On the introduction of canny operator in an advanced imaging algorithm for real-time detection of hyperbolas in ground-penetrating radar data

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB

Preliminary Analysis of Quality of Contour Lines Using Smoothing Algorithms

In this paper several well-known filtering techniques were compared in the purpose of automatic line generalization. The used methods for line simplification are digital first order low-pass filter, Savitzky-Golay (SG) filter and Whittaker filter. Two versions of the algorithm for line feature generalization were tested, from source scale 1:25 000 towards target scale of 1:100 000 and from source scale 1:25 000 towards scale of 1:50 000. Also, GPS data filtering for the target scale 1:50 000 was tested. The first version of the algorithm considers that there are no control data, and the filtering parameter is dictated by the desired accuracy for the target scale. The second version involves control data in the target scale. This means that the optimal value for the filtering parameter is the value for which the difference between input and control data is the smallest. Analysis showed that the SG filter yielded the best results in general. The proposed filters can be considered as a new solution for automated cartographic line simplification

Functional Magnetic Resonance Imaging of Motor Cortex Activation in Schizophrenia

Previous fMRI studies of sensorimotor activation in schizophrenia have found in some cases hypoactivity, no difference, or hyperactivity when comparing patients with controls; similar disagreement exists in studies of motor laterality. In this multi-site fMRI study of a sensorimotor task in individuals with chronic schizophrenia and matched healthy controls, subjects responded with a right-handed finger press to an irregularly flashing visual checker board. The analysis includes eighty-five subjects with schizophrenia diagnosed according to the DSM-IV criteria and eighty-six healthy volunteer subjects. Voxel-wise statistical parametric maps were generated for each subject and analyzed for group differences; the percent Blood Oxygenation Level Dependent (BOLD) signal changes were also calculated over predefined anatomical regions of the primary sensory, motor, and visual cortex. Both healthy controls and subjects with schizophrenia showed strongly lateralized activation in the precentral gyrus, inferior frontal gyrus, and inferior parietal lobule, and strong activations in the visual cortex. There were no significant differences between subjects with schizophrenia and controls in this multi-site fMRI study. Furthermore, there was no significant difference in laterality found between healthy controls and schizophrenic subjects. This study can serve as a baseline measurement of schizophrenic dysfunction in other cognitive processes

Automated data extraction from synthetic and real radargrams of complex structures

This paper presents a comparative study of two algorithms for detecting and analyzing the characteristic shapes of reflection obtained as a result of Ground-Penetrating Radar (GPR) scanning technology. The first algorithm is a sub-array processing method that uses direction-of arrival algorithms and the matched filter technique; this approach is implemented in SPOT-GPR (release 1.0), a new freeware tool for the detection and localization of targets in radargrams. The second algorithm, APEX, is based on machine learning and pattern recognition techniques and it allows finding the coordinates of apexes and further characteristic points of hyperbolas in radargrams. Both software solutions are implemented in MATLAB environment. As a first step, we compare the accuracy of our algorithms when applied to synthetic data, calculated by using the open-source finite-difference time-domain simulator gprMax; the scenarios are two concrete cells hosting different metallic and dielectric targets. Then, we compare the accuracy of our algorithms when applied to experimental data, recorded over district heating pipes in a trench, with known geometry and depth of the pipes. For the latter scenario, we have also generated a gprMax radargram, matching the geometry and scanning settings of the real one; both algorithms are tested on this synthetic radargram, as well. Overall, both algorithms perform well and rather uniformly in localizing the targets. The accuracy of the algorithms is at centimeter level, which is sufficient in most applications