A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

An Octree-Based Approach towards Efficient Variational Range Data Fusion

Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime. Especially for sparse geometric structures, volumetric representations produce a huge computational overhead. We present an efficient way to fuse range data via a variational Octree-based minimization approach by taking the actual range data geometry into account. We transform the data into Octree-based truncated signed distance fields and show how the optimization can be conducted on the newly created structures. The main challenge is to uphold speed and a low memory footprint without sacrificing the solutions' accuracy during optimization. We explain how to dynamically adjust the optimizer's geometric structure via joining/splitting of Octree nodes and how to define the operators. We evaluate on various datasets and outline the suitability in terms of performance and geometric accuracy.Comment: BMVC 201

Integration of Argo trajectories in the Mediterranean Forecasting System and impact on the regional analysis of the western Mediterranean circulation

The impact of Argo float trajectory assimilation on the quality of ocean analyses is studied by means of an operational oceanographic model implemented in the Mediterranean Sea and a 3D-Var assimilation scheme. For the first time, both Argo trajectories and vertical profiles of temperature and salinity (TS) together with satellite altimeter data of sea level anomaly (SLA) are assimilated to produce analyses for short-term forecasts. The study period covers 3 months during winter 2005 when four Argo trajectories were present in the northwestern Mediterranean Sea. The scheme is first assessed computing the misfits between observations and model forecast and analysis. The misfit statistics appear improved for float trajectories, while they are not degraded for the other assimilated variables (TS profiles and SLA). This indicates that the trajectory integration is consistent with the other components of the assimilation system and provides new information on horizontal pressure gradients. Comparisons between analyses obtained with and without trajectory assimilation suggest that trajectory assimilation can have an impact on the description of boundary currents and their instabilities, as well as mesoscale activity at regional scales. Changes are depicted by intermediate water mass redistributions, mesoscale eddy relocations, and net transport modulations. These impacts are detailed and assessed considering historical and simultaneous in situ data sets. The results motivate the integration of Argo trajectories in the operational Mediterranean Forecasting System

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states

FULL 3D RECONSTRUCTION OF DYNAMIC NON-RIGID SCENES: ACQUISITION AND ENHANCEMENT

Recent advances in commodity depth or 3D sensing technologies have enabled us to move closer to the goal of accurately sensing and modeling the 3D representations of complex dynamic scenes. Indeed, in domains such as virtual reality, security, surveillance and e-health, there is now a greater demand for aff ordable and flexible vision systems which are capable of acquiring high quality 3D reconstructions. Available commodity RGB-D cameras, though easily accessible, have limited fi eld-of-view, and acquire noisy and low-resolution measurements which restricts their direct usage in building such vision systems. This thesis targets these limitations and builds approaches around commodity 3D sensing technologies to acquire noise-free and feature preserving full 3D reconstructions of dynamic scenes containing, static or moving, rigid or non-rigid objects. A mono-view system based on a single RGB-D camera is incapable of acquiring full 360 degrees 3D reconstruction of a dynamic scene instantaneously. For this purpose, a multi-view system composed of several RGB-D cameras covering the whole scene is used. In the first part of this thesis, the domain of correctly aligning the information acquired from RGB-D cameras in a multi-view system to provide full and textured 3D reconstructions of dynamic scenes, instantaneously, is explored. This is achieved by solving the extrinsic calibration problem. This thesis proposes an extrinsic calibration framework which uses the 2D photometric and 3D geometric information, acquired with RGB-D cameras, according to their relative (in)accuracies, a ffected by the presence of noise, in a single weighted bi-objective optimization. An iterative scheme is also proposed, which estimates the parameters of noise model aff ecting both 2D and 3D measurements, and solves the extrinsic calibration problem simultaneously. Results show improvement in calibration accuracy as compared to state-of-art methods. In the second part of this thesis, the domain of enhancement of noisy and low-resolution 3D data acquired with commodity RGB-D cameras in both mono-view and multi-view systems is explored. This thesis extends the state-of-art in mono-view template-free recursive 3D data enhancement which targets dynamic scenes containing rigid-objects, and thus requires tracking only the global motions of those objects for view-dependent surface representation and fi ltering. This thesis proposes to target dynamic scenes containing non-rigid objects which introduces the complex requirements of tracking relatively large local motions and maintaining data organization for view-dependent surface representation. The proposed method is shown to be e ffective in handling non-rigid objects of changing topologies. Building upon the previous work, this thesis overcomes the requirement of data organization by proposing an approach based on view-independent surface representation. View-independence decreases the complexity of the proposed algorithm and allows it the flexibility to process and enhance noisy data, acquired with multiple cameras in a multi-view system, simultaneously. Moreover, qualitative and quantitative experimental analysis shows this method to be more accurate in removing noise to produce enhanced 3D reconstructions of non-rigid objects. Although, extending this method to a multi-view system would allow for obtaining instantaneous enhanced full 360 degrees 3D reconstructions of non-rigid objects, it still lacks the ability to explicitly handle low-resolution data. Therefore, this thesis proposes a novel recursive dynamic multi-frame 3D super-resolution algorithm together with a novel 3D bilateral total variation regularization to filter out the noise, recover details and enhance the resolution of data acquired from commodity cameras in a multi-view system. Results show that this method is able to build accurate, smooth and feature preserving full 360 degrees 3D reconstructions of the dynamic scenes containing non-rigid objects

