Stereo vision and wavelets/multiwavelets analysis : 3d reconstruction using wavelets/multiwavelets theory and stereo vision

During the image formation process of the camera, explicit 3D information about the scene or objects in the scene are lost. Therefore, 3D structure or depth information has to be inferred implicitly from the 2D intensity images. This problem is com- monly referred to as 3D reconstruction. In this work a complete 3D reconstruction algorithm is presented, capable of reconstructing dimensionally accurate 3D models of the objects, based on stereo vision and multi-resolution analysis. The developed system uses a reference depth model of the objects under observation to improve the disparity maps, estimated. Only a few features are extracted from that reference model, which are the relative location of the discontinuities and the z-dimensional extremes of objects depth. The maximum error deviation of the estimated depth along the surfaces is less than 0.5mm and along the discontinuities is less than 1.5mm. The developed system is invariant to illuminative variations, and orientation, location and scaling of the objects under consideration, which makes the developed system highly robust.

Finite element analysis of an axi-symmetric forward spiral extrusion of Mg-1.75Mn alloy

A potential severe plastic deformation process known as axi-symmetrical forward spiral extrusion (AFSE) has been studied numerically and experimentally. The process is based on the extrusion of cylindrical samples through a die with engraved spiral grooves in a near zero shape change manner. The process was simulated using a three dimensional finite element (FE) model that has been developed using commercial software, ABAQUS. In order to verify the finite element results, hot rolled and annealed samples of the alloy were experimentally processed by AFSE. The required extrusion forces during the process were estimated using the FE model and compared with the experimental values. The reasonable agreement between the FE results and experimental data verified the accuracy of the FE model. The numerical results indicate the linear strain distribution in the AFSE sample is only valid for a core concentric while the strain distribution in the vicinity of the grooves is non axi-symmetric. The FE simulation results from this research allows a better understanding of AFSE kinematics especially near the grooves, the required extrusion force and the resultant induced strain distribution in the sample. To compare the mechanical properties of the Mg-1.75Mn alloy before and after the process, a micro shear punch test was used. The tests were performed on samples undergoing one and four passes of AFSE. After four passes of AFSE, it was observed that the average shear strength of the alloy has improved by about 21%. The developedfinite element model enables tool design and material flow simulation during the process.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Tracking-as-recognition for articulated full-body human motion analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

Random Subspace Two-Dimensional PCA for face recognition

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets -the ORL database, the Yale face database and the extended Yale face database B - confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.

Thermal analysis of nanofluids in microfluidics using an infrared camera

We present the thermal analysis of liquid containing Al2O3 nanoparticles in a microfluidic platform using an infrared camera. The small dimensions of the microchannel along with the low flow rates (less than 120 μl min−1) provide very low Reynolds numbers of less than 17.5, reflecting practical parameters for a microfluidic cooling platform. The heat analysis of nanofluids has never been investigated in such a regime, due to the deficiencies of conventional thermal measurement systems. The infrared camera allows non-contact, three dimensional and high resolution capability for temperature profiling. The system was studied at different w/w concentrations of thermally conductive Al2O3 nanoparticles and the experiments were in excellent agreement with the computational fluid dynamics (CFD) simulations.

Peak picking and the assessment of separation performance in two-dimensional high performance liquid chromatography

An algorithm was developed for 2DHPLC that automated the process of peak recognition, measuring their retention times, and then subsequently plotting the information in a two-dimensional retention plane. Following the recognition of peaks, the software then performed a series of statistical assessments of the separation performance, measuring for example, correlation between dimensions, peak capacity and the percentage of usage of the separation space. Peak recognition was achieved by interpreting the first and second derivatives of each respective one-dimensional chromatogram to determine the 1D retention times of each solute and then compiling these retention times for each respective fraction ‘cut’. Due to the nature of comprehensive 2DHPLC adjacent cut fractions may contain peaks common to more than one cut fraction. The algorithm determined which components were common in adjacent cuts and subsequently calculated the peak maximum profile by interpolating the space between adjacent peaks. This algorithm was applied to the analysis of a two-dimensional separation of an apple flesh extract separated in a first dimension comprising a cyano stationary phase and an aqueous/THF mobile phase as the first dimension and a second dimension comprising C18-Hydro with an aqueous/MeOH mobile phase. A total of 187 peaks were detected.

