Image Segmentation
Segmentation methods in image processing and analysis
Image segmentation is the process of dividing an image into multiple parts. This is typically used to identify objects or other relevant information in digital images. There are many different ways to perform image segmentation, including:
- Thresholding methods, such as Otsu’s method
- Clustering methods, such as K-means and principle components analysis
- Transform methods, such as watershed
- Texture methods, such as texture filters
You can perform image segmentation in MATLAB with Image Processing Toolbox, which provide image segmentation algorithms, tools, and a comprehensive environment for data analysis, visualization, and algorithm development.
Examples and How To
- Segmenting the Continental Divide (Webinar)
- Indexing Segmented Objects and Connected Components (Blog)
- Color-Based Segmentation Using K-Means Clustering (Example)
- Detecting a Cell Using Image Segmentation (Example)
- Marker-controlled Watershed Segmentation (Example)
Software Reference
- Image Types and Conversions (Function List)
- Morphological Operations (Function List)
See also: Steve on Image Processing, image enhancement, digital image processing, image transform, image analysis, spatial transformations and image registration, image and video processing
Color-based Image Segmentation (Video)
