Auto Threshold

This module is useful when working with blob analysis or shape recognition whose background image can change and a manual threshold is not reliable enough.

The following briefly outlines the algorithms used by the thresholding methods to allow you to chose the most appropriate for your application. Note that they all operate on the image's histogram.

• Two Peaks - Detects the two highest peaks in the histogram separated by the distance specified. The distance will ensure that peaks close to each other are not selected. The threshold is then found by finding the deepest valley between these two peaks.
• Mean Level - the average pixel value is determined using the image histogram. All pixel intensities below that value are set to black with all pixel intensities above the mean set to white.
• Black Percent - Also known as P-Tile. The threshold level is set based on the specified percent of suggested dark pixels (or background) there are in the image. The histogram is used to indicate how much of the image at a certain threshold would be set to black. Once this amount exceeds the specified percent the current histogram index (0-255) is used as the threshold.
• Edge Percent - Similar to the black percent the edge percent threshold is determined by the specified percent of edge pixels that exist below the threshold. Instead of just counting every pixel the edge percent basis its measurement on how much a pixel is part of an edge by performing a laplacian filter prior to threshold determination.
• Entropy (Kapur) - Utilizes Kapur's entropy formula to find the threshold that minimizes the entropy between the two halves of the histogram created by a threshold.
• Cluster (Otsu) - One of the most popular threshold techniques that creates two clusters (white and black) around a threshold T and successively tests the within-class variance of the clusters for a minimum. This algorithm can also be thought of as maximizing the between-class variance.
• Symmetry - Assumes that the largest peak in the histogram is somewhat symmetrical and uses that symmetry to create a threshold just before the beginning of the largest peak. This technique is particularly useful to segment objects from large background planes.
• Triangle - Works well with histograms that don't have well defined peaks. This technique finds the maximum distance between a suggested threshold value and a line that connects the first non-zero pixel intensity with the highest peak. Inherent in this technique is the distance of a point to a line equation.

Example

Source Image	Thresholded

See Also

Manual Thresholding

For more information