Double recurrent interaction V1–V2–V4 based neural architecture for color natural scene boundary detection and surface perception

•Bio-inspired neural architecture for natural scene boundary detection.•Double feedback among V1, V2 and V4 cortical areas and chromatic diffusion.•Results quantified using F-measure from the Berkeley segmentation benchmark.•Parallel implementations on GPU: CUDA(R) and Matlab(R).•Neural architecture performance and results compatible with real applications.

In this paper, a new neural model for bio-inspired processing of color images, called dPREEN (double recurrent Perceptual boundaRy dEtection Neural) model, is presented. The dPREEN model includes a double feedback among V1, V2 and V4 cortical areas, simple and double color opponent processes, orientation filtering using Gabor kernels, surround suppression in complex cells, top-down and bottom-up information fusion and chromatic diffusion, to generate contours of perceptual significance in color natural scenes. The outputs of the model are a boundary map of the scene and surface perception images. This paper incorporates a comparative analysis of the proposed model against two other contour extraction methods in the Berkeley Segmentation Dataset and Benchmark. The analysis shows favorable results to the dPREEN model. Additionally, this paper describes two parallel implementations of the model for its execution on Graphics Processing Units.

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