Reservoir Computing Approach for Gray Images Segmentation
This is an incremental improvement for image processing tasks, specifically targeting gray image segmentation.
The paper tackled gray image segmentation by extracting multiple features from pixel intensity using an Echo State Network, which improved segmentation via clustering and intrinsic plasticity tuning, achieving better segmentation on the Lena benchmark image.
The paper proposes a novel approach for gray scale images segmentation. It is based on multiple features extraction from single feature per image pixel, namely its intensity value, using Echo state network. The newly extracted features - reservoir equilibrium states - reveal hidden image characteristics that improve its segmentation via a clustering algorithm. Moreover, it was demonstrated that the intrinsic plasticity tuning of reservoir fits its equilibrium states to the original image intensity distribution thus allowing for its better segmentation. The proposed approach is tested on the benchmark image Lena.