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
Image operators image processing in Python
β Scribed by Kinser, Jason M
- Year
- 2019
- Tongue
- English
- Leaves
- 366
- Edition
- First edition
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Part I - Image Operators -- Operator Nomenclature -- Scripting in Python -- Digital Images -- Color -- Part II Image Space Manipulations -- Geometric Transformations -- Image Morphing -- Principle Component Analysis -- Eigenimages -- Part III Frequency Space Manipulations -- Image Frequencies -- Filtering in Frequency Space -- Correlations -- Part IV Texture and Shape -- Edge Detection -- Hough Transforms -- Noise Β Read more...
Abstract:
β¦ Table of Contents
Content: 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.
β¦ Subjects
Image processing -- Digital techniques.;Python (Computer program language);Image processing -- Digital techniques;Python;Bildverarbeitung;Bildanalyse
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