Shape description and matching using integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately

Evaluation of magnetic field’s uniformity inside electromagnetic coils using graphene

The distribution of the magnetic field in electromagnetic coils, such as those employed in magnetic resonance imaging (MRI), is evaluated in this paper, through graphene gyrotropic properties. Initially, the rotation of an incident linearly polarized plane wave, due to an infinite graphene layer, is studied theoretically via the extraction of the perpendicular, to the polarization, electric component of the transmitted wave. Moreover, the influence of the magnetic bias field strength on this component is, also, examined, indicating the eligibility of graphene to detect magnetostatic field variations. To this aim, a specific device is proposed, consisting of a high frequency source, an electric field detector, and a finite graphene sheet that differs from the infinite one of the analytical case. To quantify the distance that the gyrotropic effects are detectable, the effective region is introduced and extracted via a properly modified finite-difference time-domain (FDTD) algorithm. The featured device is verified through a setup comprising a uniform electromagnetic coil, where the generated magnetostatic field is calculated at several cross-sections of the coil and compared to actual field values. Results indicate the accuracy and sensitivity of the designed device for the unambiguous regions

Mobile-Based Painting Photo Retrieval Using Combined Features

In paintings or artworks, sharing a photo of a painting using mobile phone is simple and fast. However, searching for information about specific captured photo of an unknown painting takes time and is not easy. No previous developments were introduced in the content-based indexing and retrieval (CBIR) field to ease the inconvenience of knowing the name and other information about an unknown painting through capturing photos by mobile phones. This work introduces an image retrieval framework on art paintings using shape, texture and color properties. With existing state-of-the-art developments, the proposed framework focuses on utilizing a feature combination of: generic Fourier descriptors (GFD), local binary patterns (LBP), Gray-level co-occurrence matrix (GLCM), and HSV histograms. After that, Locality Sensitive Hashing (LSH) method is used for image indexing and retrieval of paintings. The results are validated over a public database of seven different categories