Three-dimensional connectivity index for texture recognition

AbstractThis work proposes a new method of texture analysis for grey-level images based on the distribution of connectivity indexes in local neighbourhoods. The connectivity index acts as a measure of homogeneity of textures and its distribution is computed at various local neighbourhood sizes. The resulting descriptors provide an efficient multiscale representation of connectivity at different scales. The method was tested in the classification of UIUC, Outex, and KTH-TIPS2b databases and outperformed several state-of-the-art approaches, including such as LBP, LBP+VAR, MR8, multifractals among others

Identifying plant species using architectural features of leaf microscopy images

This work proposes an analytical method to identify plant species based on microscopy images of the midrib cross-section of leaves. Unlike previous shape-based approaches based on the individual shape of external contours and cells, an architectural analysis is proposed, where the midrib is semi-automatically segmented and partitioned into histologically relevant structures composed of layers of cells and vascular structures. Using a sequence of morphological operations, a set of geometrical measures from the cells in each layer is extracted to produce a vector of features for species categorization. The method applied to a database containing 10 species of plants from the Brazilian flora achieved a success rate of 91.7%, outperforming other classical shape-based approaches published in the literature.Fil: Florindo, Joao Batista. Universidade de Sao Paulo; BrasilFil: Bruno, Odemir Martinez. Universidade de Sao Paulo; BrasilFil: Rossatto, Davi Rodrigo. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Kolb, Rosana Marta. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Gómez, María Cecilia. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Landini, Gabriel. University Of Birmingham; Reino Unid

Fractal descriptors of texture Images based on the triangular prism dimension

This work presents a novel descriptor for texture images based on fractal geometry and its application to image analysis. The descriptors are provided by estimating the triangular prism fractal dimension under different scales with a weight exponential parameter, followed by dimensionality reduction using Karhunen-Loeve transform. The efficiency of the proposed descriptors is tested on four well-known texture data sets, that is, Brodatz, Vistex, UIUC and KTH-TIPS2b, both for classification and image retrieval. The novel method is also tested concerning invariances in situations when the textures are rotated or affected by Gaussian noise. The obtained results outperform other classical and state-of-the-art descriptors in the literature and demonstrate the power of the triangular descriptors in these tasks, suggesting their use in practical applications of image analysis based on texture features611140159CNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo307797/2014-7; 484312/2013-8; 301480/2016-814/08026-1; 2013/22205-3; 2012/19143-3; 2016/16060-