Superpixel-based-Fast-Fuzzy-C-Means-Clustering-for-Color-Image-Segmentation

We propose a superpixel-based fast FCM (SFFCM) for color image segmentation. The proposed algorithm is able to achieve color image segmentation with a very low computational cost, yet achieve a high segmentation precision.

Significantly-Fast-and-Robust-FCM-Based-on-Morphological-Reconstruction-and-Membership-Filtering

A fast and robust fuzzy c-means clustering algorithms, namely FRFCM, is proposed. The FRFCM is able to segment grayscale and color images and provides excellent segmentation results.

Robust-Self-Sparse-Fuzzy-Clustering-for-Image-Segmentation

Our paper “Robust Self-Sparse Fuzzy Clustering for Image Segmentation” has been accepted for publication in IEEE Access. 2020.

Adaptive-Morphological-Reconstruction-for-Seeded-Image-Segmentation

In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed. AMR is able to obtain better seed images to improve the seeded segmentation algorithms.

Automatic-Fuzzy-Clustering-Framework-for-Image-Segmentation

We proposed an automatic fuzzy clustering framework (AFCF) for image segmentation which is published in Transactions on Fuzzy Systems, 2020.