Saliency in Computer Vision

The goal of computer vision is to develop algorithms for image understanding for computers and not necessarily to emulate the human vision system in biologically plausible ways. Nevertheless, research in computer vision has understandably looked to the human visual system for inspiration and intuition. One key aspect of human perception is saliency, the property of certain arrangements conspicuously standing out from a cluttered background. Over the past several years, a computational framework has been developed to detect salient perceptual structures in 2D, 3D, or N-D data sets, even under severe noise corruption. In the framework, data tokens are represented by tensors and the saliency of each token is computed based on information propagated among neighboring tokens via tensor voting. The Tensor Voting Framework enables us to cast computer vision problems as perceptual organization ones whose solution is the most salient perceptual structures.