Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixe...
— In the last decade, graph-cut optimization has been popular for a variety of labeling problems. Typically graph-cut methods are used to incorporate smoothness constraints on a ...
We present Low Power Illinois scan architecture (LPILS) to achieve power dissipation and test data volume reduction, simultaneously. By using the proposed scan architecture, dynam...
We investigate the supereulerian graph problems within planar graphs, and we prove that if a 2-edge-connected planar graph G is at most three edges short of having two edge-disjoi...
—A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods....