Abstract
Mathematical Morphology is a well established framework for image filtering. Fundamental mathematical morphology operators, such as Erosion and Dilation (and their combinations: opening and closing) examine the geometrical structures in the image by matching them to small patterns, called Structuring Elements.
📸Remote Sensing Image Enhancement
- Very high spatial resolution remote sensing images contain large amount of details
- Sub-metric spatial resolution allows for accurate analysis of objects with different scales and shapes
📗Introduction to Mathematical Morphology
- Originates from the study of the geometry of porous media in the mid-sixties in France
- Theoretical model based on lattice theory, used for digital image processing
🦯Filtering with Morphology Operators
- Image filtering with neighborhood operators that probe images with structuring elements
- Use for noise reduction, edge enhancement and extraction/suppression of structures
📐Basic Morphological Operators
- Dilation, and Erosion are the two most basic operations in mathematical morphology
- Opening and Closing can be defined as their combinations along with set operators
🌌Geodesic Transformation
- Morphological reconstruction is based on iteration of geodesic erosion and dilation operations
- They are connected operators which either complete remove or entirely preserve connected components
⚙️Morphological Profiles
- Multi-scale decomposition of an image into a stack of filtered images
- Sequence of opening and closing by reconstruction filters
No items found.
Previous teaching
