A Model of Virtual Carrier Immigration in Digital Images for Region Segmentation

TL;DR

This paper introduces a new image segmentation model inspired by physical carrier immigration in P-N junctions, simulating diffusion and drift to achieve adaptive and meaningful pixel grouping.

Contribution

The paper presents a novel model that mimics physical carrier mechanisms for digital image segmentation, providing a self-balancing and adaptive approach.

Findings

Effective segmentation of test images

Successful application to real-world images

Demonstrates self-adaptive pixel grouping

Abstract

A novel model for image segmentation is proposed, which is inspired by the carrier immigration mechanism in physical P-N junction. The carrier diffusing and drifting are simulated in the proposed model, which imitates the physical self-balancing mechanism in P-N junction. The effect of virtual carrier immigration in digital images is analyzed and studied by experiments on test images and real world images. The sign distribution of net carrier at the model's balance state is exploited for region segmentation. The experimental results for both test images and real-world images demonstrate self-adaptive and meaningful gathering of pixels to suitable regions, which prove the effectiveness of the proposed method for image region segmentation.