Robust Image Segmentation Using Fuzzy C-Means Clustering With Spatial Information Based on Total Generalized Variation

Abstract

Fuzzy c-means clustering (FCM) has proved highly successful in the manipulation and analysis of image information, such as image segmentation. However, the effectiveness of FCM-based technique is limited by its poor robustness to noise and edge-preserving during the segmentation process. To tackle these problems, a new objective function of FCM is developed in this work. The main innovation work and results of this paper are outlined as follows. First, a regularization operation performed by total generalized variation (TGV) is used to guarantee noise smoothing and detail preserving. Second, a weight factor incorporated into the spatial information term is designed to form nonuniform membership functions, which can contribute to the assignment of each pixel for the highest membership value. In addition, a regularization parameter is used to balance the respective importance of penalty between whole image and each neighborhood. The main advantage of this technique over conventional FCM-based methods is that it can reconstruct image patterns in heavy noise with only a small loss. We perform experiments on both synthetic and real images. Compared to state-of-the art FCM-based methods, the proposed algorithm exhibits a very good ability to noise and edge-preserving in image segmentation.

Recognition of Aircraft Wake Vortex Based on Random Forest

“…In this section, the Accuracy, Recall, Precision, F1_score, receiver operating characteristic (ROC) and area under ROC curve (AUC) [20][21][22][23] were used to evaluate the classification performance of the RF model when it is applied to the recognition of aircraft wake vortex. The Accuracy is calculated as the ratio between the number of correctly predicted samples to the total number of samples; Recall measures the ratio between the true positive (TP) to the total number of positive predicted samples; Precision is calculated as the ratio between the TP to the actual total number of positive samples; The F1_score conveys the balance between the Precision and the Recall; The ROC curves explore the effects on the true positive rate (TPR) and the false positive rate (FPR) as the position of an arbitrary decision threshold is varied, and the area under the ROC curve (AUC) with a larger value indicates more accurate recognition.…”

Deep learning-based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study: A case study: Part A: Deep learning-based underwater metal object detection using input image data

“…For practical purposes, the two weights and are tuned to and , respectively. Through the definition of second-order TGV, the proposed TGVFCMS can yield results that are more robust to noise and detail-preserving [26].…”

Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

“…The shorter the distance, the higher the grade of a MF. The steps of Fuzzy c-Means Clustering algorithm is available in [22][23][24]. In this paper, we take the received signal of PUs at FC under three categories: Hypothesis H 0 (absence of PU), Hypothesis H 1 (presence of PU) and Hypothesis H 0 + (intermediate result, usually applicable to malicious attack); where SUs are used as the relay stations.…”