Skip to main content

Ctrl+K

Basics of Image Processing

Foreword

Digital images

Introduction
Image acquisition
What is a digital image?
Color models
Displaying an image
Arithmetic operations
Histogram
Conclusion

Transforms & Filtering

Introduction
Convolution
Fourier transform
Filtering
Conclusion

Compression

Introduction
Lossless compression
Lossy compression
Conclusion

Interpolation

Introduction
Interpolation methods
Conclusion

Segmentation

Introduction
Histogram thresholding
Clustering
Watershed
How to evaluate a segmentation?
Conclusion

Binary image processing

Introduction
Neighborhood and connectivity
Mathematical morphology
Applications
Measure
Conclusion

Restoration

Introduction
Denoising
Deconvolution
Conclusion

Feature detection

Introduction
Matched filter
Edge detection
Corner detection
Line detection
Conclusion

Labs

Lab 1
Lab 2
Lab 3
- Lossy compression
- Interpolation
Lab 4
- Segmentation
Lab 5
- Mathematical morphology
Lab 6
- Denoising
- Deconvolution
Lab 7

Appendices

Moodle
Installing and using Python
Glossary
License

.ipynb

Conclusion

Contents

References

Conclusion#

References#

I. Bloch et al., Le traitement des images (volume 1, volume 2), Polycopié du cours ANIM, Télécom-Paris, 2005.

previous

Measure

next

Introduction

Contents

References

By Vincent Mazet (University of Strasbourg, France)

© Copyright 2024-2025.

Basics of Image Processing — Vincent Mazet (Université de Strasbourg), 2020-2025 — CC BY-NC 4.0.