Electric field analysis of spinneret design for needleless electrospinning of nanofibers

Concentrated electric field is crucial in generation of needleless electrospinning; the electric field profile together with electric field intensity of the spinneret directly affect the needleless electrospinning performance. Understanding the electric field of different spinnerets would definitely benefit the design and optimization of needleless electrospinning. Three-dimensional (3D) finite element analysis has been used to analyze the electric field profile and electric field intensity of different spinnerets for needleless electrospinning by using the simulation software COMSOL Multiphysics 3.5a. It has been found that evolution of the spinneret of needleless electrospinning from cylinder to multiple disks and then to multiple rings results in stronger and more concentrated electric field. The analysis based on 3D simulation of the electric field could benefit further development of needleless electrospinning in which the production rate and quality of as-spun nanofibers are of great importance.

A theoretical analysis for fiber contacts in multilayer nanofibrous assemblies

A theoretical analysis is presented for the estimation of the number of contacts between fibers in random multilayer nanofibrous assemblies with arbitrary fiber diameter and orientation. The statistics of fiber contacts for single-layer nanofiber mats were considered first, and the equations were developed for three-dimensional multilayer nanofibrous assemblies by considering the superposition of the single-layer assemblies. Based on the theoretical approach presented here for multilayer nanofibrous networks, the network porosity, mean fiber diameter and a function of fiber aspect ratio contribute to a model to determine the average number of fiber contacts per unit fiber length in multilayer nanofibrous mats. The theory is studied parametrically and results compared with the work of a model presented by Samson. It is shown that the presented model compared to the existing models is more sensitive with the fiber diameter in the nano-scale. It is also believed that the presented theory for fiber-to-fiber contacts is more realistic and useful for further studies of multilayer nanofibrous assemblies.

Interobserver reliability of classification and characterization of proximal humeral fractures: a comparison of two and three-dimensional CT

Interobserver reliability for the classification of proximal humeral fractures is limited. The aim of this study was to test the null hypothesis that interobserver reliability of the AO classification of proximal humeral fractures, the preferred treatment, and fracture characteristics is the same for two-dimensional (2-D) and three-dimensional (3-D) computed tomography (CT). Members of the Science of Variation Group--fully trained practicing orthopaedic and trauma surgeons from around the world--were randomized to evaluate radiographs and either 2-D CT or 3-D CT images of fifteen proximal humeral fractures via a web-based survey and respond to the following four questions: (1) Is the greater tuberosity displaced? (2) Is the humeral head split? (3) Is the arterial supply compromised? (4) Is the glenohumeral joint dislocated? They also classified the fracture according to the AO system and indicated their preferred treatment of the fracture (operative or nonoperative). Agreement among observers was assessed with use of the multirater kappa (κ) measure. Interobserver reliability of the AO classification, fracture characteristics, and preferred treatment generally ranged from "slight" to "fair." A few small but statistically significant differences were found. Observers randomized to the 2-D CT group had slightly but significantly better agreement on displacement of the greater tuberosity (κ = 0.35 compared with 0.30, p < 0.001) and on the AO classification (κ = 0.18 compared with 0.17, p = 0.018). A subgroup analysis of the AO classification results revealed that shoulder and elbow surgeons, orthopaedic trauma surgeons, and surgeons in the United States had slightly greater reliability on 2-D CT, whereas surgeons in practice for ten years or less and surgeons from other subspecialties had slightly greater reliability on 3-D CT. Proximal humeral fracture classifications may be helpful conceptually, but they have poor interobserver reliability even when 3-D rather than 2-D CT is utilized. This may contribute to the similarly poor interobserver reliability that was observed for selection of the treatment for proximal humeral fractures. The lack of a reliable classification confounds efforts to compare the outcomes of treatment methods among different clinical trials and reports.

