On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities ill images. This estimation is crucial for the problem of crowd monitoring. and control. The assessment is carried out oil a set of nearly 300 real images captured from Liverpool Street Train Station. London, UK using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight lille segments. Fourier analysis. and fractal dimensions. The estimations of dowel densities are given in terms of the classification of the input images ill five classes of densities (very low, low. moderate. high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model). Bayesian. and an approach based on fitting functions. The results obtained by these three classifiers. using the four texture measures. allowed the conclusion that, for the problem of crowd density estimation. texture analysis is very effective.

Assessment of luteal function in goats by ultrasonographic image attribute analysis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Statistical analysis of texture in trunk images for biometric identification of tree species

The identification of tree species is a key step for sustainable management plans of forest resources, as well as for several other applications that are based on such surveys. However, the present available techniques are dependent on the presence of tree structures, such as flowers, fruits, and leaves, limiting the identification process to certain periods of the year Therefore, this article introduces a study on the application of statistical parameters for texture classification of tree trunk images. For that, 540 samples from five Brazilian native deciduous species were acquired and measures of entropy, uniformity, smoothness, asymmetry (third moment), mean, and standard deviation were obtained from the presented textures. Using a decision tree, a biometric species identification system was constructed and resulted to a 0.84 average precision rate for species classification with 0.83accuracy and 0.79 agreement. Thus, it can be considered that the use of texture presented in trunk images can represent an important advance in tree identification, since the limitations of the current techniques can be overcome.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Plant species identification using multi-scale fractal dimension applied to images of adaxial surface epidermis

This paper presents the study of computational methods applied to histological texture analysis in order to identify plant species, a very difficult task due to the great similarity among some species and presence of irregularities in a given species. Experiments were performed considering 300 ×300 texture windows extracted from adaxial surface epidermis from eight species. Different texture methods were evaluated using Linear Discriminant Analysis (LDA). Results showed that methods based on complexity analysis perform a better texture discrimination, so conducting to a more accurate identification of plant species. © 2009 Springer Berlin Heidelberg.

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.

Texture profile and correlation between sensory and instrumental analyses on extruded snacks

Instrumental texture analysis on extruded snacks is widely applied, however there is no scientific consensus about the test and probe types that can be correlated with the sensory texture of snacks. Eleven commercial extruded snacks of different shapes were evaluated instrumentally using different probes and sensorially through descriptive analysis. The snack texture was described using the attributes of hardness, crispness, adhesiveness, fracturability and chewiness. Cylindrical snacks were described through crispness and fracturability, pelleted and shell-shaped snacks by chewiness and ring-shaped snacks by adhesiveness and hardness. Hardness and adhesiveness were correlated with a Warner-Bratzler test using a V shape probe (r = 0.718 and r = 0.763, respectively), while fracturability and chewiness were correlated with a Warner-Bratzler test using a guillotine (r = 0.776 and r = 0.662, respectively). The fairly strong good correlations enable application of these instrumental tests as an indication of the sensory texture of extruded snacks. © 2013 Elsevier Ltd. All rights reserved.

Materialografia quantitativa de micro estruturas complexas baseada na segmentação por texturas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Método de detecção e classificação de lesões de pele em imagens digitais a partir do modelo Chan-Vese e máquina de vetor de suporte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fractal descriptors for discrimination of microscopy images of plant leaves

This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.

Adição de areia para dispersão de solos na análise granulométrica

A acurácia da análise granulométrica depende da obtenção de suspensões de solo completamente dispersas e estáveis para possibilitar a separação das suas frações granulométricas. O objetivo do presente trabalho foi avaliar a eficácia da adição de quantidades e tamanhos de grãos de areia na fase de dispersão da análise granulométrica de solos, visando à maior acurácia na obtenção dos resultados da análise granulométrica. Os solos utilizados foram: Latossolo Vermelho eutroférrico (LVef), LatossoloVermelho acriférrico (LVwf), Latossolo Vermelho eutrófico (LVe), Argissolo Vermelho-Amarelo eutrófico (PVAe) e Nitossolo Vermelho eutroférrico (NVef). A dispersão das amostras dos solos foi realizada por meio da adição de hidróxido de sódio e agitação rotativa (60 rpm) por 16 h. O delineamento experimental adotado foi o inteiramente casualizado, com esquema fatorial 6 x 2, com três repetições. Os tratamentos foram constituídos por seis quantidades (0, 5, 10, 15, 20 e 25 g) e dois diâmetros (2,0-1,0 e 1,0-0,5 mm) de areia, adicionados na fase de dispersão da análise granulométrica dos solos. de acordo com as equações ajustadas, a adição de areia com diâmetro entre 1,0 e 0,5 mm nas quantidades de 21,4 g para LVef, 19,6 g para LVwf e 25,8 g para NVef proporciona, respectivamente para esses solos, aumentos de 50, 38 e 14,5 % nos teores de argila. No LVe e no PVAe não se justifica a adição de areia na análise granulométrica, pois esses solos não apresentaram problemas de dispersão. Os resultados demonstram que a adição de 25 g de areia, com diâmetro entre 1,0 e 0,5 mm, na fase de dispersão da análise granulométrica de solos argilosos com altos teores de óxidos de Fe e com dificuldades de dispersão, é eficiente para promover efetiva dispersão das partículas primárias do solo.

Extended depth from focus reconstruction using NIH ImageJ plugins: Quality and resolution of elevation maps

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

Visibilidade na MPB: isotopias na composição musical de Renato Russo e sua atualização midiática : uma abordagem sincrética

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desequilíbrio de ligação e associação entre polimorfismos de base única com maciez da carne e espessura de gordura em bovinos Nelore utilizando painéis de alta densidade

Pós-graduação em Genética e Melhoramento Animal - FCAV