Abstract
The geodesic distance is commonly used when solving image processing problems. In noisy images, unfortunately, it often gives unsatisfactory results. In this paper, we propose a new k-max geodesic distance. The length of path is defined as the sum of the k maximum edge weights along the path. The distance is defined as the length of the path that is the shortest one in this sense. With an appropriate choice of the value of k, the influence of noise can be reduced substantially. The positive properties are demonstrated on the problem of seeded image segmentation. The results are compared with the results of geodesic distance and with the results of the random walker segmentation algorithm. The influence of k value is also discussed.
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Holuša, M., Sojka, E. (2015). A k-max Geodesic Distance and Its Application in Image Segmentation. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_52
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DOI: https://doi.org/10.1007/978-3-319-23192-1_52
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