A Model of Virtual Carrier Immigration in Digital Images for Region Segmentation
This addresses image segmentation for computer vision applications, but it appears incremental as it adapts a known physical mechanism to a new domain.
The authors tackled image segmentation by proposing a model inspired by carrier immigration in P-N junctions, simulating diffusion and drifting to achieve self-balancing; experimental results on test and real-world images demonstrated self-adaptive pixel gathering for region segmentation, proving the method's effectiveness.
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.