An enhanced content selection mechanism for personalization of video news programmes

In this paper, we propose a content selection framework that improves the users` experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media`s constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique`s limitations are minimized by other techniques` strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

Propriedades mecânicas de peças com dimensões estruturais de Pinus spp: correlação entre resistência à tração e classificação visual

A redução da disponibilidade de espécies de madeiras nativas e seus efeitos na economia, associada ao fortalecimento dos conceitos de preservação ambiental, criou a necessidade de desenvolvimento de alternativas viáveis para utilização racional de espécies de reflorestamento. E uma das opções é a realização de classificação visual das peças. Autores de trabalhos desenvolvidos nessa linha de pesquisa verificaram a adequação das regras de classificação visual do Southern Pine Inspection Bureau (SPIB) dos EUA à madeira de Pinus do Brasil e apresentaram proposta para normalizar o processo de classificação visual dessa madeira. Nessa classificação, os aspectos com maior influência são: presença de nós, desvio de grã em relação ao eixo da peça e densidade de anéis de crescimento. Assim, esta pesquisa apresenta um estudo experimental que consistiu na classificação visual e determinação da resistência à tração de 85 peças de Pinus spp e um estudo teórico, que propôs uma equação para determinar a resistência à tração média de peças estruturais em função da classificação visual. Com este trabalho, foi possível observar a influência dos nós e dos anéis de crescimento sobre a resistência à tração das peças analisadas.

Multivariate analysis of the effects of soil parameters and environmental factors on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae

The aim of the present study was to evaluate the effect of soil characteristics (pH, macro- and micro-nutrients), environmental factors (temperature, humidity, period of the year and time of day of collection) and meteorological conditions (rain, sun, cloud and cloud/rain) on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae. The total flavonoid contents of leaf samples harvested from plants cultivated or collected under different conditions were quantified by high-performance liquid chromatography with ultraviolet detection (HPLC-UV/PAD). Chemometric treatment of the data by principal component (PCA) and hierarchic cluster analyses (HCA) showed that the samples did not present a specific classification in relation to the environmental and soil variables studied, and that the environmental variables were not significant in describing the data set. However, the levels of the elements Fe, B and Cu present in the soil showed an inverse correlation with the total flavonoid contents of the leaves of P. incarnata.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content

Introduction: Internet users are increasingly using the worldwide web to search for information relating to their health. This situation makes it necessary to create specialized tools capable of supporting users in their searches. Objective: To apply and compare strategies that were developed to investigate the use of the Portuguese version of Medical Subject Headings (MeSH) for constructing an automated classifier for Brazilian Portuguese-language web-based content within or outside of the field of healthcare, focusing on the lay public. Methods: 3658 Brazilian web pages were used to train the classifier and 606 Brazilian web pages were used to validate it. The strategies proposed were constructed using content-based vector methods for text classification, such that Naive Bayes was used for the task of classifying vector patterns with characteristics obtained through the proposed strategies. Results: A strategy named InDeCS was developed specifically to adapt MeSH for the problem that was put forward. This approach achieved better accuracy for this pattern classification task (0.94 sensitivity, specificity and area under the ROC curve). Conclusions: Because of the significant results achieved by InDeCS, this tool has been successfully applied to the Brazilian healthcare search portal known as Busca Saude. Furthermore, it could be shown that MeSH presents important results when used for the task of classifying web-based content focusing on the lay public. It was also possible to show from this study that MeSH was able to map out mutable non-deterministic characteristics of the web. (c) 2010 Elsevier Inc. All rights reserved.

Chemical composition of mixed construction and demolition recycled aggregates from the State of Sao Paulo

Construction and Demolition Waste (CDW) represents. about 50% of the total Brazilian municipal solid waste: thus, recycling represents huge benefits both in environmental and economic perspectives. Herein, the chemical characterization results of three samples from two different recycling plants from the State of Sao Paulo is prevented. The results demonstrated that the visual classification into grey and red is not related to the chemical composition but mostly to the grain size fraction. The chemical composition of the CDW varies according to the content of cement paste, natural aggregates (quartz sand or granite), red ceramic and clay. Furthermore, the production of recycled concrete aggregates requires two crushing stages to meet the technical standards. The sand fraction (below 4.8 mm) presents high grades of SiO(2), which indicates the liberation of cement paste to fines (< 0.15 mm). The fines have a great potential to be used in the cement industry.

On the classification of mixed construction and demolition waste aggregate by porosity and its impact on the mechanical performance of concrete

The properties of recycled aggregate produced from mixed (masonry and concrete) construction and demolition (C&D) waste are highly variable, and this restricts the use of such aggregate in structural concrete production. The development of classification techniques capable of reducing this variability is instrumental for quality control purposes and the production of high quality C&D aggregate. This paper investigates how the classification of C&D mixed coarse aggregate according to porosity influences the mechanical performance of concrete. Concretes using a variety of C&D aggregate porosity classes and different water/cement ratios were produced and the mechanical properties measured. For concretes produced with constant volume fractions of water, cement, natural sand and coarse aggregate from recycled mixed C&D waste, the compressive strength and Young modulus are direct exponential functions of the aggregate porosity. Sink and float technique is a simple laboratory density separation tool that facilitates the separation of cement particles with lower porosity, a difficult task when done only by visual sorting. For this experiment, separation using a 2.2 kg/dmA(3) suspension produced recycled aggregate (porosity less than 17%) which yielded good performance in concrete production. Industrial gravity separators may lead to the production of high quality recycled aggregate from mixed C&D waste for structural concrete applications.

