A complex network approach for nanoparticle agglomeration analysis in nanoscale images

Machado, Bruno Brandoli; Scabini, Leonardo F. S.; Oruê, Jonatan Patrick Margarido; Arruda, Mauro Santos de; Gonçalves, Diogo Nunes; Gonçalves, Wesley Nunes; Moreira, Raphaell; Rodrigues-Jr, José F.

doi:10.1007/s11051-017-3760-7

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…We can observe the application of CN concepts in various areas such as physics [42], nanotechnology [43], neuroscience [44], and many more [45]. This is an emerging science which is gaining strength due to big data and recent faster computer hardware, that enables the processing of larger amounts of data.…”

Section: Complex Networkmentioning

confidence: 99%

Multilayer complex network descriptors for color–texture characterization

Scabini

Condori

Gonçalves

et al. 2019

Information Sciences

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A new method based on complex networks is proposed for color-texture analysis. The proposal consists on modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT. Results among various literature methods are compared, including deep convolutional neural networks with pre-trained architectures. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers. and its results, comparisons with literature methods and discussions; finally, on Section 5 we discuss the main findings and results of the work. Theoretical Concepts and ReviewOn this section, we present the theoretical concepts of color-texture and CN. Color-Texture AnalysisThe goal of texture analysis is to study how to measure texture aspects and employ it to image characterization. Although this approach has been explored for many years, most works approach gray-level images, i.e. a single color-channel representing the pixel luminance. On this case, the multispectral information is either discarded or is not present. However, nowadays images that derive from different sources are mostly colored, for instance from the internet, surveillance cameras, satellites, microscopes, personal cameras and much more. The recent increase in computer hardware performance also makes possible to analyze larger amounts of data, allowing to keep the color information for texture analysis.In general, color-texture methods found in the literature are mostly integrative, which separate color from texture. These methods usually compute traditional gray-level descriptors from each color-channel, separately. For that, various gray-level methods can be applied or combined into an integrative descriptor. There are many graylevel methods found in literature. Classical techniques can be divided into statistical, model-based and structural methods [15]. The statistical methods were one of the first approaches which considered texture as a property to characterize images. Among statistical methods, the most common ones are those based on gray-level co-occurrence matrices [16,17] and Local Binary Patterns (LBP) [18]. Model-based methods include descriptors such as Gabor filters [19], which explore texture in the frequency domain, and Markov random field models [20]. Structural methods consists on analyzing the texture as a combination of various smaller elements, that are spatially arranged to compose the overall texture pattern. This is achieved, for...

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Section: Complex Networkmentioning

confidence: 99%

Multilayer complex network descriptors for color–texture characterization

Scabini

Condori

Gonçalves

et al. 2019

Information Sciences

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“…Then, the nodes are divided into unit grids [14], and a set of nodes are defined based on the number of pixel in each grid. In the second part, the similarity [15] between nodes is calculated based on the node coordinate information. Each node is mapped into a vertex of the complex network.…”

Section: Segmentation With Complex Network Methodsmentioning

confidence: 99%

Material Image Segmentation with the Machine Learning Method and Complex Network Method

et al. 2019

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The study of the relationship among the manufacturing process, the structure and the property of materials can help to develop the new materials. The material images contain the microstructures of materials, therefore, the quantitative analysis for the material images is the important means to study the characteristics of material structures. Generally, the quantitative analysis for the material microstructures is based on the exact segmentation of the materials images. However, most material microstructures are shown with various shapes and complex textures in images, and they seriously hinder the exact segmentation of the component elements. In this research, machine learning method and complex networks method are adopted to the challenge of automatic material image segmentation. Two segmentation tasks are completed: on the one hand, the images of the titanium alloy are segmented based on the pixel-level classification through feature extraction and machine learning algorithm; on the other hand, the ceramic images are segmented with the complex

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“…Two particles are linked, defining and edge, only if their distance is smaller than a given radius and its density is higher than a given threshold. This work was published in the Journal of Nanoparticle Research [21]. Furthermore, this work was integrated into an expert system (see Figure 4), named NanoImage Analyzer, and it was submitted for software registration also at INPI.…”

Section: Conclusãomentioning

confidence: 99%

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

Machado¹,

Rodrigues²

2017

Anais Do Concurso De Teses E Dissertações Da SBC (CTD-SBC)

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Texture is one of the primary visual features used to computationally describe the patterns found in nature. Existing computational methods, however, do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing images by benefiting from the complex systems properties that are characteristic to textures. To do so, we created approaches based on the Bouligand-Minkowski fractal dimension, swarm-system Artificial Crawlers, and non-linear diffusion of Perona-Malik, techniques that led to methodologies with efficacy and efficiency comparable to the state-ofthe-art. The results achieved in the four methodologies described in this work demonstrated the validity and the potential of our hypothesis in tasks of pattern recognition. The contributions of our methodologies shall support advances in materials engineering, computer vision, and agriculture.

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A complex network approach for nanoparticle agglomeration analysis in nanoscale images

Cited by 7 publications

References 38 publications

Multilayer complex network descriptors for color–texture characterization

Multilayer complex network descriptors for color–texture characterization

Material Image Segmentation with the Machine Learning Method and Complex Network Method

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

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