Solution:

Thresholding is the simplest segmentation method.

The pixels are partitioned depending on their intensity value Global thresholding using an appropriate threshold T:

$$ \begin{array}{ll} g(x, y)=1 & \text { if } f(x, y)T \\ 0 & \text { if } f(x, y) \leq T \end{array} $$

It is called variable thresholding if T can change over the image Local or regional thresholding if T depends on a neighborhood of $(x, y)$

Adaptive thresholding if T is a function of It can be multiple thresholding:

$$ \begin{aligned} g(x, y)=a & \text { if } f(x, y)T_2 \\ b & \text { if } T_1(x, y) \leq T_2 \\ c & \text { if } f(x, y) \leq T_1 \end{aligned} $$

For choosing the thresholds; peaks and valleys of the image histogram can help. histogram for guiding the choice of the the of the histogram for guiding the choice of the threshold.

• The separation between peaks.

• The noise content in the image.

• The relative size of objects and background.

• The uniformity of the illumination.

• The uniformity of the reflection.

The reflective nature of objects and background could be such that they are easily separable However the image resulting from poor illumination could be quite difficult to segment.