Dynamic Scene Stitching Driven by Visual Cognition Model

Dynamic scene stitching still has a great challenge in maintaining the global key information without missing or deforming if multiple motion interferences exist in the image acquisition system. Object clips, motion blurs, or other synthetic defects easily occur in the final stitching image. In our research work, we proceed from human visual cognitive mechanism and construct a hybrid-saliency-based cognitive model to automatically guide the video volume stitching. The model consists of three elements of different visual stimuli, that is, intensity, edge contour, and scene depth saliencies. Combined with the manifold-based mosaicing framework, dynamic scene stitching is formulated as a cut path optimization problem in a constructed space-time graph. The cutting energy function for column width selections is defined according to the proposed visual cognition model. The optimum cut path can minimize the cognitive saliency difference throughout the whole video volume. The experimental results show that it can effectively avoid synthetic defects caused by different motion interferences and summarize the key contents of the scene without loss. The proposed method gives full play to the role of human visual cognitive mechanism for the stitching. It is of high practical value to environmental surveillance and other applications.