otsusebaasilised

Otsusebaasilised, also known as *Otsu’s method* or *Otsu’s thresholding*, is a widely used algorithm in image processing for automatic global thresholding. Developed by Nobuyuki Otsu in 1979, this method determines the optimal threshold value to separate an image into foreground and background by maximizing the between-class variance. It assumes that the image contains two distinct classes of pixels (e.g., object and background) and aims to minimize intra-class variance while maximizing inter-class separation.

The algorithm works by analyzing the histogram of pixel intensities, treating each possible threshold as a candidate for separating the two classes. For each threshold *t*, it calculates the weighted mean of the foreground and background pixels and computes the variance between these two groups. The threshold that yields the highest between-class variance is selected as the optimal threshold. This approach is particularly effective for bimodal histograms, where two distinct peaks represent the two classes.

Otsusebaasilised is favored for its simplicity and computational efficiency, making it suitable for real-time applications. However, it may struggle with images containing more than two classes or complex distributions, as it assumes a clear separation between foreground and background. Variations and extensions of the method have been proposed to address these limitations, such as multi-thresholding techniques or adaptive thresholding.

The algorithm is commonly implemented in software libraries like OpenCV and SciPy, where it is often used as a preprocessing step in segmentation tasks, such as document scanning, medical imaging, and object detection. Its effectiveness depends on the quality of the input image, and preprocessing steps like noise reduction or contrast enhancement may improve results.