A Recognition System for Subjects' Signature Using the Spatial Distribution of Signature Body

This investigation proposed an identification system of offline signature by utilizing rotation compensation depending on the features that were saved in the database. The proposed system contains five principle stages, they are: (1) data acquisition, (2) signature data file loading, (3) signature preprocessing, (4) feature extraction, and (5) feature matching. The feature extraction includes determination of the center point coordinates, and the angle for rotation compensation (θ), implementation of rotation compensation, determination of discriminating features and statistical condition. During this work seven essential collections of features are utilized to acquire the characteristics: (i) density (D), (ii) average (A), (iii) standard deviation (S) and integrated between them (iv) density and average (DA), (v) density and standard deviation (DS), (vi) average and standard deviation (AS), and finally (vii) density with average and standard deviation (DAS). The determined values of features are assembled in a feature vector used to distinguish signatures belonging to different persons. The utilized two Euclidean distance measures for matching stage are: (i) normalized mean absolute distance (nMAD) (ii) normalized mean squared distance (nMSD). The suggested system is tested by a public dataset collect from 612 images of handwritten signatures. The best recognition rate (i.e., 98.9%) is achieved in the proposed system using number of blocks (21×21) in density feature set. With the same number of blocks (i.e., 21×21) the maximum verification accuracy obtained is (100%).

Improving the Topside Profile of Ionosonde with TEC Retrieved from Spaceborne Polarimetric SAR

Signals from spaceborne polarimetric synthetic aperture radar will suffer from Faraday rotations when they propagate through the ionosphere, especially those at L-band or lower frequencies, such as signals from the Phased Array type L-band Synthetic Aperture Radar (PALSAR). For this reason, Faraday rotation compensation should be considered. On the other hand, Faraday rotation could also be retrieved from distorted echoes. Moreover, combining Faraday rotation with the radar parameters and the model of magnetic field, we could derive the total electron content (TEC) along the signal path. Benefiting from the high spatial resolution of the SAR system, TEC obtained from PALSAR could be orders of magnitude higher in spatial resolution than that from GPS. Besides, we demonstrated that the precision of TEC from PALSAR is also much higher than that from GPS. With the precise TEC available, we could fuse it with data from other ionosphere detection devices to improve their performances. In this paper, we adopted it to help modify the empirically modeled topside profile of ionosonde. The results show that the divergence between the modified profile and the referenced incoherent scattering radar profile reduced by about 30 percent when compared to the original ionosonde topside profile.

Global and Local Oriented Edge Magnitude Patterns for Texture Classification

In this paper, we propose a gray-scale texture descriptor, name the global and local oriented edge magnitude patterns (GLOEMP), for texture classification. GLOEMP is a framework, which is able to effectively combine local texture, global structure information and contrast of texture images. In GLOEMP, the principal orientation is determined by Histogram of Gradient (HOG) feature, then each direction is respectively shown in detail by a local binary patterns (LBP) occurrence histogram. Due to the fact that GLOEMP characterizes image information across different directions, it contains very abundant information. The global-level rotation compensation method is proposed, which shifts the principal orientation of the HOG to the first position, thus allowing GLOEMP to be robust to rotations. In addition, gradient magnitudes are used as weights to add to the histogram, making GLOEMP robust to lighting variances as well, and it also possesses a strong ability to express edge information. The experimental results obtained from the representative databases demonstrate that the proposed GLOEMP framework is capable of achieving significant improvement, in some cases reaching classification accuracy of 10% higher than over the traditional rotation invariant LBP method.