Robust single-sample face recognition by sparsity-driven sub-dictionary learning using deep features

Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative \u2113 0 -norm minimization algorithm called k-LIMAPS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8 7 8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations

Application of Wavelet Analysis and Random Field in Integrity Management of Pipelines Containing Dents and Corrosions

Metal loss corrosions and dents are two major threats to the integrity of oil and natural gas pipelines. In the pipeline industry, the Fitness-For-Service (FFS) assessment is commonly employed for pipelines containing these defects. However, FFS assessment usually assumes that a defect has a simple shape, and such a simplification may significantly affect the accuracy of the assessment. Therefore, retaining the actual shapes of defects and incorporating them into the FFS assessment can improve assessment accuracy. The main objective of the present thesis is to extract key information about the sizes, directions, and shapes of corrosions and dents from the measurement of in-service and excavated pipelines, and then improve the accuracy of FFS assessment based on the extracted information. The first study develops a wavelet transform-based denoising method for the measured inner surface of in-service dented pipelines obtained from caliper tools. Since the inner surface is differently sampled along the longitudinal and circumferential directions, the commonly used denoising methods cannot sufficiently remove measurement errors from the signal. The proposed method is based on overcomplete expansion, and the overcomplete dictionary is constructed from the hyperbolic wavelet transform and stationary transform. The strain estimated from the signal denoised by the proposed method is closer to the actual strain than the other denoising method. An overcomplete dictionary that can effectively denoise the dent signal is then constructed based on the statistics of the measurement of in-service dented pipelines. The second study explores the vital directional features and length scales of natural corrosion clusters that govern the burst capacity of corroded pipelines. The corrosion depths in a cluster are measured by high-resolution laser scans, and two-dimensional (2D) discrete wavelet transform (DWT) with a suitable wavelet function is employed to decompose the corrosion cluster. A methodology is proposed to determine level- and sub-band-dependent thresholds such that those wavelet coefficients below the thresholds have a negligible impact on the burst capacity predicted by the widely used RSTRENG model and can be ignored for the reconstruction of the cluster. The preserved wavelet coefficients show that longitudinally orientated features with 4 – 32 mm in length have a greater influence on the remaining burst capacity than other features. This facilitates FFS assessment of corroded pipelines. The third study aims to simulate the corrosion fields whose morphology and marginal distribution are close to the actual corrosion fields from limited information summarized from the ILI data. The corrosion field containing multiple corrosion anomalies is modelled as a nonhomogeneous non-Gaussian random field, where the spatial correlation and marginal distribution of anomalies are estimated from their sizes. The proposed methodology provides realizations of corrosion fields with the RSTRENG-predicted burst capacity closer to the actual burst capacity than the commonly used methodology that idealizes anomalies as cuboids. The fourth study presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms. Continuous wavelet transform (CWT), dual-tree complex discrete wavelet transform (DT-CDWT), and dual-tree complex discrete wavelet with hyperbolic wavelet transform scheme (DT-CHWT) are incorporated into the iterative power and amplitude correction (IPAC) algorithm to extract the features of the natural corrosion field measured by a high-resolution laser scan and generate synthetic corrosion fields. The results indicate that the proposed framework can generate synthetic corrosion fields that effectively capture probabilistic characteristics of the measured corrosion field in terms of the scalogram, textural features, and burst capacity of the pipe segment containing the corrosion field

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Inspección visual automática de superficies continuas, caracterizando anomalías locales en el dominio Espacio-Frecuencial

Esta tesis propone una metodología para la inspección visual automática de superficies continuas que abarca las etapas de adquisición de imágenes, su procesamiento y la utilización de los resultados obtenidos. Su objetivo es determinar qué zonas de la superficie son defectuosas por alejarse de la homogeneidad esperada y cuál es el tipo de defecto presente. Para ello, se caracterizan anomalías en el dominio espacio-frecuencial, explotando las posibilidades que ofrece el filtro de Gabor. Se ha definido una metodología para el diseño de bancos de filtros de Gabor que analiza una zona del espacio de frecuencias y orientaciones. La información extraída por estos filtros son las características evaluadas en la detección y clasificación de defectos. Este enfoque general ha sido particularizado a la resolución de tres problemas reales de reconocida trascendencia: la inspección de bobinas de chapa de acero laminado, del pavimento de carreteras y del revestimiento de túneles de hormigón.Departamento de Ingeniería de Sistemas y Automátic