This article proposes offline language-free writer identification based on speeded-up robust features (SURF), goes through training, enrollment, and identification stages. In all stages, an isotropic Box filter is first used to segment the handwritten text image into word regions (WRs). Then, the SURF descriptors (SUDs) of word region and the corresponding scales and orientations (SOs) are extr...

Two-dimensional (2D) adaptive filtering is a technique that can be applied to many image and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to 2D structure and the novel 2D adaptive filters are established. Based on this extension, the 2D variable step-size normalized least mean squares (2D-VSSNLMS), the 2D-VSS affine projection algorithms (2D-...

in this paper, using annular resonator we have designed an adding and dropping filter light based ontwo-dimensional photonic crystals. the shape of ring resonator filter adding and dropping that wehave proposed is race track. this filter has a hexagonal lattice structure of silicon bars withrefractive index 3/46 that is located in the context of air with refractive index 1. transmissionefficien...

The Exact Set Similarity Join problem aims to find all similar sets between two collections of sets, with respect to a threshold and a similarity function such as overlap, Jaccard, dice or cosine. The näıve approach verifies all pairs of sets and it is often considered impractical due the high number of combinations. So, Exact Set Similarity Join algorithms are usually based on the Filter-Verif...

The Gaussian filter has been used extensively in image processing and computer vision for many years. In this survey paper, we discuss the various features of this operator that make it the filter of choice in the area of edge detection. Despite these desirable features of the Gaussian filter, edge detection algorithms which use it suffer from many problems. We will review several linear and no...

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification. In order to improve robustness...

A new method to quantify the nonlinearity of data assimilation problems is proposed. The method includes the effects of system errors, measurement errors, observational network, and sampling interval. It is based on computation of the first neglected term in a ‘‘Taylor’’ series expansion of the errors introduced by an extended Kalman filter, and can be computed at very little cost when one is a...