Automatic Fuzzy Clustering Framework for Image Segmentation

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According to T. ,S k } should satisfy the following four main rules [10]: S is a partition of I that covers the whole image, no regions intersect, homogeneity predicate is satisfied

It is however possible to directly use the component-tree structure by taking advantage of the decomposition of the image into nodes/connected components that it provides, in order

In the following section, we propose a technique to correctly label such pixels (first for the pixels in cluster-2 and then for the pixels in cluster- 3) as either foreground

The experiment shows that the described approach of using a larger dimension of the hidden space with its further decomposition using the t-SNE method gives better separability

As algorithms for binary semantic segmentation, we used SegNet neural networks [7], a model with an encoder from the ResNet-50 network [8] and a decoder in the form of a

The quality of segmentation for each image has been estimated by means of Q criterion described above. We have tested algorithm parameters for the.. The examples of test images

Second, we generalize the enhanced fuzzy c-means algorithm [4] to color images (in fact any N -d image) by using a quantization of the color space.. Thus, we keep the ability of