Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

Automatic segmentation of latent fingerprints

Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. © 2012 IEEE.

Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.

Ki-67 labeling in canine perianal glands neoplasms: A novel approach for immunohistological diagnostic and prognostic

Background: The antibody Ki-67 is a reliable and easy tool to accurately assess the growth fraction of neoplasms in humans and animals, and it has been used to predict the clinical outcome. Therefore, the aim of the present study was to investigate the immunohistochemical expression pattern of Ki-67 in normal and neoplastic perianal glands of dogs to evaluate the possible use of this proliferation marker as an ancillary method of perianal tumor diagnosis. We studied 42 cases of perianal gland neoplasms including adenomas (n = 15), epitheliomas (n = 15), and carcinomas (n = 12). As controls, 13 tissue samples from normal perianal glands were used. A Ki-67 index was established by a computer-assisted image analysis and compared with manual counting. Results: Out of the 42 cases of perianal gland neoplasms, 34 were from males and eight from females. Recurrence was reported in 14 cases, being higher (8/12) in carcinomas. Immunostaining for Ki-67 revealed that the carcinomas showed a higher proliferation rate (9.87%) compared to groups of epitheliomas (2.66%) and adenomas (0.36%). For adenomas and epitheliomas of the perianal glands the computer-assisted counting and the manual counting gave similar results; however, only the computer-assisted image analysis was efficient to predict the perianal gland carcinoma recurrence.Conclusion: Since there were significant differences in the number of Ki-67-positive nuclei, this marker proved to be effective in helping the classification of perianal gland neoplasms and to refine the diagnosis criteria, especially in those samples with high variation in morphology/area. Also, higher Ki-67 index is related to recurrence in cases of perianal gland carcinomas. Further, the computer-assisted image analysis proved to be a fast and reliable method to assess the Ki-67 index in perianal gland neoplasms. © 2013 Pereira et al.; licensee BioMed Central Ltd.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Propriedades físicas de um latossolo vermelho manejado com material orgânico e submetido à compactação

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparação de métodos de mapeamento da distribuição espacial da infestação de plantas daninhas

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Avaliação de um biorreator rotativo para fermentação em estado sólido

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise microestrutural de revestimentos usados no reparo de turbinas hidráulicas danificadas pela cavitação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Emprego da fotografia digital para avaliação da topografia microscópia da pele

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Utilização de bulked segregant analysis na identificação de marcadores ligados a genes que controlam a resistência à ferrugem (Puccinia psidii Winter) em Eucalyptus sp

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise comparativa entre o sistema digital Digora e a digitalização da radiografia convencional na avaliação da densidade radiográfica do processo de reparo alveolar

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo das características morfométricas de diferentes regiões do intestino delgado e índices zootécnicos em quatro linhagens de frangos de corte

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

Avaliação da qualidade da obturação de canais radiculares e da confiabilidade do modelo de penetração de glicose

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação da aplicabilidade de sistema computacional de visualização de imagens aéreas de alta resolução em tempo real e multitemporal à degradação do rio Corumbataí

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)