SLIC Superpixel Segmentation for Image Processing
- Login to Download
- 1 Credits
Resource Overview
MATLAB implementation of SLIC superpixel segmentation algorithm featuring efficient clustering, boundary adherence, and customizable parameter tuning for superior image partitioning results.
Detailed Documentation
This practical MATLAB implementation of the SLIC (Simple Linear Iterative Clustering) superpixel segmentation algorithm provides robust performance for detailed image analysis. The code employs k-means clustering in a five-dimensional color-space (Labxy) combining color similarity and spatial proximity to generate uniform superpixels with preserved boundary accuracy. Key functions include adaptive distance metric calculations, centroid initialization optimization, and post-processing connectivity enforcement. Users can adjust parameters like desired superpixel count and compactness factor to balance segmentation granularity with computational efficiency. This implementation facilitates advanced image processing workflows by converting pixel-level data into perceptually meaningful regions, significantly improving performance in computer vision applications such as object detection and semantic segmentation.
- Login to Download
- 1 Credits