In-plane displacement and strain image analysis

Measurements in civil engineering load tests usually require considerable time and complex procedures. Therefore, measurements are usually constrained by the number of sensors resulting in a restricted monitored area. Image processing analysis is an alternative way that enables the measurement of the complete area of interest with a simple and effective setup. In this article photo sequences taken during load displacement tests were captured by a digital camera and processed with image correlation algorithms. Three different image processing algorithms were used with real images taken from tests using specimens of PVC and Plexiglas. The data obtained from the image processing algorithms were also compared with the data from physical sensors. A complete displacement and strain map were obtained. Results show that the accuracy of the measurements obtained by photogrammetry is equivalent to that from the physical sensors but with much less equipment and fewer setup requirements. © 2015Computer-Aided Civil and Infrastructure Engineering.

A toolbox for hyperspectral image analysis

This paper introduces a new toolbox for hyperspectral imagery, developed under the MATLAB environment. This toolbox provides easy access to different supervised and unsupervised classification methods. This new application is also versatile and fully dynamic since the user can embody their own methods, that can be reused and shared. This toolbox, while extends the potentiality of MATLAB environment, it also provides a user-friendly platform to assess the results of different methodologies. In this paper it is also presented, under the new application, a study of several different supervised and unsupervised classification methods on real hyperspectral data.

Methods for automatic and assisted image annotation

Personal memories composed of digital pictures are very popular at the moment. To retrieve these media items annotation is required. During the last years, several approaches have been proposed in order to overcome the image annotation problem. This paper presents our proposals to address this problem. Automatic and semi-automatic learning methods for semantic concepts are presented. The automatic method is based on semantic concepts estimated using visual content, context metadata and audio information. The semi-automatic method is based on results provided by a computer game. The paper describes our proposals and presents their evaluations.

Dependent component analysis: a hyperspectral unmixing algorithm

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.

Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data?

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

O património local como recurso para o desenvolvimento de competências essenciais da história no 1.º ciclo

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

Shear-induced lamellar ionic liquid-crystal foam

n a recent paper we reported an experimental study of two N-alkylimidazolium salts. These ionic compounds exhibit liquid crystalline behaviour with melting points above 50 degrees C in bulk. However, if they are sheared, a (possibly non-equilibrium) lamellar phase forms at room temperature. Upon shearing a thin film of the material between microscope slides, textures were observed that are strikingly similar to liquid (wet) foams. The images obtained from polarising optical microscopy (POM) were found to share many of the known quantitative properties of a two-dimensional foam coarsening process. Here we report an experimental study of this foam using a shearing system coupled with POM. The structure and evolution of the foam are investigated through the image analysis of time sequences of micrographs obtained for well-controlled sets of physical parameters (sample thickness, shear rate and temperature). In particular, we find that there is a threshold shear rate below which no foam can form. Above this threshold, a steady-state foam pattern is obtained where the mean cell area generally decreases with increasing shear rate. Furthermore, the steady-state internal cell angles and distribution of the cell number of sides deviate from their equilibrium (i.e. zero-shear) values.

Cyclic deformation of bidisperse two-dimensional foams

In-plane deformation of foams was studied experimentally by subjecting bidisperse foams to cycles of traction and compression at a prescribed rate. Each foam contained bubbles of two sizes with given area ratio and one of three initial arrangements: sorted perpendicular to the axis of deformation (iso-strain), sorted parallel to the axis of deformation (iso-stress), or randomly mixed. Image analysis was used to measure the characteristics of the foams, including the number of edges separating small from large bubbles N-sl, the perimeter (surface energy), the distribution of the number of sides of the bubbles, and the topological disorder mu(2)(N). Foams that were initially mixed were found to remain mixed after the deformation. The response of sorted foams, however, depended on the initial geometry, including the area fraction of small bubbles and the total number of bubbles. For a given experiment we found that (i) the perimeter of a sorted foam varied little; (ii) each foam tended towards a mixed state, measured through the saturation of N-sl; and (iii) the topological disorder mu(2)(N) increased up to an "equilibrium" value. The results of different experiments showed that (i) the change in disorder, Delta mu(2)(N), decreased with the area fraction of small bubbles under iso-strain, but was independent of it under iso-stress; and (ii) Delta mu(2)(N) increased with Delta N-sl under iso-strain, but was again independent of it under iso-stress. We offer explanations for these effects in terms of elementary topological processes induced by the deformations that occur at the bubble scale.

Integrated vehicle classification system

This paper presents an integrated system for vehicle classification. This system aims to classify vehicles using different approaches: 1) based on the height of the first axle and_the number of axles; 2) based on volumetric measurements and; 3) based on features extracted from the captured image of the vehicle. The system uses a laser sensor for measurements and a set of image analysis algorithms to compute some visual features. By combining different classification methods, it is shown that the system improves its accuracy and robustness, enabling its usage in more difficult environments satisfying the proposed requirements established by the Portuguese motorway contractor BRISA.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

STIR, SPIR and SPAIR techniques in magnetic resonance of the breast: a comparative study

The amount of fat is a component that complicates the clinical evaluation and the differential diagnostic between benign and malign lesions in the breast MRI examinations. To overcome this problem, an effective erasing of the fat signal over the images acquisition process, is essentials. This study aims to compare three fat suppression techniques (STIR, SPIR, SPAIR) in the MR images of the breast and to evaluate the best image quality regarding its clinical usefulness. To mimic breast women, a breast phantom was constructed. First the exterior contour and, in second time, its content which was selected based on 7 samples with different components. Finally it was undergone to a MRI breast protocol with the three different fat saturation techniques. The examinations were performed on a 1.5 T MRI system (Philips®). A group of 5 experts evaluated 9 sequences, 3 of each with fat suppression techniques, in which the frequency offset and TI (Inversion Time) were the variables changed. This qualitative image analysis was performed according 4 parameters (saturation uniformity, saturation efficacy, detail of the anatomical structures and differentiation between the fibroglandular and adipose tissue), using a five-point Likert scale. The statistics analysis showed that anyone of the fat suppression techniques demonstrated significant differences compared to the others with (p > 0.05) and regarding each parameter independently. By Fleiss’ kappa coefficient there was a good agreement among observers P(e) = 0.68. When comparing STIR, SPIR and SPAIR techniques it was confirmed that all of them have advantages in the study of the breast MRI. For the studied parameters, the results through the Friedman Test showed that there are similar advantages applying anyone of these techniques.

Signal Subspace Identification in Hyperspectral Linear Mixtures

Chpater in Book Proceedings with Peer Review Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II