Chemical-mineralogical characterization of C&D waste recycled aggregates from Sao Paulo, Brazil

This study presents a methodology for the characterization of construction and demolition (C&D) waste recycled aggregates based on a combination of analytical techniques (X-ray fluorescence (XRF), soluble ions, semi-quantitative X-ray diffraction (XRD), thermogravimetric analysis (TCA-DTG) and hydrochloric acid (HCl) selective dissolution). These combined analytical techniques allow for the estimation of the amount of cement paste, its most important hydrated and carbonated phases, as well as the amount of clay and micas. Details of the methodology are presented here and the results of three representative C&D samples taken from the Sao Paulo region in Brazil are discussed. Chemical compositions of mixed C&D aggregate samples have mostly been influenced by particle size rather than the visual classification of C&D into red or grey and geographical origin. The amount of measured soluble salts in C&D aggregates (0.15-25.4 mm) is lower than the usual limits for mortar and concrete production. The content of porous cement paste in the C&D aggregates is around 19.3% (w/w). However, this content is significantly lower than the 43% detected for the C&D powders (< 0.15 min). The clay content of the powders was also high, potentially resulting from soil intermixed with the C&D waste, as well as poorly burnt red ceramic. Since only about 50% of the measured CaO is combined with CO(2), the powders have potential use as raw materials for the cement industry. (C) 2008 Elsevier Ltd. All rights reserved.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Comparison of visual inspection, radiographic examination, laser fluorescence and their combinations on treatment decisions for occlusal surfaces

Purpose: To compare visual inspection (VI), radiographic examination (RX) and the laser fluorescence device DIAGNOdent (L), as well as their combinations in vitro regarding treatment decisions for occlusal surfaces. Methods: 72 extracted human permanent teeth (molars and premolars) were used. Treatment decisions were recorded by three calibrated examiners, and the options available were fissure sealant and conservative restoration. For validation of treatment decisions, the teeth were sectioned and examined in a stereomicroscope. Thereafter, dental slices were scanned and the images were edited to facilitate classification of existing carious lesions. Intra and inter-examiner reproducibility for the determination of treatment plans were calculated using Cohen`s kappa test (95%-CI). Sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve were also calculated. Results: VI and L provided on average the greatest intra- and inter-examiner reproducibility, respectively. Although the combination of diagnostic methods may decrease both intra- and inter examiners reproducibility, combination of VI, L and RX resulted in the greatest sensitivity, being statistically superior to RX and L. There was more inter-examiner agreement for the option of restorative treatment, while the use of sealants as a treatment option yielded the lowest values. Negative predictive values were numerically inferior to positive predictive values, indicating that the examiners preferred not to restore a carious tooth than to proceed operatively in an intact tooth. The combination of the three methods studied showed the best results in determining treatment plans for occlusal surfaces, when compared to the other types of exams. On the other hand, radiographic examination and laser fluorescence were less efficient when used alone.

Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

Correlação entre as escalas visual analógica, de Melbourne e filamentos de Von Frey na avaliação da dor pós-operatória em cadelas submetidas à ovariossalpingohisterectomia

A avaliação da dor em animais necessita da utilização de escalas de avaliação, que dependem da interpretação realizada por observadores. O objetivo do presente estudo foi avaliar a correlação entre a escala visual analógica (EVA), escala de Melbourne e os filamentos de Von Frey, na avaliação da dor pós-operatória em 42 cadelas adultas e saudáveis, submetidas à ovariossalpingohisterectomia (OSH). A dor pós-operatória foi avaliada por dois observadores cegos aos tratamentos analgésicos, em intervalos de uma hora, utilizando a EVA, a escala de Melbourne e os filamentos de Von Frey, aplicados ao redor da incisão cirúrgica. Foram considerados como critérios para realização da analgesia resgate uma pontuação de 50mm na EVA ou de 13 pontos na escala de Melbourne. A EVA revelou-se a escala mais sensível, uma vez que 100% dos animais receberam resgate seguindo esse método. Os valores obtidos na EVA e na escala de Melbourne determinaram boa correlação, com r=0,74, o que não ocorreu com os filamentos de Von Frey (r=-0,18). Já a correlação entre a escala de Melbourne e os filamentos de Von Frey foi de -0.37. Apesar de a EVA e a escala de Melbourne apresentarem boa correlação, sugere-se que se considere uma pontuação menor na escala de Melbourne como critério para administração de analgesia resgate.