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1.	Öfverstedt, Johan, et al. (author) Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection 2017 In: Mathematical Morphology and its Applications to Signal and Image Processing. - Cham : Springer. - 9783319572390 - 9783319572406 ; , s. 395-407 Conference paper (peer-reviewed)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.

Öfverstedt, Johan, et al. (author)
Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection
2017
In: Mathematical Morphology and its Applications to Signal and Image Processing. - Cham : Springer. - 9783319572390 - 9783319572406 ; , s. 395-407
Conference paper (peer-reviewed)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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