Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

The isolated neutron star candidate 2XMM J104608.7-594306

Over the last decade, X-ray observations have revealed the existence of several classes of isolated neutron stars (INSs) which are radio-quiet or exhibit radio emission with properties much at variance with those of ordinary radio pulsars. The identification of new sources is crucial in order to understand the relations among the different classes and to compare observational constraints with theoretical expectations. A recent analysis of the 2XMMp catalogue provided fewer than 30 new thermally emitting INS candidates. Among these, the source 2XMM J104608.7-594306 appears particularly interesting because of the softness of its X-ray spectrum, kT = 117 +/- 14 eV and N(H) = (3.5 +/- 1.1) x 10(21) cm(-2) (3 sigma), and of the present upper limits in the optical, m(B) greater than or similar to 26, m(V) greater than or similar to 25.5 and m(R) greater than or similar to 25 (98.76% confidence level), which imply a logarithmic X-ray-to-optical flux ratio log(F(X)/F(V)) greater than or similar to 3.1, corrected for absorption. We present the X-ray and optical properties of 2XMM J104608.7-594306 and discuss its nature in the light of two possible scenarios invoked to explain the X-ray thermal emission from INSs: the release of residual heat in a cooling neutron star, as in the seven radio-quiet ROSAT-discovered INSs, and accretion from the interstellar medium. We find that the present observational picture of 2XMM J104608.7-594306 is consistent with a distant cooling INS with properties in agreement with the most up-to-date expectations of population synthesis models: it is fainter, hotter and more absorbed than the seven ROSAT sources and possibly located in the Carina Nebula, a region likely to harbour unidentified cooling neutron stars. The accretion scenario, although not entirely ruled out by observations, would require a very slow (similar to 10 km s(-1)) INS accreting at the Bondi-Hoyle rate.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

AUTOMATIC CORONARY WALL SEGMENTATION IN INTRAVASCULAR ULTRASOUND IMAGES USING BINARY MORPHOLOGICAL RECONSTRUCTION

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging. (E-mail: matheuscardosomg@hotmail.com) (C) 2011 World Federation for Ultrasound in Medicine & Biology.

Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the Most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions. (C) 2008 Elsevier B.V. All rights reserved.

Cloud point extraction to avoid interferences by structured background on nickel determination in plant materials by FAAS

An analytical procedure based on microwave-assisted digestion with diluted acid and a double cloud point extraction is proposed for nickel determination in plant materials by flame atomic absorption spectrometry. Extraction in micellar medium was successfully applied for sample clean up, aiming to remove organic species containing phosphorous that caused spectral interferences by structured background attributed to the formation of PO species in the flame. Cloud point extraction of nickel complexes formed with 1,2-thiazolylazo-2-naphthol was explored for pre-concentration, with enrichment factor estimated as 30, detection limit of 5 mu g L(-1) (99.7% confidence level) and linear response up to 80 mu g L(-1). The accuracy of the procedure was evaluated by nickel determinations in reference materials and the results agreed with the certified values at the 95% confidence level.

Diagnosis and treatment of extraction sockets in preparation for implant placement: report of three cases

This article addresses diagnostic parameters that should be assessed in the treatment of extraction sockets with dental implant placement by presenting three case reports that emphasize the relevance of the amount of remaining bone walls. Diagnosis was based on the analysis of clinical and radiographic parameters (e.g.: bone defect morphology, remaining bone volume, presence of infections on the receptor site). Case 1 presents a 5-wall defect in the maxillary right central incisor region with severe root resorption, which was treated with immediate implant placement. Cases 2 and 3 present, respectively, two- and three-wall bone defects that did not have indication for immediate implants. These cases were first submitted to a guided bone regeneration (GBR) procedure with bone graft biomaterial and membrane barriers, and the implants were installed in a second surgical procedure. The analysis of the preoperative periodontal condition of the adjacent teeth and bone defect morphology is extremely important because these factors determine the choice between immediate implant or GBR treatment followed by implant installation in a subsequent intervention.

Evaluation of DNA extraction protocols for Brucella abortus pcr detection in aborted fetuses or calves born from cows experimentally infected with strain 2308

The objective of the present study was to improve the detection of B. abortus by PCR in organs of aborted fetuses from infected cows, an important mechanism to find infected herds on the eradication phase of the program. So, different DNA extraction protocols were compared, focusing the PCR detection of B. abortus in clinical samples collected from aborted fetuses or calves born from cows challenged with the 2308 B. abortus strain. Therefore, two gold standard groups were built based on classical bacteriology, formed from: 32 lungs (17 positives), 26 spleens (11 positives), 23 livers (8 positives) and 22 bronchial lymph nodes (7 positives). All samples were submitted to three DNA extraction protocols, followed by the same amplification process with the primers B4 and B5. From the accumulated results for organ, the proportion of positives for the lungs was higher than the livers (p=0.04) or bronchial lymph nodes (p=0.004) and equal to the spleens (p=0.18). From the accumulated results for DNA extraction protocol, the proportion of positives for the Boom protocol was bigger than the PK (p<0.0001) and GT (p=0.0004). There was no difference between the PK and GT protocols (p=0.5). Some positive samples from the classical bacteriology were negative to the PCR and viceversa. Therefore, the best strategy for B. abortus detection in the organs of aborted fetuses or calves born from infected cows is the use, in parallel, of isolation by classical bacteriology and the PCR, with the DNA extraction performed by the Boom protocol.

VUV spectral line emission measurements in the TCABR tokamak

The study of tokamak plasma light emissions in the vacuum ultraviolet (VUV) region is an important subject since many impurity spectral emissions are present in this region. These spectral emissions can be used to determine the plasma ion temperature and density from different species and spatial positions inside plasma according to their temperatures. We have analyzed VUV spectra from 500 Å to 3200 Å wavelength in the TCABR tokamak plasma including higher diffraction order emissions. There have been identified 37 first diffraction order emissions, resulting in 28 second diffraction order, 24 third diffraction order, and 7 fourth diffraction order lines. The emissions are from impurity species such as OII, OIII, OIV, OV, OVI, OVII, CII, CIII, CIV, NIII, NIV, and NV. All the spectra beyond 1900 Å are from higher diffraction order emissions, and possess much better spectral resolution. Each strong and isolated spectral line, as well as its higher diffraction order emissions suitable for plasma diagnostic is identified and discussed. Finally, an example of ion temperature determination using different diffraction order is presented.

ScriptLattes: an open-source knowledge extraction system from the Lattes platform

The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.

Vegetable-origin foam employed in dye extraction in tanning and leather processing facilities

This study addressed the use of conventional and vegetable origin polyurethane foams to extract C. I. Acid Orange 61 dye. The quantitative determination of the residual dye was carried out with an UV/Vis absorption spectrophotometer. The extraction of the dye was found to depend on various factors such as pH of the solution, foam cell structure, contact time and dye and foam interactions. After 45 days, better results were obtained for conventional foam when compared to vegetable foam. Despite presenting a lower percentage of extraction, vegetable foam is advantageous as it is considered a polymer with biodegradable characteristics.