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- W823444901 abstract "In this paper, we apply generalized weighted mean to construct interval-valued fuzzy relations for grayscale image edge detection and derive the learning formulas for parameters in order to decrease the edge detection error. The proposed detector consists of three stages. In the first stage, we use the upper and lower constructors to calculate the weighted mean aggregations of the central pixel and its eight neighbor pixels in each sliding window. Then we construct the interval-valued fuzzy relation and its associated W-fuzzy relation indicating the degree of intensity variation between the center pixel and its neighborhood. In the second stage, we update the weighting parameters of the mean which can be learned by the gradient method casted in discrete formulation and utilize pocket algorithm to obtain the optimal parameter set for all training images. Finally, we use post-processing techniques to strengthen the connectivity of edges and remove isolated pixels for obtaining better edge images. Our method produces a more stable and robust edge images on synthetic images and nature images as well, in comparison with the well-known Canny edge detector." @default.
- W823444901 created "2016-06-24" @default.
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- W823444901 date "2013-01-01" @default.
- W823444901 modified "2023-09-26" @default.
- W823444901 title "Applying Weighted Generalized Mean Aggregation and Learning Rule to Edge Detection of Images" @default.
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