Design and evaluation of an advanced virtual reality system for visualization of dentistry structures

This paper presents the development of an application created to assist the teaching of dental structures, generate rich content information and different manners of interaction. An ontology was created to provide semantics informations for virtual models. We also used two devices gesture-based interaction: Kinect and Wii Remote. It was developed a system which use intuitive interaction, and it is able to generate three dimensional images, making the experience of teaching / learning motivating. The projection environment used by the system was called Mini CAVE. © 2012 IEEE.

Components of the acoustic swallowing signal: Preliminary study

Purpose: To analyze the components of the acoustic signal of swallowing using a specific software. Methods: Fourteen healthy subjects ranging in age from 20 to 50 years (mean age 31±10 years), were evaluated. Data collection consisted on the simultaneous capture of the swallowing audio with a microphone and of the swallowing videofluoroscopic image. The bursts of the swallowing acoustic signal were identified and their duration and the interval between them were later analyzed using a specific software, which allowed the simultaneous analyses between the acoustic wave and the videofluoroscopic image. Results: Three burst components were identified in most of the swallows evaluated. The first burst presented mean time of 87.3 milliseconds (ms) for water and 78.2 for the substance. The second burst presented mean time of 112.9 ms for water and 85.5 for the pasty substance. The mean interval between first and second burst was 82.1 ms for water and 95.3 ms for the pasty consistency, and between second and third burst was 339.8 ms for water and 322.0 ms for the pasty consistency. Conclusion: The software allowed the visualization of three bursts during the swallowing of healthy individuals, and showed that the swallowing signal in normal subjects is highly variable.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Plataforma para medição de forças na região plantar de pacientes normais e hemiplégicos

This work describes an electronic system implementation with two force plates, 24 load cells with strain gages, signal conditioning circuit, interfacing circuit and data acquisition system, designed for measuring plantar force distribution in patients. The system presented linear response, low hysteresis, with determination coefficient of 0.9997, precision better than 0,84% and resolution less than 0.5 N. The Measurements are presented on a computer screen and easier visualization to specialists, mainly physicians, physiotherapists and occupational therapists. Using the system, the distribution of weight in the plantar region of 100 normal subjects and 10 hemiplegic patients was investigated. There were determined the relationship between weight distribution on the right and left forefoot, hemiplegic patients and normal patients, with the implemented system. © 2013 Springer.

A computer vision approach to quantify leaf anatomical plasticity: A case study on gochnatia polymorpha (less.) cabrera

Inferences about leaf anatomical characteristics had largely been made by manually measuring diverse leaf regions, such as cuticle, epidermis and parenchyma to evaluate differences caused by environmental variables. Here we tested an approach for data acquisition and analysis in ecological quantitative leaf anatomy studies based on computer vision and pattern recognition methods. A case study was conducted on Gochnatia polymorpha (Less.) Cabrera (Asteraceae), a Neotropical savanna tree species that has high phenotypic plasticity. We obtained digital images of cross-sections of its leaves developed under different light conditions (sun vs. shade), different seasons (dry vs. wet) and in different soil types (oxysoil vs. hydromorphic soil), and analyzed several visual attributes, such as color, texture and tissues thickness in a perpendicular plane from microscopic images. The experimental results demonstrated that computational analysis is capable of distinguishing anatomical alterations in microscope images obtained from individuals growing in different environmental conditions. The methods presented here offer an alternative way to determine leaf anatomical differences. © 2013 Elsevier B.V.