koushikraghav/imagethresholding

imageThresholding

Types

Simple Thresholding
Adaptive Thresholding
Otsu's Thresholding

Simple Thresholding

if pixelValue > thresholdValue:
  assign one value (may be white)
else:
  assign another value (may be black)

Types

• cv2.THRESH_BINARY
• cv2.THRESH_BINARY_INV
• cv2.THRESH_TRUNC
• cv2.THRESH_TOZERO
• cv2.THRESH_TOZERO_INV

Adaptive Thresholding

Calculates the threshold for a small regions of the image. So we get different thresholds for different regions of the same image and it gives us better results for images with varying illumination.

Types

1. Adaptive Method - It decides how thresholding value is calculated.

cv2.ADAPTIVE_THRESH_MEAN_C : threshold value is the mean of neighbourhood area.

cv2.ADAPTIVE_THRESH_GAUSSIAN_C : threshold value is the weighted sum of neighbourhood values where weights are a gaussian window.

2. Block Size - It decides the size of neighbourhood area.

3. C - It is just a constant which is subtracted from the mean or weighted mean calculated.

Otsu’s Binarization

Finds a threshold value that minimizes weighted within class variation

MatplotLib

matplotlib.pyplot.xticks(ticks=None, labels=None, ** kwargs)

ticks --> A list of positions at which ticks should be placed.

      --> Passing an empty list will disable xticks.

labels --> A list of explicit labels to place at the given locations.

**kwargs --> Text properties can be used to control the appearance.