Image Segmentation with Two-Dimension Threshold Based on Adaptive Particle Swarm Optimization and Data Field

A novel method of image segmentation based on adaptive particle swarm optimization and data field has been proposed for optimal threshold selection in image segmentation.In the proposed method,images are mapped from the grayscale space to the potential space of the data field.By taking the frequency of two-dimension gray histogram as the mass of data field,the interactions between elements in the two-dimension histogram can be calculated,a three-dimension data field is generated subsequently.Thus,by employing adaptive particle swarm optimization,the optimal threshold,which is the point with the maximum potential value,can be found and good segmentation results can be obtained.The relevant experiments have shown that the proposed method is effective and greatly reduces the complexity of computation.