Difference Of Gaussian Function Research Articles

Recognition of Tunnel Lining Cracks Based on Digital Image Processing

Most of the tunnel projects are related to the national economy and people’s livelihood, and their operational safety is of paramount importance. Tunnel safety accidents or hidden safety hazards often start from subtleties. Therefore, the identification of tunnel cracks is a key part of tunnel safety control. The development of computer vision technology has made it possible for the automatic detection of tunnel cracks. Aiming at the problem of low recognition accuracy of existing crack recognition algorithms, this paper uses an improved homomorphic filtering algorithm to dehaze and clear the collected images according to the characteristics of tunnel images and uses an adaptive median filter to denoise the grayscaled image. The extended difference of Gaussian function is used for edge extraction, and the morphological opening and closing operations are used to remove noise. The breakpoints of the binary image are connected after removing the noise to make the image more in line with the actual situation. Aiming at the identification of tunnel crack types, the block index is proposed and used to distinguish linear cracks and network cracks. Using the histogram-like method to distinguish linear cracks in different directions can well solve the mixed crack situation in an image. Compared with the traditional method, the recognition rate of the new algorithm is increased to 94.5%, which is much higher than the traditional crack recognition algorithm. The average processing time of an image is 5.2 s which is moderate, and the crack type discrimination accuracy is more than 92%. In general, the new algorithm has good prospects for theoretical promotion and high engineering application value.

Noise sharing and Mexican-hat coupling in a stochastic neural field.

A diffusion-type coupling operator that is biologically significant in neuroscience is a difference of Gaussian functions (Mexican-hat operator) used as a spatial-convolution kernel. We are interested in pattern formation by stochastic neural field equations, a class of space-time stochastic differential-integral equations using the Mexican-hat kernel. We explore quantitatively how the parameters that control the shape of the coupling kernel, the coupling strength, and aspects of spatially smoothed space-time noise influence the pattern in the resulting evolving random field. We confirm that a spatial pattern that is damped in time in a deterministic system may be sustained and amplified by stochasticity. We find that spatially smoothed noise alone causes pattern formation even without direct spatial coupling. Our analysis of the interaction between coupling and noise sharing allows us to determine parameter combinations that are optimal for the formation of spatial pattern.

Fluorescence correlation spectroscopy with a doughnut-shaped excitation profile as a characterization tool in STED microscopy.

The resolution of stimulated emission depletion (STED) microscopes is ultimately limited by the quality of the doughnut-shaped illumination profile of the STED erase beam. We show here that in the focal plane this illumination profile is well approximated by an analytical expression - a difference of Gaussian functions, which tends towards a first order Laguerre-Gaussian profile in the case of a well aligned beam with a true zero-intensity central minimum. We further show that along the optical axis the maximum intensity profile is reasonably approximated by a Gaussian decay away from the focal plane. The result is a fully Gaussian analytical approximation of the three-dimensional point-spread function of STED erase beams. This allows the derivation of an analytical form for the autocorrelation function of the fluorescence generated by fluorophore diffusion through the STED depletion volume. We verified this form to be correct by performing fluorescence correlation spectroscopy (FCS) experiments in solutions of the dye Alexa Fluor 532. Since the quality of the illumination profile is reflected in the shape of the autocorrelation function, we propose that fluctuation analysis can be used as a tool to assess the quality of STED erase beams.

An Automatic Baseline Extraction Algorithm for Intensity Absorption Type Gas Sensing

A novel algorithm for automatic baseline extraction of gas absorption spectrum is proposed. It is based on an automatic signal-free regions recognition that uses a difference of Gaussians function, followed by a baseline fitting procedure based on segmented polynomial fitting and segmented kernel smoothing. This algorithm has been applied to both simulated data and experimental data. The results show that this algorithm can extract baseline of spectrum with a wide range of possible baseline types, is tolerant to low signal-to-noise ratio spectrum, and to signals of varying widths in a single spectrum. Moreover, the efficiency of the algorithm is tested by accurately detecting the absorbance of experimental CO 2 absorption spectrum and C 2H 2 absorption spectrum.

A functional model of spectral orientation columns in the primary auditory cortex.

A functional model of the primary auditory cortex is proposed based on physiological maps of the response area organization in ferret AI [S. A. Shamma, J. W. Fleshman, and P. Wiser, J. Neurophysiol. (submitted for publication)]. Systematic changes in the excitatory and inhibitory portions of the response areas along the isofrequency planes are approximated by a difference of Gaussian function with spatially changing parameters. The fundamental functional principle that emerges from the analysis of the model is that the primary auditory cortex encodes the shape of the acoustic spectrum in the distribution of its responses along the isofrequency planes. Specifically, it maps to each isofrequency plane a normalized measure of the locally averaged gradient of the input spectrum at that frequency. Physiological and psychoacoustical correlates and implications of these findings are explained. Parallels to the functional organization of the visual cortex are also discussed.