Fast Component Tree Computation for Images of Limited Levels

Area of Science:

• Computer Vision
• Image Processing
• Algorithms

Background:

• Component trees are widely used in image analysis.
• Existing algorithms for component tree computation have limitations.

Purpose of the Study:

• Introduce a novel, efficient component tree computation algorithm.
• Enhance component representation by including boundary information.

Main Methods:

• The algorithm involves two main steps: building level line trees via an optimized top-down approach.
• Computing components from level lines using a new line-by-line method.
• The approach is applicable to 4-/8-connectivity and 6-connectivity.

Main Results:

• The new algorithm demonstrates high speed for images with a limited number of intensity levels.
• Components are represented by level lines, which inherently contain boundary information.
• This boundary information is not provided by traditional component computation algorithms.

Conclusions:

• The developed algorithm offers a fast and informative method for component tree computation.
• The inclusion of boundary information enhances the utility of component trees in image analysis.
• This approach is particularly beneficial for specific types of image data.