Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

International audienceThis paper proposes a novel method for fully automatic anomaly detection on objects inspected using an imaging system. In order to address the inspection of a wide range of objects and to allow the detection of any anomaly, an original adaptive linear parametric model is proposed; The great flexibility of this adaptive model offers highest accuracy for a wide range of complex surfaces while preserving detection of small defects. In addition, because the proposed original model remains linear it allows the application of the hypothesis testing theory to design a test whose statistical performances are analytically known. Another important novelty of this paper is that it takes into account the specific heteroscedastic noise of imaging systems. Indeed, in such systems, the noise level depends on the pixels’ intensity which should be carefully taken into account for providing the proposed test with statistical properties. The proposed detection method is then applied for wheels surface inspection using an imaging system. Due to the nature of the wheels, the different elements are analyzed separately. Numerical results on a large set of real images show both the accuracy of the proposed adaptive model and the sharpness of the ensuing statistical test

Detection and Quantification of Antiviral Drug Tenofovir Using Silver Nanoparticles and Surface Enhanced Raman Spectroscopy (SERS) With Spatially Resolved Hotspot Selection

This study introduces a convenient and ultra-sensitive method of detection and quantification of the antiviral drug, tenofovir (TFV), by surface-enhanced Raman spectroscopy (SERS). Novel spatially resolved instrumentation for spectral acquisition and subsequent statistical analysis for hot spot selection was developed for convenient quantification of TFV in an aqueous matrix. Methods of statistical analysis include the use of partial least squares (PLS) regression vector analysis and spectral ranking by quality indices computed using CHAOS theory. Hydroxylamine-reduced Ag colloidal nanoparticles evaporated to dryness on an aluminum well-plate were used as the SERS substrate. To our knowledge, quantification of TFV down to 25 ng/mL by SERS, comprising clinically relevant concentrations, has not been previously reported. Furthermore, in this work we propose a novel method of quantification of aqueous TFV standards by SERS using statistical treatment of data by PLS and CHAOS theory. Based on these data, we propose future studies to develop a method of TFV detection and quantification in biological samples, beneficial to clinicians for rapid assessment of drug adherence during the treatment and prevention of viral diseases

Ship Detection and Segmentation using Image Correlation

There have been intensive research interests in ship detection and segmentation due to high demands on a wide range of civil applications in the last two decades. However, existing approaches, which are mainly based on statistical properties of images, fail to detect smaller ships and boats. Specifically, known techniques are not robust enough in view of inevitable small geometric and photometric changes in images consisting of ships. In this paper a novel approach for ship detection is proposed based on correlation of maritime images. The idea comes from the observation that a fine pattern of the sea surface changes considerably from time to time whereas the ship appearance basically keeps unchanged. We want to examine whether the images have a common unaltered part, a ship in this case. To this end, we developed a method - Focused Correlation (FC) to achieve robustness to geometric distortions of the image content. Various experiments have been conducted to evaluate the effectiveness of the proposed approach.Comment: 8 pages, to be published in proc. of conference IEEE SMC 201

Doctor of Philosophy

dissertationThis dissertation focuses on the study of surface biomolecular interactions using second harmonic generation (SHG) spectroscopy, surface SHG imaging (SSHGI), and SH correlation spectroscopy (SHCS). The binding kinetics and energetics of four biotinbound proteins, avidin, streptavidin, neutrAvidin, and anti-biotin antibody were compared and data revealed significant differences in their apparent binding affinities and nonspecific binding. Specifically, protein-protein interactions were found to play an important role in the apparent binding affinity, making the streptavidin-biotin interaction the most energetically favorable. The details of the binding properties of these frequently employed tether/linker protein-biotin complexes provide valuable information for biosensors, immunoassays, and medical diagnostics. As most biosensor platforms are designed for high throughput detection, the resolution and planar wave-front of the SSHGI system was thoroughly analyzed. It was demonstrated that the coherent plane wave generated by SHG followed Gaussian beam propagation, enabling SSHGI to image without a lens system at rather long distances. Lens-less imaging simplifies the detection method, increases photon collection efficiency, and increases the detection area. These advantages could potentially make SSHGI a simple, label-free high throughput detection method for surface biomolecular interactions. The versatility and sensitivity of SHG were further probed by coupling SHG with correlation spectroscopy, a statistical fluctuation time-dependent method. SHCS was established as a viable and valuable option for the detection of surface binding kinetics for small molecule and protein-ligand interactions at the surface of lipid bilayers. First, the simple binding kinetics of a small molecule, (s)-(+)-1,1'-bi-2-napthol (SBN), incorporating into a lipid bilayer was determined using SHCS and results were statistically similar to those obtained from a traditional binding isotherm. Next, SHCS was used to examine the binding kinetics of a more complex interaction between the multivalent proteins, cholera toxin subunit b (CTb) and peanut agglutinin (PnA), and a GM1 doped lipid bilayer. SHCS was able to obtain the binding kinetics for these surface biomolecular interactions with more efficiency, less analyte, and less sensitivity to mass transport effects. Cumulatively, the studies of this dissertation showcase SHG, SSHGI, and SHCS as valuable label-free detection methods with incredible sensitivity for investigation of surface biomolecular interactions

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms

Road Surface Recognition at mm-Wavelengths Using a Polarimetric Radar

We demonstrate detection of ice formations on a road surface using a polarimetric radar operating at 87.5-92.5 GHz. The radar measures the scattering parameters of the surface at horizontal and vertical polarizations and their cross-polarization components. We demonstrate detection of ice for radar beam directed at up to 45⁰ angle of incidence with respect to the surface which allows for road surface characterization in front of a vehicle. The method used is based on a statistical approach where the 2-port scattering parameters are measured multiple times and used to calculate an average scatter coherence matrix representing the surface. The coherence matrix is then decomposed to eigenvalues/vectors, which are used to estimate polarimetric attributes such as target entropy(degree of randomness) and polarimetric pedestal (degree of depolarization). Through measurements of dry, ice-covered and wet road surfaces, we show that both entropy and depolarization are increased with respect to dry surface when a thin ice layer is formed, while their value decrease for the case of wet surface. It is also shown that these polarimetric attributes are not sensitive to surface roughness in dry conditions, minimizing the probability of false alarm due to road surface wear

Stellar activity as noise in exoplanet detection I. Methods and application to solar-like stars and activity cycles

The detection of exoplanets using any method is prone to confusion due to the intrinsic variability of the host star. We investigate the effect of cool starspots on the detectability of the exoplanets around solar-like stars using the radial velocity method. For investigating this activity-caused "jitter" we calculate synthetic spectra using radiative transfer, known stellar atomic and molecular lines, different surface spot configurations, and an added planetary signal. Here, the methods are described in detail, tested and compared to previously published studies. The methods are also applied to investigate the activity jitter in old and young solar-like stars, and over a solar-like activity cycles. We find that the mean full jitter amplitude obtained from the spot surfaces mimicking the solar activity varies during the cycle approximately between 1 m/s and 9 m/s. With a realistic observing frequency a Neptune mass planet on a one year orbit can be reliably recovered. On the other hand, the recovery of an Earth mass planet on a similar orbit is not feasible with high significance. The methods developed in this study have a great potential for doing statistical studies of planet detectability, and also for investigating the effect of stellar activity on recovered planetary parameters.Comment: Accepted to MNRA