Image Segmentation #

Image segmentation is about dividing an image into regions that are non-overlapping. Together, these regions cover the entire image. Ideally, each region represents a meaningful part of the image, such as objects or background.

Segmentation methods often rely on two pixel properties:

Discontinuity: Identifying edges or sharp changes in intensity.
Similarity: Grouping areas with similar pixel values.

Challenges in segmentation arise from factors like occlusions, uneven lighting, and shadows. There is no universal method for all images, so segmentation techniques vary depending on the image type, feature selection, and computational requirements.

Categories of Segmentation Techniques #

Segmentation approaches can be grouped into:

Intensity-based methods: Operate on pixel intensity distributions, such as histograms. Example: thresholding.
Region-based methods: Use adjacency and connectivity between pixels. Examples include region growing and split-and-merge techniques.
Other methods: Include texture, motion, and edge-based segmentation.

Image Thresholding #

Thresholding simplifies an image by converting multiple gray levels into fewer levels, typically black and white. This involves:

Global Thresholding: A single threshold value is used for the entire image. Methods like Otsu’s algorithm determine the optimal threshold by maximizing between-class variance.
Local (Adaptive) Thresholding: Divides the image into blocks, applying a unique threshold for each block. It is effective for images with uneven lighting but computationally more intensive.

Advanced Thresholding Techniques #

Some specialized methods include:

Niblack Algorithm: Considers the mean and standard deviation within a local window.
Sauvola Algorithm: A modification of Niblack that normalizes the windowed standard deviation.

Practical Considerations #

Illumination: Variations in lighting can complicate even simple segmentations.
Noise: Thresholding results are sensitive to image noise. Preprocessing like noise reduction is often necessary.

Image segmentation is critical in computer vision and remains an active research area. Its success often depends on the context and the preprocessing applied to the input image.