SCG - Research Projects

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Computer vision methods applied to the identification and analysis of plants

FAPESP 11/01523-1

This project aims to develop and apply computational methods in order to identify, characterize and analyze plants using images of leaves. The main motivation of this study is to contribute to the morphology techniques already developed, facilitating and streamlining the task of identifying plant species. For morphology and texture of the leaves are considered macroscopic and microscopic image analysis, thereby promoting a better identification of the species. The nature of plant biology and its characteristics of large intra-species and small differences between species, make the problem of the characterization and identification leaves a great scientific challenge. Motivated by this problem the proponent group studies and develops methods for its solution for almost a decade, featuring a symbiotic approach, which are benefiting the biological sciences, computer science and applied mathematics. In plant biology, the use of sophisticated mathematics and computing, favor the emergence of tools to enhance the study of plants. Already in computing and mathematics, the problem in question motivates the development and improvement methodology.

Image analysis based on fractals descriptors

This project describes the development, study and application of fractal descriptors to texture analysis. Most of these works use fractal dimension as a descriptor of the object depicted in the image. However, due to the complexity of many problems in this area, some solutions have been proposed to improve this analysis. These proposed methods use not only the value of fractal dimension, but a set of measures which could be extracted by fractal geometry to describe the textures with greater richness and accuracy. Among such techniques, we emphasize the multifractal methodology, multiscale fractal dimension and, more recently, fractal descriptors. This latter technique has demonstrated to be efficient in solving problems related to the discrimination of texture and shape images. This is possible as the extracted descriptors provide a direct representation of the complexity (the details distribution along the scales of observation) in the image. Thus, this solution allows for a rich description of the image studied by analyzing the spatial/spectral distribution of pixels and intensity of colors/gray-levels, with a model which can approximate the human visual perception, generating an automatic and precise method. However, the works about fractal descriptors presented in the literature focus on classical methods to estimate fractal dimension, such as Bouligand-Minkowski and Box-counting. This project aims at studying more deeply the concept, generalizing to other approaches in fractal dimension, as well as exploring different ways of extracting the key features from the logarithmic curve associated with the dimension. The developed methods are applied to texture analysis, in classification problems over public databases, whose results can be compared with literature methods, as well as to the segmentation of satellite images and automatically identifying samples obtained from studies on nanotechnology. The results demonstrate the potential of the methodology developed to solve such problems, showing that this is a new frontier to be explored and used in image analysis and computer vision at all.

Fractals applied to bioimageinformatics

This project proposes the development and application of mathematical and image analysis tools derived from fractal geometry to the analysis and identification of plant species of Brazilian flora. The proposed use of fractal geometry for this analysis comes from the precision that this approach presents in the modeling of objects from nature. Actually, elements such as plant leaves have properties closer to those of a fractal object than to objects of classical geometry. For example, these structures are highly complex and have a significant level of self-similarity in their appearance. This work will explore techniques known in the literature such as multifractal and fractal descriptors and propose new approaches for extracting image features based on fractal geometry. This combination of mathematical and computational tools with biological techniques will provide benefits for both exact and biological areas. In this way, this study allows the development of a tool that can turn the process of identifying and analyzing plants more precise and agile. At the same time, it encourages the development of more advanced techniques in image analysis.

Cryptograph using chaos theory

Since ancient times, cryptography has been used as a tool to exchange secret messages by keeping it protected and hardly to read. Most of well-known encryption algorithms are based on classical mathematics. But recently some new mathematical methodologies have being considered: chaos theory. The beauty of chaos theory is that even simple equations can generate unexpected complex behavior. For example, the simpler Logistic Map can change over time in an apparently random manner, when it is conveniently arranged hence its sensitiveness to initial conditions. Many chaotic-cryptosystems have been proposed based on complex systems such as: cellular automata and chaotic maps with excellent results. Therefore cryptography can take advantages of these chaotic features that led to a new kind of cryptography.