Abstract
Image Stitching refers to the technology fusing more than one images with overlapping part into a large field of view image. Image mosaic consists of image preprocessing, image registration and image fusion. To solve problems of serious clustering phenomenon and fewer corner points in the texture region caused by traditional Harris Corner detection algorithm, this paper proposes an improving adaptive threshold setting algorithm by calculating the second-order value of the corner response function, avoiding effects of the selection of scale factor k and threshold T on corner detection. To overcome the weakness of obvious traces in the jointing places caused by traditional weighted average method for image fusion, this paper enhances the weighted average method with trigonometric functions. Experimental results show our proposed algorithms can effectively eliminate the gap generated by image mosaic, with a better speed and precision.
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Pan, H., Zhang, Y., Li, C., Wang, H. (2014). An Adaptive Harris Corner Detection Algorithm for Image Mosaic. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_6
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DOI: https://doi.org/10.1007/978-3-662-45643-9_6
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