Improving peak shapes with counter gradients in two-dimensional high performance liquid chromatography

To achieve the greatest peak capacity in two-dimensional high performance liquid chromatography (2D-HPLC) a gradient should be operated in both separation dimensions. However, it is known that when an injection solvent that is stronger than the initial mobile phase composition is deleterious to peak performance, thus causing problems when cutting a portion from one gradient into another. This was overcome when coupling hydrophilic interaction with reversed phase chromatography by introducing a counter gradient that changed the solvent strength of the second dimension injection. It was found that an injection solvent composition of 20% acetonitrile in water gave acceptable results in one-dimensional simulations with an initial composition of 5% acetonitrile. When this was transferred to a 2D-HPLC separation of standards it was found that a marked improvement in peak shape was gained for the moderately retained analytes (phenol and dimethyl phthalate), some improvement for the weakly retained caffeine and very little change for the strongly retained n-propylbenzene and anthracene which already displayed good chromatographic profiles. This effect was transferred when applied to a 2D-HPLC separation of a coffee extract where the indecipherable retention profile was transformed to a successful application multidimensional chromatography with peaks occupying 71% of the separation space according to the geometric approach to factor analysis.

Determination of neurotransmitters and their metabolites using one- and two-dimensional liquid chromatography with acidic potassium permanganate chemiluminescence detection

High-performance liquid chromatography with chemiluminescence detection based on the reaction with acidic potassium permanganate and formaldehyde was explored for the determination of neurotransmitters and their metabolites. The neurotransmitters norepinephrine and dopamine were quantified in the left and right hemispheres of rat hippocampus, nucleus accumbens and prefrontal cortex, and the metabolites vanillylmandelic acid, 3,4-dihydrophenylacetic acid, 5-hydroxyindole-3-acetic acid and homovanillic acid were identified in human urine. Under optimised chemiluminescence reagent conditions, the limits of detection for these analytes ranged from 2.5 × 10−8 to 2.5 × 10−7 M. For the determination of neurotransmitter metabolites in urine, a two-dimensional high-performance liquid chromatography (2D-HPLC) separation operated in heart-cutting mode was developed to overcome the peak capacity limitations of the one-dimensional separation. This approach provided the greater separation power of 2D-HPLC with analysis times comparable to conventional one-dimensional separations.

Fracture modelling of DP780 sheets using a hybrid experimental-numerical method and two-dimensional digital image correlation

This paper is concerned with the construction of fracture envelopes of DP780 sheets using two methods: a hybrid experimental-numerical method; two-dimensional digital image correlation (2D-DIC). For the hybrid method, four types of ductile fracture tests were carried out covering a wide range of stress states on specimens: with a central hole; two symmetric circular notches; flat grooved; and diagonally double-notched. Based on the fracture strain and loading paths identified with finite element simulation, a fracture envelope was obtained by employing the three-parameter modified Mohr-Coulomb fracture model. In addition, the fracture surface strain was directly measured using 2D-DIC. Loading histories of each test were extracted from a surface element of a three dimensional finite element model. The comparison of fracture envelopes constructed by the two methods reveals that there is little difference. Thus, it can be concluded that 2D-DIC is applicable to fracture modelling of DP780 sheets despite the assumption of the plane stress condition even after necking

Off-line two-dimensional liquid chromatography for metabolomics: an example using Agaricus bisporus mushrooms exposed to UV irradiation

It has previously been shown that irradiation with UV light increases the vitamin D content of certain mushroom species, but the effect on other nutrients is unknown, and is difficult to assess due to the complexity of the sample matrix. Here, an offline reversed phase × reversed phase two-dimensional liquid chromatography methodology was developed and applied to Agaricus bisporus mushrooms in order to demonstrate the potential of the technique and assess the effect of UV irradiation on the mushroom’s metabolic profile. The method allowed the detection of 158 peaks in a single analytical run. A total of 51 compounds including sugars, amino acids, organic and fatty acids and phenolic compounds were identified using certified reference standards. After irradiation of the mushrooms with UV for 30 s the number of peaks detected decreased from 158 to 150; 47 compounds increased in concentration while 72 substances decreased. This is the first time that two-dimensional liquid chromatography has been carried out for the metabolomic analysis of mushrooms. The data provide an overview of the gain/loss of nutritional value of the mushrooms following UV irradiation and demonstrate that the increased peak capacity and separation space of two-dimensional liquid chromatography has great potential in metabolomics.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.