Howtosignalprocessing
- What is Otsu method in image processing?
- How does Otsu algorithm work?
- What is Otsu's thresholding method for grayscale images?
- What is the primary limitation of Otsu's method for thresholding?
What is Otsu method in image processing?
In computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之, Ōtsu Nobuyuki), is used to perform automatic image thresholding. In the simplest form, the algorithm returns a single intensity threshold that separate pixels into two classes, foreground and background.
How does Otsu algorithm work?
Otsu's method[1] is a variance-based technique to find the threshold value where the weighted variance between the foreground and background pixels is the least. The key idea here is to iterate through all the possible values of threshold and measure the spread of background and foreground pixels.
What is Otsu's thresholding method for grayscale images?
Otsu's method chooses a threshold that minimizes the intraclass variance of the thresholded black and white pixels. The global threshold T can be used with imbinarize to convert a grayscale image to a binary image. [ T , EM ] = graythresh( I ) also returns the effectiveness metric, EM .
What is the primary limitation of Otsu's method for thresholding?
Otsu's algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu's method is more robust compared to 1D Otsu's method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images.
