๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Fuzzy Image Processing and Applications with MATLAB

โœ Scribed by Tamalika Chaira, Ajoy Kumar Ray


Publisher
CRC Press
Year
2009
Tongue
English
Leaves
234
Edition
1
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.

Fuzzy Image Processing and Applications with MATLABยฎ presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few.

Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation.

Minimize Processing Errors Using Dynamic Fuzzy Set Theory

This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecisionโ€”whether it is in the grey level of the image, geometry of an object, definition of an objectโ€™s edges or boundaries, or in knowledge representation, object recognition, or image interpretation.

The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.


๐Ÿ“œ SIMILAR VOLUMES


Image processing with MATLAB: Applicatio
โœ Omer Demirkaya, Musa H. Asyali, Prasanna K. Sahoo ๐Ÿ“‚ Library ๐Ÿ“… 2008 ๐Ÿ› CRC Press ๐ŸŒ English

<P><STRONG>Image Processing with MATLAB<SUP>ยฎ</SUP>: <EM>Applications in Medicine and Biology</EM> explains complex, theory-laden topics in image processing through examples and MATLAB<SUP>ยฎ</SUP> algorithms. It describes classical as well emerging areas in image processing and analysis. </P> <P></

Image processing with MATLAB: applicatio
โœ Omer Demirkaya, Musa H. Asyali, Prasanna K. Sahoo ๐Ÿ“‚ Library ๐Ÿ“… 2009 ๐Ÿ› CRC Press ๐ŸŒ English

<P><STRONG>Image Processing with MATLAB<SUP>ยฎ</SUP>: <EM>Applications in Medicine and Biology</EM> explains complex, theory-laden topics in image processing through examples and MATLAB<SUP>ยฎ</SUP> algorithms. It describes classical as well emerging areas in image processing and analysis. </P> <P></

Image processing with MATLAB: Applicatio
โœ Omer Demirkaya, Musa H. Asyali, Prasanna K. Sahoo ๐Ÿ“‚ Library ๐Ÿ“… 2008 ๐Ÿ› CRC Press ๐ŸŒ English

<P><STRONG>Image Processing with MATLAB<SUP>ยฎ</SUP>: <EM>Applications in Medicine and Biology</EM> explains complex, theory-laden topics in image processing through examples and MATLAB<SUP>ยฎ</SUP> algorithms. It describes classical as well emerging areas in image processing and analysis. </P> <P></

Fuzzy algorithms: with applications to i
โœ Zheru Chi, Hong Yan, Tuan Pham ๐Ÿ“‚ Library ๐Ÿ“… 1996 ๐Ÿ› World Scientific ๐ŸŒ English

This text deals with the subject of fuzzy algorithms and their applications to image processing and pattern recognition. Subjects covered include membership functions; fuzzy clustering; fuzzy rulers and defuzzification; fuzzy classifiers; and combined classifiers.