Abstract
We present a novel approach to measuring distance between multi-channel images, suitably represented by vector-valued fuzzy sets. We first apply the intersection decomposition transformation, based on fuzzy set operations, to vector-valued fuzzy representations to enable preservation of joint multi-channel properties represented in each pixel of the original image. Distance between two vector-valued fuzzy sets is then expressed as a (weighted) sum of distances between scalar-valued fuzzy components of the transformation. Applications to object detection and classification on multi-channel images and heterogeneous object representations are discussed and evaluated subject to several important performance metrics. It is confirmed that the proposed approach outperforms several alternative single- and multi-channel distance measures between information-rich image/object representations.
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Acknowledgement
A. Suveer, A. Dragomir and I.-M. Sintorn are acknowledged for acquisition and annotation of cilia images. Ministry of Science of the Republic of Serbia is acknowledged for support through Projects ON174008 and III44006 of MI-SANU.
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Öfverstedt, J., Sladoje, N., Lindblad, J. (2017). Distance Between Vector-Valued Fuzzy Sets Based on Intersection Decomposition with Applications in Object Detection. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2017. Lecture Notes in Computer Science(), vol 10225. Springer, Cham. https://doi.org/10.1007/978-3-319-57240-6_32
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DOI: https://doi.org/10.1007/978-3-319-57240-6_32
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