It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

Stress responses play a critical role in the ecology and demography of wild animals, and the analysis of fecal hormone metabolites is a powerful noninvasive method to assess the role of stress. We characterized the metabolites of injected radiolabeled cortisol in the urine and feces of Columbian ground squirrels and validated an enzyme immunoassay for measuring fecal cortisol metabolites (FCM) ...

This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to larger adjacent clusters. In order to overcome this, a modified version of FCM, called Conditional FCM, is used to balance the different sized clus...

FCM is one of a conventional clustering method and has been generally applied for medical image segmentation. On the other hand, conventional FCM at all times suffers from noise in the images. Even though the unique FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. The most important shortcomin...

The weighting exponent m is called the fuzzifier that can have influence on the clustering performance of fuzzy c-means (FCM) and m∈ [1.5,2.5] is suggested by Pal and Bezdek [13]. In this paper, we will discuss the robust properties of FCM and show that the parameter m will have influence on the robustness of FCM. According to our analysis, we find that a large m value will make FCM more robust...

Fuzzy c-means (FCM) clustering is based on minimizing the fuzzy within cluster scatter matrix trace but FCM neglects the between cluster scatter matrix trace that controls the distances between the class centroids. Based on the principle of cluster centers separation, fuzzy cluster centers separation (FCCS) clustering is an extended fuzzy c-means (FCM) clustering algorithm. FCCS attaches import...

Geometry conforming meshing techniques such as the finite element method (FEM) face a big challenge when dealing with complex and heterogeneous microstructures. Therefore, efficient simulation methods are needed combining accurate morphological reproducibility computational efficiency. For problems, cell (FCM) is promising approach, which uses Cartesian grid – independent of geometry leading to...