Domain-Size Pooling in Local Descriptors: DSP-SIFT

We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.Comment: Extended version of the CVPR 2015 paper. Technical Report UCLA CSD 14002

Self-referenced optical fiber sensor for hydrogen peroxide detection based on LSPR of metallic nanoparticles in layer-by-layer films

Intensity-based optical fiber sensors are one of the most studied sensor approaches thanks to their simplicity and low cost. Nevertheless, their main issue is their lack of robustness since any light source fluctuation, or unexpected optical setup variation is directly transferred to the output signal, which, significantly reduces their reliability. In this work, a simple and robust hydrogen peroxide (H2O2) optical fiber sensor is proposed based on the Localized Surface Plasmon Resonance (LSPR) sensitivity of silver and gold metallic nanoparticles. The precise and robust detection of H2O2 concentrations in the ppm range is very interesting for the scientific community, as it is a pathological precursor in a wide variety of damage mechanisms where its presence can be used to diagnose important diseases such as Parkinson's disease, diabetes, asthma, or even Alzheimer's disease). In this work, the sensing principle is based the oxidation of the silver nanoparticles due the action of the hydrogen peroxide, and consequently the reduction of the efficiency of the plasmonic coupling. At the same time, gold nanoparticles show a high chemical stability, and therefore provide a stable LSPR absorption band. This provides a stable real-time reference that can be extracted from the spectral response of the optical fiber sensor, giving a reliable reading of the hydrogen peroxide concentration.This work has been supported by the Spanish Economy and Competitiveness TEC2016-78047-R grant and the PhD research grants of the Public University of Navarre

Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth

We focus on electromagnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.Comment: Brain Topogr (2020

XRD Identification of Ore Minerals during Cruises: Refinement of Extraction Procedure with Sodium Acetate Buffer

The on-board identification of ore minerals during a cruise is often postponed until long after the cruise is over. During the M127 cruise, 21 cores with deep-seafloor sediments were recovered in the Trans-Atlantic Geotraverse (TAG) field along the Mid Atlantic Ridge (MAR). Sediments were analyzed on-board for physicochemical properties such as lightness (L*), pH and Eh. Selected samples were studied for mineral composition by X-ray powder diffraction (XRD). Based on XRD data, sediment samples were separated into high-, low- and non-carbonated. Removal of carbonates is a common technique in mineralogical studies in which HCl is used as the extraction agent. In the present study, sequential extraction was performed with sodium acetate buffer (pH 5.0) to remove carbonates. The ratio between the highest calcite XRD reflection in the original samples (Iorig) vs its XRD-reflection in samples after their treatment with the buffer (Itreat) was used as a quantitative parameter of calcite removal, as well as to identify minor minerals in carbonated samples (when Iorig/Itreat > 24). It was found that the lightness parameter (L*) showed a positive correlation with calcite XRD reflection in selected TAG samples, and this could be applied to the preliminary on-board determination of extraction steps with acetate buffer (pH 5.0) in carbonated sediment samples. The most abundant minerals detected in carbonated samples were quartz and Al- and Fe-rich clays. Other silicates were also detected (e.g., calcic plagioclase, montmorillonite, nontronite). In non-carbonated samples, Fe oxides and hydroxides (goethite and hematite, respectively) were detected. Pyrite was the dominant hydrothermal mineral and Cu sulfides (chalcopyrite, covellite) and hydrothermal Mn oxides (birnessite and todorokite) were mineral phases identified in few samples, whereas paratacamite was detected in the top 20 cm of the core. The present study demonstrates that portable XRD analysis makes it possible to characterize mineralogy at cored sites, in particular in both low- and high-carbonated samples, before the end of most cruises, thus enabling the quick modification of exploration strategies in light of new information as it becomes available in near-real time

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio