Co-occurrence features of multi-scale directional filter bank for texture characterization

Author name used in this publication: N. F.LawAuthor name used in this publication: K. O. ChengAuthor name used in this publication: W. C. SiuRefereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Environmentally and behaviourally mediated co-occurrence of functional traits in bird communities of tropical forest fragments

Two major theories of community assembly - based on the assumption of limiting similarity' or 'habitat filtering', respectively - predict contrasting patterns in the spatial arrangement of functional traits. Previous analyses have made progress in testing these predictions and identifying underlying processes, but have also pointed to theoretical as well as methodological shortcomings. Here we applied a recently developed methodology for spatially explicit analysis of phylogenetic meta-community structure to study the pattern of co-occurrence of functional traits in Afrotropical and Neotropical bird species inhabiting forest fragments. Focusing separately on locomotory, dietary, and dispersal traits, we tested whether environmental filtering causes spatial clustering, or competition leads to spatial segregation as predicted by limiting similarity theory. We detected significant segregation of species co-occurrences in African fragments, but not in the Neotropical ones. Interspecific competition had a higher impact on trait co-occurrence than filter effects, yet no single functional trait was able to explain the observed degree of spatial segregation among species. Despite high regional variability spanning from spatial segregation to aggregation, we found a consistent tendency for a clustered spatial patterning of functional traits among communities in fragmented landscapes, particularly in non-territorial species. Overall, we show that behavioural effects, such as territoriality, and environmental effects, such as the area of forest remnants or properties of the landscape matrix in which they are embedded, can strongly affect the pattern of trait co-occurrence. Our findings suggest that trait-based analyses of community structure should include behavioural and environmental covariates, and we here provide an appropriate method for linking functional traits, species ecology and environmental conditions to clarify the drivers underlying spatial patterns of species co-occurrence

The effect of phylogeny, environment and morphology on communities of a lianescent clade (Bignonieae-Bignoniaceae) in neotropical biomes

The influence of ecological traits to the distribution and abundance of species is a prevalent issue in biodiversity science. Most studies of plant community assembly have focused on traits related to abiotic aspects or direct interactions among plants, with less attention paid to ignore indirect interactions, as those mediated by pollinators. Here, we assessed the influence of phylogeny, habitat, and floral morphology on ecological community structure in a clade of Neotropical lianas (tribe Bignonieae, Bignoniaceae). Our investigation was guided by the long-standing hypothesis that habitat specialization has promoted speciation in Bignonieae, while competition for shared pollinators influences species co-occurrence within communities. We analyzed a geo-referenced database for 94 local communities occurring across the Neotropics. The effect of floral morphological traits and abiotic variables on species co-occurrence was investigated, taking into account phylogenetic relationships. Habitat filtering seems to be the main process driving community assembly in Bignonieae, with environmental conditions limiting species distributions. Differing specialization to abiotic conditions might have evolved recently, in contrast to the general pattern of phylogenetic clustering found in communities of other diverse regions. We find no evidence that competition for pollinators affects species co-occurrence; instead, pollinator occurrence seems to have acted as an "environmental filter'' in some habitats93FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2006/59916-0CCSD-Missouri Botanical Garden: Elizabeth E. Bascom Fellowships for Latin American Female Botanist

Using conceptual vectors to get Magn collocations (and using contrastive properties to get their translations)

International audienceThis paper presents a semi-automatic approach for extraction of collocations from corpora which uses the results of Conceptual Vectors as a semantic filter. First, this method estimates the ability of each co-occurrence to be a collocation, using a statistical measure based on the fact that it occurs more often than by chance. Then the results are automatically filtered (with conceptual vectors) to retain only one given semantic kind of collocations. Finally we perform a new filtering based on manually entered data. Our evaluation on monolingual and bilingual experiments shows the interest to combine automatic extraction and manual intervention to extract collocations (to fill multilingual lexical databases). It proves especially that the use of conceptual vectors to filter the candidates allows us to increase the precision noticeably

A methodology for filtering association rules

Basket data analysis is an important issue in the area of Artificial Intelligence and Decision Support Systems. Association rules are a model that represents co-occurrence of items in a transaction according to some support and confidence measures. However, sometimes the number of generated association rules is too large to be analyzed. A methodology is presented to highlight the strongest rules, using a filter. Experiment results show that this filter is efficient and capable of making basket data analysis easier to implement.Resumo: A análise de dados de cestos de compras é um assunto importante na área de Inteligência Artificial e Sistemas de Apoio à Decisão. As regras de associação são um modelo que representa co-ocorrência de itens numa transacção segundo determinados valores de suporte e confiança. No entanto, o número de regras geradas é, por vezes, suficientemente grande, dificultando a análise. Uma metodologia é apresentada para evidenciar as regras mais fortes, usando um filtro, preservando as restantes. Os resultados experimentais mostram que este filtro é eficiente e capaz de tornar a análise de dados de cestos de compras mais fácil de realizar.peerreviewe

Multi texture analysis of colorectal cancer continuum using multispectral imagery

Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images

Integration of Spectral and Textural Features from Ikonos Image to Classify Vegetation Cover in Mountainous Area

Studi ini mengevaluasi penggunaan fitur spektral dan tekstur secara terintegrasi yang didapat dari citra IKONOS untuk mengindentifikasi tipe-tipe tutupan lahan pertanian di daerahpegunungan. Studi meliputi pra pengolahan citra, pengembangan metode kuantisasi citra, penghitungan nilai tekstur, pembuatan dataset dan penilaian akurasi. Pra pengolahan citra berfokus pada registrasi citra dan normalisasi topografis. Dalam studi ini dikembangkan dua metodekuantisasi citra yaitu segmentasi citra dan filter rata-rata. Segmentasi citra mengklasifikasi citra kedalam beberapa segmentasi berdasarkan determinasi jumlah total piksel setiap kelas, sedangkan filter rata-rata mengelompokkan citra berdasarkan rata-rata nilai angka dijital dalam ukuranwindow tertentu. Empat ukuran tekstur yaitu inverse difference moment, contrast, entropy dan energy dihitung dengan grey level co-occurrence matrix (GLCM). Hasil studi menunjukkankombinasi aspek spektral dan tekstur meningkatkan akurasi klasifikasi secara signifikan dibandingkan klasifikasi hanya menggunakan fitur spektral saja. Segmentasi citra dan filter rata-rata dapat memberikan bentuk-bentuk spasial tipe tutupan lahan pertanian yang lebih efektif dibanding menggunakan citra dengan derajat keabuan 256. Ketelitian keseluruhan meningkat 11,33% ketika menggunakan integrasi spektral dan fitur tekstur inverse difference moment (5x5) danenergy (9x9)