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Image Operators: Image Processing in Python de Jason M. Kinser

Image Operators: Image Processing in Python

Autor Jason M. Kinser
en Limba Engleză Hardback – 24 oct 2018
For decades, researchers have been developing algorithms to manipulate and analyze images. From this, a common set of image tools now appear in many high-level programming languages. Consequently, the amount of coding required by a user has significantly lessened over the years. While the libraries for image analysis are coalescing to a common toolkit, the language of image analysis has remained stagnant. Often, textual descriptions of an analytical protocol consume far more real estate than does the computer code required to execute the processes. Furthermore, the textual explanations are sometimes vague or incomplete. This book offers a precise mathematical language for the field of image processing. Defined operators correspond directly to standard library routines, greatly facilitating the translation between mathematical descriptions and computer script. This text is presented with Python 3 examples.
  • This text will provide a unified language for image processing
  • Provides the theoretical foundations with accompanied Python® scripts to precisely describe steps in image processing applications
  • Linkage between scripts and theory through operators will be presented
  • All chapters will contain theories, operator equivalents, examples, Python® codes, and exercises
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Specificații

ISBN-13: 9781498796187
ISBN-10: 1498796184
Pagini: 366
Ilustrații: 36 Illustrations, color; 275 Illustrations, black and white
Dimensiuni: 178 x 254 x 21 mm
Greutate: 0.88 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Public țintă

Academic and Professional Practice & Development

Cuprins

PART I Image Operators. 1 Introduction. 2  Operator Nomenclature. 3 Scripting in Python. 4  Digital Images. 5 Color. PART II Image Space Manipulations. 6 Geometric Transformations. 7 Image Morphing. 8 Principle Component Analysis. 9 Eigenimages. PART III Frequency Space Manupulations. 10 Image Frequemncies. 11 Filtering in Frequency Space. 12 Correlations. PART IV  Texture and Shape. 13 Edge Detection. 14 Hough Transforms. 15 Noise. 16 Texture Recognition. 17 Gabor Filtering. 18 Describing Shape. PART V Basis. 19 Basis Sets. 20 Pulse Images and Autowaves. Appendix A Operators. Appendix B Operators in Symbolic Order. Appendix C Lengthy Codes. Bibliography.

Notă biografică

Jason M Kinser, DSc, has been an associate professor at George Mason University for more than 18 years teaching courses in physics, computational science, bioinformatics and forensic science. Recently, he converted the traditional university physics course into an active learning technology environment at GMU. His research interests include modern teaching techniques, more effective methods in text-based education, image operators and analysis, pulse image processing and multi-domain data analysis. This book was born from a desire to engage students in physics education and to find ways of reducing the external costs that both students and institutions incur within the traditional education framework.Jason M Kinser, DSc, has been an associate professor at George Mason University for more than 18 years teaching courses in physics, computational science, bioinformatics and forensic science. Recently, he converted the traditional university physics course into an active learning technology environment at GMU. His research interests include modern teaching techniques, more effective methods in text-based education, image operators and analysis, pulse image processing and multi-domain data analysis. This book was born from a desire to engage students in physics education and to find ways of reducing the external costs that both students and institutions incur within the traditional education framework.

Descriere

For decades, researchers have been developing algorithms to manipulate and analyze images. From this, a common set of image tools now appear in many high-level programming languages. Consequently, the amount of coding required by a user has significantly lessened over the years.

While the libraries for image analysis are coalescing to a common toolkit, the language of image analysis has remained stagnant. Often, textual descriptions of an analytical protocol consume far more real estate than does the computer code required to execute the processes. Furthermore, the textual explanations are sometimes vague or incomplete.

This book offers a precise mathematical language for the field of image processing. Defined operators correspond directly to standard library routines, greatly facilitating the translation between mathematical descriptions and computer script. This text is presented with Python 3 examples.

This text will provide a unified language for image processing Provides the theoretical foundations with accompanied Python (R) scripts to precisely describe steps in image processing applications Linkage between scripts and theory through operators will be presented All chapters will contain theories, operator equivalents, examples, Python (R) codes, and exercises