Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Automated diagnosis of human intestinal parasites using optical microscopy images

Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes. © 2013 IEEE.

Dental fluorescence: Potential forensic use

In cases of identification of bones, skeletal segments or isolated bones, searching for biotypologic diagnostic data to estimate an individual's age enables comparing these data with those of missing individuals. Enamel, dentin and pulp undergo remarkable changes during an individual's life. The enamel becomes more mineralized, smoother and thinner, and deteriorates because of physiological and pathological factors. Dental pulp decreases in volume due to the deposition of secondary dentin; thus, the dentin becomes thicker with time. In natural teeth, the fluorescence phenomenon occurs in dentin and enamel and changes in those tissues may alter the expression of the natural tooth color. The aim of this study was to assess the correlation between age and teeth fluorescence for individuals from different age groups. The sample consisted of 66 randomly selected Brazilians of both genders aged 7-63 years old. They were divided into 6 groups: Group 1 - aged 7-12 years, Group 2 - aged 13-20 years, Group 3 - aged 21-30 years, Group 4 - aged 31-40 years, Group 5 - aged 41-50 years and Group 6 - aged between 51 and 63 years. Upper right or left central incisors were used for the study. Restored and aesthetic rehabilitated teeth were excluded from the sample. The measurement of tooth fluorescence was carried out via computer analysis of digital images using the software ScanWhite DMC/Darwin Systems - Brazil. It was observed that dental fluorescence decreases when comparing the age groups 21-30, 31-40, 41-50 and 51-63 years. The results also showed that there is a statistically significant difference between the groups 41-50 years and 21-30 years (p=. 0.005) and also among the group 51-63 years and all other groups (p< 0.005). It can be concluded that dental fluorescence is correlated with age and has a similar and stable behavior from 7 to 20 years of age. It reaches its maximum expected value at the age of 26.5 years and thereafter decreases. © 2013 Elsevier B.V.

HTS: A new shape descriptor based on Hough Transform

In this paper we propose a novel method for shape analysis called HTS (Hough Transform Statistics), which uses statistics from Hough Transform space in order to characterize the shape of objects in digital images. Experimental results showed that the HTS descriptor is robust and presents better accuracy than some traditional shape description methods. Furthermore, HTS algorithm has linear complexity, which is an important requirement for content based image retrieval from large databases. © 2013 IEEE.

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

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Visualização 3D de uma imagem digital

Pós-graduação em Ciências Cartográficas - FCT

Imagem digital em movimento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Detecção de Crateras de Impacto em Imagens Digitais. Estudo de caso: Superfície de Marte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise discriminatória de alvos da paisagem urbana em imagens aéreas multiespectrais

Pós-graduação em Ciências Cartográficas - FCT

Sistema CIE Lab: análise computacional de fotografias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da reprodutibilidade do diagnóstico de microinfiltração marginal

Pós-graduação em Ciências Odontológicas - FOAR

Desenvolvimento de um programa computacional para classificação do uso da terra em imagens CBERS 2

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

Sensoriamento remoto e geoprocessamento aplicado ao estudo temporal do uso da terra e na comparação entre classificação não supervisionada e análise visual

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

Modelos matemáticos para o retoque digital de imagens

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fractografia quantitativa: análise do comportamento fractal da fratura por explosão hidrostática em cilindros de aço sem costura

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)