The aim of this work is to improve the classification of defects in X-ray inspection by developing a new method based on Dempster-Shafer data fusion theory where measured features on the detected objects are considered as information sources. From the histogram of features values on a learning database of manually classified objects, an automatic procedure is proposed to define a set of mass fu...

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...

NGC 4815 is a distant and populous open cluster, which lies in the galactic plane in a region of strong absorption. As a consequence, its membership, spatial distribution and luminosity function are not well determined. In this paper, we present an algorithm which uses both positional and photometric data simultaneously to search for open cluster members. The contribution of the field stars is ...

This paper, presents a new image segmentation method based on Wavelets, Particle Swarm Optimization (PSO) and outlier rejection caused by the membership function of the kernel fuzzy local information c-means (KFLICM) algorithm combined with level set is proposed. The segmentation of Magnetic Resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research, but...

Rough membership function defines the degree of relationship between conditional and decision attributes an information system. It is defined by where src=image/13429145_07.gif> subset src=image/13429145_08.gif> under relation src=image/13429145_09.gif> universe discourse. can be expressed in different forms like cardinality form, probabilistic form etc. In it as...

A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral variability and leveraging spatial information. In this work, we exte...