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Contour detection combined with depth information

Many challenging computer vision problems have been proven to benefit from the incorporation of depth information, to name a few, semantic labellings, pose estimations and even contour detection. Different objects have different depths from a single monocular image. The depth information of one object is coherent and the depth information of different objects may vary discontinuously. Meanwhile, there exists a broad non-classical receptive field (NCRF) outside the classical receptive field (CRF). The response of the central neuron is affected not only by the stimulus inside the CRF, but also modulated by the stimulus surrounding it. The contextual modulation is mediated by horizontal connections across the visual cortex. Based on the findings and researches, a biological-inspired contour detection model which combined with depth information is proposed in this paper.