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Image stitching aims to stitch multiple images with overlapping areas into a high-resolution image with natural appearance, no ghosting and no seam, so as to obtain a wide field of vision. And the stitching is required to be as fast as possible. In this article, we discuss the loss of foreground object caused by the seam-cut method for image stitching. Moreover, we propose an improved seam-cut method based on superpixel to solve this problem. The proposed method uses the matching information of feature points and superpixel segmentation to divide the scene into foreground and background. By adjusting the penalty value of foreground superpixels in the superpixel-based seam-cut method, the foreground object can avoid the cutting of optimal seam. The experimental results demonstrate that our method will not cause the ghosting of foreground object or the loss of foreground object in the stitching result in the scene with large parallax. In this method, the foreground object will be preserved completely. [ABSTRACT FROM AUTHOR]

• A novel framework of image stitching with large parallax based on disparity feature. • Stereo disparity estimation free from camera calibration. • Plane clustering and alignment to avoid artifacts due to discontinuous content. Image stitching aims to warp and align two or more images with overlapping areas to generate a panorama with a larger field of view. Due to the wide baseline or the abrupt depth changes caused by the foreground objects, the phenomenon that adjacent pixels or regions in one image are not adjacent in another image happens. It is difficult to avoid severe parallax artifacts and get good alignment results when stitching such images. In this paper, focusing on the images with large parallax, we design a multi-plane alignment algorithm guided by the disparity map. The concept of average tolerable parallax is proposed to help distinguish one background plane and multiple foreground objects from the image, which are robust to small parallax. In order to obtain the reliable disparity map of the image pairs got from any camera with unknown camera parameters, we propose an optimal homography estimation method based on the relative projection biases. This helps to satisfy the common epipolar line constraint when applying stereo matching modules. Experimental results demonstrate that our algorithm provides accurate stitching results on images with large parallax, and outperforms other existing state-of-the-art methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]