Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Comparison of Two Staining Methods for Pollen Viability Studies in Sugarcane

The present work aimed to compare two staining methods for pollen viability evaluation in sugarcane. Pollen from four sugarcane genotypes were collected at three different times (6.00, 8.00 and 9.00 a.m.) and tested for viability using two staining methods (iodine and lactophenol blue). Three anthers, of each genotype were crushed in a glass slide with a drop of the respective stain (iodine 0.1 N and lactophenol blue). The percentage of pollen viability was obtained with an optic microscope (250×) and compared with the pollen germination at culture media where one raquis of each genotype was gentle shaken in a petridish. Three replicates (petri dishes) was performed for each genotype which were maintained at the temperature of 25 °C and air humidity around 95 % for 30 min. The factors (staining methods, genotypes and times) and their interactions were evaluated by the analysis of variance, F test (P < 0.01) and the means compared by the t test (P < 0.05). The lactophenol blue staining was more sensible than the iodine staining method to detect the decrease of pollen viability which occurs naturally in sugarcane. The iodine staining method was more stable and easier than lactophenol to perform the inflorescence classification at any evaluated time (6.00, 8.00 and 9.00 a. m.). Both staining methods overestimated the viability obtained by the germination at culture media when performed at 6.00 a.m. © 2012 Society for Sugar Research & Promotion.

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

A study of neolignan compounds with biological activity against Paracoccidioides brasiliensis by using quantum chemical and chemometric methods

Métodos quimiométricos (estatísticos) são empregados para classificar um conjunto de compostos derivados de neolignanas com atividade biológica contra a Paracoccidioides brasiliensis. O método AM1 (Austin Model 1) foi utilizado para calcular um conjunto de descritores moleculares (propriedades) para os compostos em estudo. A seguir, os descritores foram analisados utilizando os seguintes métodos de reconhecimento de padrões: Análise de Componentes Principais (PCA), Análise Hierárquica de Agrupamentos (HCA) e o método de K-vizinhos mais próximos (KNN). Os métodos PCA e HCA mostraram-se bastante eficientes para classificação dos compostos estudados em dois grupos (ativos e inativos). Três descritores moleculares foram responsáveis pela separação entre os compostos ativos e inativos: energia do orbital molecular mais alto ocupado (EHOMO), ordem de ligação entre os átomos C1'-R7 (L14) e ordem de ligação entre os átomos C5'-R6 (L22). Como as variáveis responsáveis pela separação entre compostos ativos e inativos são descritores eletrônicos, conclui-se que efeitos eletrônicos podem desempenhar um importante papel na interação entre receptor biológico e compostos derivados de neolignanas com atividade contra a Paracoccidioides brasiliensis.

Classification of honeys from Pará State (Amazon region, Brazil) produced by three different species of bees

Vinte e sete amostras de mel, produzidas em dez cidades do Estado do Pará (Região Amazônica, norte do Brasil) por três espécies diferentes de abelhas (Apis mellifera, Melipona fasciculata e Melipona flavoneata), foram analisadas em seus teores de elementos minerais (Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr e Zn) e alguns parâmetros fisicoquímicos (cor, umidade, densidade, pH, sólidos insolúveis e solúveis totais, cinzas, condutividade elétrica, índice de formol, acidez livre, hidroximetilfurfural, açúcares redutores e totais e sacarose). Os teores minerais foram determinados via espectrometria de emissão atômica por plasma acoplado indutivamente (ICP OES) e as análises dos parâmetros físico-químicos seguiram metodologias oficiais. Os resultados das análises físico-químicas apresentaram-se de acordo com a legislação nacional e internacional, bem como com outros trabalhos similares ao redor do mundo. A análise estatística multivariada (análise por agrupamento hierárquico (HCA) e por componentes principais (PCA)) foi aplicada aos resultados dos teores metálicos e aos parâmetros físico-químicos, sendo possível a separação das amostras de mel conforme a espécie produtora.

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Midpalatal suture maturation: Classification method for individual assessment before rapid maxillary expansion

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Use of classification in organizing and searching the web

The traditional characteristics and challenges for organizing and searching information on the World Wide Web are outlined and reviewed. The classification features of two of these methods, such as Google, in the case of automated search engines, and Yahoo! Directory, in the case of subject directories are analyzed. Recent advances in the Semantic Web, particularly the growing application of ontologies and Linked Data are also reviewed. Finally, some problems and prospects related to the use of classification and indexing on the World Wide Web are discussed, emphasizing the need of rethinking the role of classification in the organization of these resources and outlining the possibilities of applying Ranganathan's facet theories of classification.

Classification of Eucalyptus urograndis hybrids under different water availability based on biometric traits

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Staphylococcal enterotoxins: molecular aspects and detection methods

Members of the Staphylococcus genus, especially Staphylococcus aureus, are the most common pathogens found in hospitals and in community-acquired infections. Some of their pathogenicity is associated with enzyme and toxin production. Until recently, S. aureus was the most studied species in the genus; however, in last few years, the rise of infections caused by coagulase-negative staphylococci has pointed out the need for further studies on virulence factors that have not yet been completely elucidated so as to better characterize the pathogenic potential of this group of microorganisms. Several staphylococcal species produce enterotoxins, a family of related proteins responsible for many diseases, such as the toxic-shock syndrome, septicemia and food poisoning. To this date, 23 different enterotoxin types have been identified besides toxic-shock syndrome toxin-1 (TSST-1), and they can be divided into five phylogenetic groups. The mechanism of action of these toxins includes superantigen activity and emetic properties, which can lead to biological effects of infection. Various methods can detect genes that encode enterotoxins and their production. Molecular methods are the most frequently used at present. This review article has the objective to describe aspects related to the classification, structure and regulation of enterotoxins and toxic-shock syndrome toxin-1 detection methods.

A comparison of CA125, HE4, risk ovarian malignancy algorithm (ROMA), and risk malignancy index (RMI) for the classification of ovarian masses

OBJECTIVE: Differentiation between benign and malignant ovarian neoplasms is essential for creating a system for patient referrals. Therefore, the contributions of the tumor markers CA125 and human epididymis protein 4 (HE4) as well as the risk ovarian malignancy algorithm (ROMA) and risk malignancy index (RMI) values were considered individually and in combination to evaluate their utility for establishing this type of patient referral system. METHODS: Patients who had been diagnosed with ovarian masses through imaging analyses (n = 128) were assessed for their expression of the tumor markers CA125 and HE4. The ROMA and RMI values were also determined. The sensitivity and specificity of each parameter were calculated using receiver operating characteristic curves according to the area under the curve (AUC) for each method. RESULTS: The sensitivities associated with the ability of CA125, HE4, ROMA, or RMI to distinguish between malignant versus benign ovarian masses were 70.4%, 79.6%, 74.1%, and 63%, respectively. Among carcinomas, the sensitivities of CA125, HE4, ROMA (pre-and post-menopausal), and RMI were 93.5%, 87.1%, 80%, 95.2%, and 87.1%, respectively. The most accurate numerical values were obtained with RMI, although the four parameters were shown to be statistically equivalent. CONCLUSION: There were no differences in accuracy between CA125, HE4, ROMA, and RMI for differentiating between types of ovarian masses. RMI had the lowest sensitivity but was the most numerically accurate method. HE4 demonstrated the best overall sensitivity for the evaluation of malignant ovarian tumors and the differential diagnosis of endometriosis. All of the parameters demonstrated increased sensitivity when tumors with low malignancy potential were considered low-risk, which may be used as an acceptable assessment method for referring patients to reference centers.