,
placing it within a fundamental theoretical framework, and leading to the
development of many advanced techniques. By comprehensively considering
the essential principles of morphological algorithms and architectures,
readers can not only obtain novel ideas in implementing advanced algorithms
and architectures, but also discover new techniques.
These advanced techniques include image enhancement, edge detection
and linking, alternating sequential morphology, recursive morphology, soft
morphology, order-statistic soft morphology, recursive soft morphology,
recursive order-statistic soft morphology, regulated morphology, fuzzy morphology,
and sweep morphology. The book also examines practical applications
such as distance transformation, decomposition of structuring elements,
feature extraction, skeletonization, object representation and recognition,
shape description, and shortest path planning. It covers morphological architectures
such as threshold decomposition of grayscale morphology into
binary morphology, programmable neural networks, multilayer perceptron,
systolic array, and multi computers. The principles of image processing and
mathematical morphology in this book are illustrated with abundant graphs,
pictures, and examples in order for readers to simplify problems and understand
the underlying theoretical concept easily, even though the theories and
algorithms are themselves complicated.
References
Angulo, J. and Serra, J., “Automatic analysis of DNA microarray images using mathematical
morphology,” Bioinformatics, vol. 19, no. 5, pp. 553–562, Mar. 2003.
Damera-Venkata, N., Kite, T. D., Geisler, W. S., Evans, B. L., and Bovik, A. C., “Image
quality assessment based on a degradation model,” IEEE Trans. Image Processing,
vol. 9, no. 4, pp. 636–650, Apr. 2000.