Image Segmentation Using PSO

What is this project about?

Particle Swarm Optimization (PSO) is a popular optimization technique inspired by the collective behavior of bird flocking or fish schooling. It is commonly used to solve optimization problems in various fields, including image processing.

Image segmentation is the process of partitioning an image into multiple segments or regions based on certain criteria, such as color, texture, or intensity. It is a fundamental task in computer vision, and it has various applications, including object recognition, image analysis, and medical imaging.

PSO can be used for image segmentation because it is a powerful optimization technique that can find the optimal segmentation parameters by searching the solution space efficiently. PSO-based image segmentation algorithms typically define the fitness function based on the similarity of the segmented

Velocity Vector Update Equation:

$$ \begin{equation} \mathbf{v_{id}}(t+1) = w \mathbf{v_{id}}(t) + c_1 rand() \left( \mathbf{p_{id}}(t) - \mathbf{x_{id}}(t) \right) + c_2 rand() \left( \mathbf{v_{gd}}(t) - \mathbf{x_{gd}}(t) \right) \end{equation} $$

Position Vector Update Equation:

$$ \begin{equation} \mathbf{x_{id}}(t+1) = \mathbf{x_{id}}(t) + \mathbf{v_{id}}(t+1) \end{equation} $$

Inertia Weight:

$$ \begin{equation} w = w_{max} - \frac{w_{max} - w_{min}}{iter_{max}} \times iter \end{equation} $$