Fractal dimension applied to plant identification

Bruno, Odemir Martinez; Plotze, Rodrigo de Oliveira; Falvo, Maurício; Castro, Mário de

doi:10.1016/j.ins.2008.01.023

Cited by 210 publications

(160 citation statements)

References 24 publications

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“…On the other hand, it also quantifies properties like complexity and spatial occupation, in the same way as the original fractal dimension concept. The effectiveness of this method as an image descriptor is validated in works like Backes and Bruno, 2012;Bruno et al, 2008). On (Zuniga et al,6419) the authors successfully demonstrated the power of VFD to describe the Gabor images.…”

Section: Volumetric Fractal Dimensionmentioning

confidence: 91%

“…The fractal dimension has found a number of applications in various data analysis studies (Camastra, 2003;Rozza et al, 2012) and, particularly, in image analysis Backes and Bruno, 2012;Bruno et al, 2008). Actually, as stated in Mandelbrot (1983) and Falconer (2003), natural objects cannot be well described using Euclidean geometry but using persistent self-repeating patterns, like those present in fractal objects.…”

Section: Volumetric Fractal Dimensionmentioning

confidence: 99%

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Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

Zuñiga

Florindo

Bruno

2014

Pattern Recognition Letters

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a b s t r a c tTexture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.

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Section: Volumetric Fractal Dimensionmentioning

confidence: 91%

Section: Volumetric Fractal Dimensionmentioning

confidence: 99%

Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

Zuñiga

Florindo

Bruno

2014

Pattern Recognition Letters

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“…Among the many approaches developed to estimate this parameter, Bouligand-Minkowski method [5,11,12] has emerged as the one that presents the most accurate results and has shown to be very sensitive to structural changes of the object. This method was originally developed for shape analysis.…”

Section: Fractal Dimension Based Approachmentioning

confidence: 99%

Characterizing 3D Shapes Using Fractal Dimension

Eler

Minghim

Bruno

et al. 2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Developments in techniques for modeling and digitizing have made the use of 3D models popular to a large number of new applications. With the diffusion and spreading of 3D models employment, the demand for efficient search and retrieval methods is high. Researchers have dedicated effort to investigate and overcome the problem of 3D shape retrieval. In this work, we propose a new way to employ shape complexity analysis methods, such as the fractal dimension, to perform the 3D shape characterization for those purposes. This approach is described and experimental results are performed on a 3D models data set. We also compare the technique to two other known methods for 3D model description, reported in literature, namely shape histograms and shape distributions. The technique presented here has performed considerably better than any of the others in the experiments.

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“…[5][6][7] Most of these methods employ the fractal dimension direct or indirectly for the representation of the texture. In recent years, however, a family of methods 3,[8][9][10] was developed through extracting a set of features from the fractal modelling, unlike the fractal dimension which is only a unique number. Generically speaking, these methods provide the called fractal descriptors, capable of representing a texture with a higher degree of richness than the simple fractal dimension.…”

Section: Introductionmentioning

confidence: 99%

“…This is an important problem in computational vision. Bruno et al 3 applied the technique to a shape recognition problem. Here, we propose the development and study of a novel fractal-based method for the analysis of color texture.…”

mentioning

confidence: 99%

Fractal descriptors in the Fourier domain applied to color texture analysis

Florindo¹,

Bruno²

2011

Chaos

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A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data Rev. Sci. Instrum. 82, 015105 (2011) Support vector machine-based feature extractor for L/H transitions in JET Rev. Sci. Instrum. 81, 10E123 (2010) Determining the absorption tolerance of single chromophore photodiodes for machine vision Appl. Phys. Lett. 96, 253303 (2010) Determining the absorption tolerance of single chromophore photodiodes for machine vision APL: Org. Electron. Photonics 3, 123 (2010) The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists of two steps. First, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. Second, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors that are used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach. Fractal objects constitute a particular category of nonlinear dynamic system. Essentially, fractals are geometric objects which do not obey the classical Euclidian rules. Besides, they are characterized by the self-similarity, that is, we observe some geometrical and/or statistical patterns which repeat themselves along different observation scales. In a true fractal, the self-similarity is observed at infinite observation scales. The inherent composition rules of fractals yield to some interesting characteristics. For instance, fractals present non-proportionality between cause and effect. Still they present infinite complexity (complexity in the sense of observed details) and they show a high dependence level from initial conditions. Such characteristics allow for fractals are also to be considered as a chaotic system. This physical interpretation collaborated for authors, such as Mandelbrot, 1 to suggest the use of fractals to model objects found in the nature. More recently, Manoel et al. 2 developed a method for using fractal theory in the extraction of features from natural objects represented in a digital image. This is an important problem in computational vision. Bruno et al. 3 applied the technique to a shape recognition problem. Here, we propose the development and study of a novel fractal-based method for the analysis of color texture. We verify the efficiency of the proposed approach in the solution of a color texture classification problem. Additional information on Chaos

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Fractal dimension applied to plant identification

Cited by 210 publications

References 24 publications

Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

Characterizing 3D Shapes Using Fractal Dimension

Fractal descriptors in the Fourier domain applied to color texture analysis

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