Three-Dimensional Fractal Analysis of Idiopathic Pulmonary Fibrosis

The characterization of lung tissue architecture in Idiopathic Pulmonary Fibrosis (IPF) can provide useful insights into disease presentation and progression. In this study, we propose a novel three-dimensional (3D) fractal analysis to quantify the behavior of lung tissue in both healthy and bleomycin (BLM)-induced fibrotic mouse models. The fractal dimension (FD), which is a statistical index of complexity, was calculated for each voxel in reconstructed micro-CT images of the lung samples. These values were plotted on a kernel density estimation (KDE) plot, generating a distribution of FDs for each sample. Results indicate a slight but not statistically significant difference in average FD between the control and BLM samples. Tissue densities between the two groups were also compared in Hounsfield units (HU), a radiodensity scale, revealing elevated collagen concentrations and peripheral fibrosis in the BLM groups, consistent with IPF. Due to our small sample size of only 9 mouse lungs, further conclusions about the structural differences between healthy and fibrotic lungs are impaired. However, the results suggest disparities in the organization and/or collagen density between groups. Therefore, further assessments encompassing density features into the FD may prove to be an effective mode for differentiating and/or describing healthy and IPF lungs

Integration of ARGO trajectories in the Mediterranean Forecasting System and impact on the regional analysis of the Western Mediterranean circulation

The impact of ARGO trajectory assimilation on the quality of ocean analyses is studied by means of an operational oceanographic model implemented in the Mediterranean Sea and a 3D-var assimilation scheme. For the first time, both ARGO trajectories and vertical profiles together with satellite data are assimilated to produce analyses for short term forecasts. The study period covers three months during winter 2005 when four ARGO trajectories were present in the northwestern Mediterranean Sea. It is shown that their integration is consistent with the other components of the assimilation system, and it contributes to refine the model error structure with new information on horizontal pressure gradients. So the analysis benefits of a more accurate description of the boundary currents and their instabilities that drive the mesoscale activity of regional circulations. As a consequence, the trajectory assimilation remotely and significantly influences the basin scale circulation. Changes can be depicted by intermediate water mass redistributions, mesoscale eddy relocations or net transports modulations. These impacts are detailed and assessed considering historical and contemporary datasets. The obtained qualitative and quantitative agreements motivate the integration of ARGO trajectories in the operational Mediterranean Forecasting System

Three-dimensional quantification and visualization of vascular networks in engineered tissues

Three-dimensional textural and volumetric image analysis holds great potential in understanding the image data produced by multi-photon microscopy. In this thesis, a tool that provides quantitative textural and morphometric analyzes of vasculature in engineered tissues, alongside with a fast three-dimensional volume rendering is proposed. The investigated 3D artificial tissues consist of Human Umbilical Vein Endothelial Cells (HUVEC) embedded in collagen exposed to two regimes of ultrasound standing wave fields under different pressure conditions. Textural features were evaluated over the extracted connected region in our samples using the normalized Gray Level Co-occurrence Matrix (GLCM) combined with Gray-Level Run Length Matrix (GLRLM) analysis. To minimize the error resulting from any possible volumetric rotation and to provide a comprehensive textural analysis, an averaged version of nine GLCM and GLRLM orientations is used. To evaluate volumetric features, parameters such as volume run length and percentage volume were utilized. The z-projection versions of the samples were used to estimate the tortuosity of the vessels, as well as, to measure the length and the angle of the branches. We utilized a three-dimensional volume rendering technique named MATVTK (derived from MATLAB and VTK) and runs under MATLAB that shows a great improvement on the processing time to reconstruct our volumes compared to MATLAB built-in functions. Results show that our analysis is able to differentiate among the exposed samples, due to morphological changes induced by the ultrasound standing wave fields. Furthermore, we demonstrate that providing more textural parameters than what is currently being reported in the literature, enhances the quantitative understanding of the heterogeneity of artificial tissues