Image mosaicking using improved auto-sorting algorithm and local difference-based harris features

Abstract

Image mosaicking is an image processing technique which is useful for tiling images. Image Mosaicing stitches many correlated images to get a picture of a greater field of view. General-purpose cameras, which have a low field of view, can not create images with a higher field of view while mosaicking can help us achieve it. One important step in an image mosaicking framework is the auto-sorting algorithm, which is to be performed to minimize registration errors in the mosaic image. Another step in mosaicking is the detection of interest points for matching of the source images obtained after auto-sorting. However, in the presence of noisy and pseudo-periodic structures in the source images, the existing auto-sorting methods generally produce distortions in the final mosaic image. Secondly, most of the popular interest point detection algorithms do not specifically consider computational issues. So, this work mainly addresses the above-mentioned problems which are generally encountered during image mosaicking. The problem of image auto-sorting can be partially solved by adopting a phase correlation strategy. In our method, the sorting procedure is further improved by deploying the structural similarity index (SSIM) measure instead of using the phase correlation. The issue of high time complexity of conventional corner detectors is reduced by using our proposed local difference operation in place of standard Sobel edge detector. Experimental results show the efficacy of the proposed method.