PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Seamless Image Stitching by Minimizing False Edges
Assaf Zomet, Anat Levin, Shmuel Peleg and Yair Weiss
IEEE Transactions on Image Processing 2005.

Abstract

Abstract—Various applications such as mosaicing and object insertion require stitching of image parts. The stitching quality is measured visually by the similarity of the stitched image to each of the input images, and by the visibility of the seam between the stitched images. In order to define and get the best possible stitching, we introduce several formal cost functions for the evaluation of the stitching quality. In these cost functions the similarity to the input images and the visibility of the seam are defined in the gradient domain, minimizing the disturbing edges along the seam. A good image stitching will optimize these cost functions, overcoming both photometric inconsistencies and geometric misalignments between the stitched images. We study the cost functions and compare their performance for different scenarios both theoretically and practically. Our approach is demonstrated in various applications including generation of panoramic images, object blending and removal of compression artifacts. Comparisons with existing methods show the benefits of optimizing the measures in the gradient domain.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1062
Deposited By:Yair Weiss
Deposited On:04 September 2005