Dalcimar Casanova

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has an efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, curvature, Zernike moments and multiscale fractal dimension).

Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistic...

A Systematic Comparison of Supervised Classifiers

Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

Texture analysis and classification: A complex network-based approach

In this paper, we propose a novel texture analysis method using the complex network theory. We investigated how a texture image can be effectively represented, characterized and analyzed in terms of a complex network. The proposed approach uses degree measurements to compose a set of texture descriptors. The results show that the method is very robust, and it presents a excellent texture discrimination for all considered classes, overcoming traditional texture methods.

Fractal analysis of leaf-texture properties as a tool for taxonomic and identification purposes: a case study with species from Neotropical Melastomataceae (Miconieae tribe)

Melastomataceae is a common and dominant family in Neotropical vegetation, with high species diversity which leads to a large variation in some morphological structures. Despite this, some species of Melastomataceae are very similar in their external leaf morphology, leading to difficulties in their identification without the presence of reproductive organs. Here we have proposed and tested a computer-aided texture-based approach used to correctly identify and distinguish leaves of some species of Melastomataceae that occur in a region of Neotropical savanna in Southeastern Brazil, also comparing it with other previously proposed approaches. The results demonstrated that our approach may clearly separate the studied species, analyzing the patterns of leaf texture (both adaxial and abaxial surfaces), and achieving better accuracy (100%) than other methods. Our work has suggested that leaf texture properties can be used as a new characteristic for identification, and as an additional source of information in taxonomic and systematic studies. As the method may be supervised by experts, it is also suitable for discrimination of species with high morphological plasticity, improving the automated discrimination task. This approach can be very useful for identification of species in the absence of reproductive material, and is a rapid and powerful tool for plant identification.

Texture analysis using fractal descriptors estimated by the mutual interference of color channels

Fractal descriptors are used to describe color textures.Spatial structure and color distribution are analyzed without separating the image into channels.The volumes of the dilated structure express the level of details at each scale based on the intensity/color distribution.The descriptors combine the efficiency of fractals in describing complex structures with the richness of color analysis.The method outperformed other approaches whose effectiveness is widely attested in studies on texture analysis methods. This work presents a method for color texture analysis based on fractal geometry. The method is based on its predecessor 4 and consists of mapping each color channel onto a surface and dilating such surface by spheres with a variable radius. The descriptors are obtained from the relation between the volumes of the dilated surfaces and the dilation radii. The dilation process creates a mutual interference among the color channels. The proposed descriptors measure the degree of such interference as well as the complexity of pixel intensity arrangements. This combination provides a robust and precise texture description. The efficiency of the method is assessed in a classification task of well-known texture data sets and the results demonstrate that it outperforms the best approaches described in the literature.

Partial differential equations and fractal analysis to plant leaf identification

Texture is an important visual attribute used to plant leaf identification. Although there are many methods of texture analysis, some of them specifically for interpreting leaf images is still a challenging task because of the huge pattern variation found in nature. In this paper, we investigate the leaf texture modeling based on the partial differential equations and fractal dimension theory. Here, we are first interested in decomposing the original texture image into two components f = u + v, such that u represents a cartoon component, while v represents the oscillatory component. We demonstrate how this procedure enhance the texture component on images. Our modeling uses the non-linear partial differential equation (PDE) of Perona-Malik. Based on the enhanced texture component, we estimated the fractal dimension by the Bouligand-Minkowski method due to its precision in quantifying structural properties of images. The feature vectors are then used as inputs to our classification system, based on linear discriminant analysis. We validate our approach on a benchmark with 8000 leaf samples. Experimental results indicate that the proposed approach improves average classification rates in comparison with traditional methods. The results suggest that the proposed approach can be a feasible step for plant leaf identification, as well as different real-world applications.

A complex network-based approach for texture analysis

In this paper, we propose a novel texture analysis method using the complex network theory. It was investigated how a texture image can be effectively represented, characterized and analyzed in terms of a complex network. The propose uses degree measurements in a dynamic evolution network to compose a set of feasible shape descriptors. Results show that the method is very robust and it presents a very excellent texture discrimination for all considered classes.

Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation

This work proposes to obtain novel fractal descriptors from gray-level texture images by combining information from interior and boundary measures of the Minkowski dilation applied to the texture surface. At first, the image is converted into a surface where the height of each point is the gray intensity of the respective pixel in that position in the image. Thus, this surface is morphologically dilated by spheres. The radius of such spheres is ranged within an interval and the volume and the external area of the dilated structure are computed for each radius. The final descriptors are given by such measures concatenated and subject to a canonical transform to reduce the dimensionality. The proposal is an enhancement to the classical Bouligand–Minkowski fractal descriptors, where only the volume (interior) information is considered. As different structures may have the same volume, but not the same area, the proposal yields to more rich descriptors as confirmed by results on the classification of benchmark databases.