Jarbas Joaci de Mesquita Sá Junior

Color texture classification using shortest paths in graphs.

Color textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze color textures by modeling a color image as a graph in two different and complementary manners (each color channel separately and the three color channels altogether) and by obtaining statistical moments from the shortest paths between specific vertices of this graph. Such an approach allows to create a set of feature vectors, which were extracted from VisTex, USPTex, and TC00013 color texture databases. The best classification results were 99.07%, 96.85%, and 91.54% (LDA with leave-one-out), 87.62%, 66.71%, and 88.06% (1NN with holdout), and 98.62%, 96.16%, and 91.34% (LDA with holdout) of success rate (percentage of samples correctly classified) for these three databases, respectively. These results prove that the proposed approach is a powerful tool for color texture analysis to be explored.

A simplified gravitational model to analyze texture roughness

Textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze texture, based on representing states of a simplified gravitational collapse from an image and extracting information from each state using fractal dimension. In this approach, an image evolves in times t={1,2,...,20}, each time representing a state, which is explored by the Bouligand-Minkowski method using radius r={3,4,...,8}. These parameters allow to create a set of feature vectors, which were extracted from Brodatzs textures and leaf textures. The best classification results were 98.75% and 86.67% of success rate (percentage of samples correctly classified) for these two databases, respectively. These results prove that the proposed approach opens a promising source of research in texture analysis to be explored.

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.

Color texture classification based on gravitational collapse

Texture and color are essential attributes to be analyzed for any robust computer vision system. This paper presents a novel method to analyze color-texture images, based on representing states of a simplified gravitational collapse from each image color channel and extracting information from each state using the Bouligand-Minkowski fractal dimension and the lacunarity method. In this approach, we obtained the best classification results when the images of each channel evolved in times t={1,5,10,15}, each time representing a state, using radius r={3,4,5,6} for the Bouligand-Minkowski method and box size l={2,3,4,5,6} for the lacunarity method. The best classification results were 99.37% and 96.57% of success rate (percentage of samples correctly classified) for VisTex and USPTex databases, respectively. These results prove that the proposed approach opens a promising source of research in color texture analysis still to be explored.

A computer vision approach to quantify leaf anatomical plasticity: A case study on gochnatia polymorpha (less.) cabrera

Abstract Inferences about leaf anatomical characteristics had largely been made by manually measuring diverse leaf regions, such as cuticle, epidermis and parenchyma to evaluate differences caused by environmental variables. Here we tested an approach for data acquisition and analysis in ecological quantitative leaf anatomy studies based on computer vision and pattern recognition methods. A case study was conducted on Gochnatia polymorpha (Less.) Cabrera (Asteraceae), a Neotropical savanna tree species that has high phenotypic plasticity. We obtained digital images of cross-sections of its leaves developed under different light conditions (sun vs. shade), different seasons (dry vs. wet) and in different soil types (oxysoil vs. hydromorphic soil), and analyzed several visual attributes, such as color, texture and tissues thickness in a perpendicular plane from microscopic images. The experimental results demonstrated that computational analysis is capable of distinguishing anatomical alterations in microscope images obtained from individuals growing in different environmental conditions. The methods presented here offer an alternative way to determine leaf anatomical differences.

A simplified gravitational model for texture analysis

Textures are among the most important features in image analysis. This paper presents a novel methodology to extract information from them, converting an image into a simplified dynamical system in gravitational collapse whose states are described by using the lacunarity method. The paper compares the proposed approach to other classical methods using Brodatzs textures as benchmark.

Plant Leaf Classification Using Color on a Gravitational Approach

Literature describes the analysis and identification of plant leaves as a difficult task. Many features may be used to describe a plant leaf. One of them is its texture, which is also one of most important features in image analysis. This paper proposes to study the texture information of all three color channels of a plant leaf by converting it into a simplified gravitational system in collapse. We also use fractal dimension to describe the states of the gravitational collapse as they occur. This enable us to describe the texture information as a function of complexity and colapsing time. During the experiments, we compare our approach to other color texture analysis methods in a plant leaves dataset.

LBP maps for improving fractal based texture classification

Abstract This paper presents an innovative manner of obtaining discriminative texture signatures by using the LBP approach to extract additional sources of information from an input image and by using fractal dimension to calculate features from these sources. Four strategies, called Min, Max, Diff Min and Diff Max , were tested, and the best success rates were obtained when all of them were employed together, resulting in an accuracy of 99.25%, 72.50% and 86.52% for the Brodatz, UIUC and USPTex databases, respectively, using Linear Discriminant Analysis. These results surpassed all the compared methods in almost all the tests and, therefore, confirm that the proposed approach is an effective tool for texture analysis.

Gravitational Based Texture Roughness for Plant Leaf Identification

The analysis and identification of plant leaves is a difficult task. Among the many features available for its identification, texture pattern is one of the most important. In this work we propose to explore texture information from a plant leaf by converting it into a simplified dynamical system in gravitational collapse. We use complexity estimates, such as fractal dimension and lacunarity, to describe the states of gravitation collapse of the system and, as a consequence, the texture itself. We also compare our approach to other classical texture analysis methods in a plant leaf dataset.

Texture Classification of Phases of Ti-6Al-4V Titanium Alloy Using Fractal Descriptors

Traditionally, the evaluation of metal microstructures and their physical properties is a subject of study in Metallography. Through microscopy, we obtain images of the microstructures of the material evaluated, while a human expert performs its analysis. However, texture is an important image descriptor as it is directly related to the physical properties of the surface of the object. Thus, in this paper, we propose to use texture analysis methods to automatically classify metal microstructures, more specifically, the phases of a Titanium alloy, Ti-6Al-4V. We performed texture analysis using the Bouligand-Minkowski fractal dimension method, which enables us to describe a texture image in terms of its irregularity. Experiments were performed using 3900 texture samples of 2 different phases of the titanium alloy. We used LDA (Linear Discriminant Analysis) to evaluate computed texture descriptors. The results indicated that fractal dimension is a feasibility tool for the evaluation of the microstructures present in the metal samples.