Stitching of heterogeneous images using depth information

We propose a novel heterogeneous image stitching algorithm, which employs disparity information as well as color information. It is challenging to stitch heterogeneous images that have different background colors and diverse foreground objects. To overcome this difficulty, we set the criterion that objects should preserve their shapes in the stitched image. To satisfy this criterion, we derive an energy function using color and disparity gradients. As the gradients are highly correlated with object boundaries, we can find the optimal seam from the energy function, along which two images are pasted. Moreover, we develop a retargeting scheme to reduce the size of the stitched image further. Experimental results demonstrate that the proposed algorithm is a promising tool for stitching heterogeneous images.