Noise removal is an important task inside the image processing area. In this paper, an algorithm for reducing salt and pepper noise and improving the restoration quality through refinement is presented. This algorithm proposes a double screen. As a first step, the algorithm computes an estimation of the denoised image by using an adaptive median filter. Then, the Non-Local Means algorithm is us...

the morphological river evolution at long-time (centuries and even millennia) and large spatial scale (watersheds of several square kilometres) can be described by means of simplified 1d models, able to simulate the variation of bed elevation and grain size composition at non-detailed scales, involving a reduced computational effort. the erosion and deposition phenomena acting along rivers can ...

A desired dynamic behavior of constrained manipulators can be achieved by means of impedance control and various implementations of fixed controllers have been proposed. In this paper, and adaptive implementation is presented as an alternative to reduce the design sensitivity due to manipulator mismatch. The adaptive controller globally achieves the impedance objective for the nonlinear dynamic...

The aim of this paper is to present a new proposal for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization method for getting initial solutions and the K-Means algorithm as a local search algorithm. The new proposal will become a new initializa...

We introduce the diffusion K-means clustering method on Riemannian submanifolds, which maximizes within-cluster connectedness based distance. The constructs a random walk similarity graph with vertices as data points randomly sampled manifolds and edges similarities given by kernel that captures local geometry of manifolds. is multi-scale tool suitable for non-linear non-Euclidean geometric fea...

We present a new approach for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization to get initial solutions and K-Means as a local search algorithm. The approach is a new initialization one for K-Means. Hence, we compare it with some typical ini...