Data-driven nonlinear diffusion for object segmentation
We propose a novel technique for effective object segmentation, which is based on the combination of image simplification via data-driven nonlinear diffusion and subsequent efficient segmentation of the simplified image. In particular, the data, taking the form of a disparity field from a stereo analysis, has been used to modulate the diffusion process. The strength of this strategy consists of an ability to smooth considerably the details of the imaging scene within the objects' boundaries while inhibiting the diffusion across the boundaries, preserving and even enhancing the object borders. As such, from the simplified image, a simple but efficient histogram-based thresholding and labeling technique can be used to extract precisely an object boundary in its entirety.