Hyperspectral Data Acquisition and Its Application for Face Recognition

Current face recognition systems are rife with serious challenges in uncontrolled conditions: e.g., unrestrained lighting, pose variations, accessories, etc. Hyperspectral imaging (HI) is typically employed to counter many of those challenges, by incorporating the spectral information within different bands. Although numerous methods based on hyperspectral imaging have been developed for face recognition with promising results, three fundamental challenges remain: 1) low signal to noise ratios and low intensity values in the bands of the hyperspectral image specifically near blue bands; 2) high dimensionality of hyperspectral data; and 3) inter-band misalignment (IBM) correlated with subject motion during data acquisition. This dissertation concentrates mainly on addressing the aforementioned challenges in HI. First, to address low quality of the bands of the hyperspectral image, we utilize a custom light source that has more radiant power at shorter wavelengths and properly adjust camera exposure times corresponding to lower transmittance of the filter and lower radiant power of our light source. Second, the high dimensionality of spectral data imposes limitations on numerical analysis. As such, there is an emerging demand for robust data compression techniques with lows of less relevant information to manage real spectral data. To cope with these challenging problems, we describe a reduced-order data modeling technique based on local proper orthogonal decomposition in order to compute low-dimensional models by projecting high-dimensional clusters onto subspaces spanned by local reduced-order bases. Third, we investigate 11 leading alignment approaches to address IBM correlated with subject motion during data acquisition. To overcome the limitations of the considered alignment approaches, we propose an accurate alignment approach ( A3) by incorporating the strengths of point correspondence and a low-rank model. In addition, we develop two qualitative prediction models to assess the alignment quality of hyperspectral images in determining improved alignment among the conducted alignment approaches. Finally, we show that the proposed alignment approach leads to promising improvement on face recognition performance of a probabilistic linear discriminant analysis approach

Surface Denoising based on Normal Filtering in a Robust Statistics Framework

During a surface acquisition process using 3D scanners, noise is inevitable and an important step in geometry processing is to remove these noise components from these surfaces (given as points-set or triangulated mesh). The noise-removal process (denoising) can be performed by filtering the surface normals first and by adjusting the vertex positions according to filtered normals afterwards. Therefore, in many available denoising algorithms, the computation of noise-free normals is a key factor. A variety of filters have been introduced for noise-removal from normals, with different focus points like robustness against outliers or large amplitude of noise. Although these filters are performing well in different aspects, a unified framework is missing to establish the relation between them and to provide a theoretical analysis beyond the performance of each method. In this paper, we introduce such a framework to establish relations between a number of widely-used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising. We cover robust statistical estimation with M-smoothers and their application to linear and non-linear normal filtering. Although these methods originate in different mathematical theories - which include diffusion-, bilateral-, and directional curvature-based algorithms - we demonstrate that all of them can be cast into a unified framework of robust statistics using robust error norms and their corresponding influence functions. This unification contributes to a better understanding of the individual methods and their relations with each other. Furthermore, the presented framework provides a platform for new techniques to combine the advantages of known filters and to compare them with available methods

The curvelet transform for image denoising

We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A central tool is Fourier-domain computation of an approximate digital Radon transform. We introduce a very simple interpolation in the Fourier space which takes Cartesian samples and yields samples on a rectopolar grid, which is a pseudo-polar sampling set based on a concentric squares geometry. Despite the crudeness of our interpolation, the visual performance is surprisingly good. Our ridgelet transform applies to the Radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain. Our curvelet transform uses our ridgelet transform as a component step, and implements curvelet subbands using a filter bank of a` trous wavelet filters. Our philosophy throughout is that transforms should be overcomplete, rather than critically sampled. We apply these digital transforms to the denoising of some standard images embedded in white noise. In the tests reported here, simple thresholding of the curvelet coefficients is very competitive with "state of the art" techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms and also including tree-based Bayesian posterior mean methods. Moreover, the curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems. The empirical results reported here are in encouraging agreement

Cramer-Rao bounds for nonparametric surface reconstruction from range data

technical reportThe Cramer-Rao error bound provides a fundamental limit on the expected performance of a statistical estimator. The error bound depends on the general properties of the system, but not on the specific properties of the estimator or the solution. The Cramer-Rao error bound has been applied to scalar- and vector-valued estimators and recently to parametric shape estimators. However, nonparametric, low-level surface representations are an important important tool in 3D reconstruction, and are particularly useful for representing complex scenes with arbitrary shapes and topologies. This paper presents a generalization of the Cramer-Rao error bound to nonparametric shape estimators. Specifically, we derive the error bound for the full 3D reconstruction of scenes from multiple range images

Spatial mapping of band bending in semiconductor devices using in-situ quantum sensors

Band bending is a central concept in solid-state physics that arises from local variations in charge distribution especially near semiconductor interfaces and surfaces. Its precision measurement is vital in a variety of contexts from the optimisation of field effect transistors to the engineering of qubit devices with enhanced stability and coherence. Existing methods are surface sensitive and are unable to probe band bending at depth from surface or bulk charges related to crystal defects. Here we propose an in-situ method for probing band bending in a semiconductor device by imaging an array of atomic-sized quantum sensing defects to report on the local electric field. We implement the concept using the nitrogen-vacancy centre in diamond, and map the electric field at different depths under various surface terminations. We then fabricate a two-terminal device based on the conductive two-dimensional hole gas formed at a hydrogen-terminated diamond surface, and observe an unexpected spatial modulation of the electric field attributed to a complex interplay between charge injection and photo-ionisation effects. Our method opens the way to three-dimensional mapping of band bending in diamond and other semiconductors hosting suitable quantum sensors, combined with simultaneous imaging of charge transport in complex operating devices.Comment: This is a pre-print of an article published in Nature Electronics. The final authenticated version is available online at https://dx.doi.org/10.1038/s41928-018-0130-