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

Casanova, Dalcimar; Florindo, João Batista; Falvo, Maurício; Bruno, Odemir Martinez

doi:10.1016/j.ins.2016.01.077

Cited by 28 publications

(18 citation statements)

References 34 publications

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“…Since fractal theory, feature and descriptor are widely applied in image processing, content analysis and other research domains [6,13,21], our research of generalized Mandelbrot set in this paper, which is the base of fractal theory, is meaningful for fractal applications.…”

Section: Resultsmentioning

confidence: 99%

Fractal generation method based on asymptote family of generalized Mandelbrot set and its application

Liu¹,

Zheng²,

Fu³

et al. 2017

J. Nonlinear Sci. Appl.

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Generalized Mandelbrot set (k-M set) is the basis of fractal analysis. This paper presents a novel method to generate k-M set, which generates k-M set precisely by constructing its asymptote family. Correctness of the proposed method is proved as well as computational complexity is researched. Further, application of the generation method is studied, which is used to analyze distribution of boundary points and periodic points of k-M set. Finally, experiments have been implemented to evaluate the theoretical results.

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Section: Resultsmentioning

confidence: 99%

Fractal generation method based on asymptote family of generalized Mandelbrot set and its application

Liu¹,

Zheng²,

Fu³

et al. 2017

J. Nonlinear Sci. Appl.

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“…Monochromatic and opponent channels are obtained, calculated from the output of Gabor filters. More recently, some methods based on image complexity focused on the analysis of within-between channel aspects for color texture with fractal geometry [8] and CNs [30,44]. the vertex degree, which is the sum of its connections.…”

Section: Color Vision and Color Texturementioning

confidence: 99%

Spatio-spectral networks for color-texture analysis

Scabini

Ribas

Bruno

2020

Information Sciences

Self Cite

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Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this context, this work focus on a new method for color texture analysis considering all color channels in a more intrinsic approach. Our proposal consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel edges that cover spatial patterns throughout individual image color channels, while between-channel edges tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through a concise topological characterization of the modeled network in a multiscale approach with radially symmetric neighborhoods. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks, and also has the most stable performance between datasets, achieving 98.5(±1.1) of average accuracy against 97.1(±1.3) of MCND and 96.8(±3.2) of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, where results show that SSN also has higher performance and robustness.Keywords color-texture · texture analysis · feature extraction · complex networks · spatio-spectral networks Complex NetworksThe researches in CNs arise from the combination of graph theory, physics, and statistics, with the aim of analyzing large networks that derive from complex natural processes. Initially, works have shown that there are structural patterns in most of these networks, something that is not expected in a random network. This led to the definition of CN models that allow us to understand the structural properties of real networks. The most popular models are the scale-free [5] and small-world [52] networks. Therefore, a new line of research has been opened for pattern recognition, where CNs are adopted as a tool for modeling and characterizing natural phenomena.The concepts of CN are applied in several areas such as physics, biology, nanotechnology, neuroscience, sociology, among others [13]. Applying CNs to some problem consists of two main steps: i) modeling the problem as a network; ii) structural analysis of the resulting CN. The topological quantification of the CN allows us to arrive at important conclusions related to the system that it represents. For example, local vertex measurements can highlight important network regions, estimate their vulnerability, find groups of similar vertices, and etc.

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“…With the advent of consumer available depth acquisition hardware [25] and laser 70 scanning systems such as LiDAR, research in scene analysis in the 3D domain has grown considerably [8,35,54].…”

Section: Scene Analysis and Risk Assessmentmentioning

confidence: 99%

“…Mass is defined by calculating the number of voxels within an object cluster and using the assumption that all objects are made from the same material. However through the use of material estimation (such as BRDF function estimation [53,29] or techniques such as visual vibrometry [13] as well as others [8,56]),…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Risk analysis for smart homes and domestic robots using robust shape and physics descriptors, and complex boosting techniques

Dupre

Argyriou

Tzimiropoulos

et al. 2016

Information Sciences

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Please cite this article as: Rob Dupre, Vasileios Argyriou, George Tzimiropoulos, Darrel Greenhill, Risk analysis for smart homes and domestic robots using robust shape and physics descriptors, and complex boosting techniques, Information Sciences (2016Sciences ( ), doi: 10.1016Sciences ( /j.ins.2016 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T AbstractIn this paper, the notion of risk analysis within 3D scenes using vision based techniques is introduced. In particular the problem of risk estimation of indoor environments at the scene and object level is considered, with applications in domestic robots and smart homes. To this end, the proposed Risk Estimation Framework is described, which provides a quantified risk score for a given scene.This methodology is extended with the introduction of a novel robust kernel for 3D shape descriptors such as 3D HOG and SIFT3D, which aims to reduce the effects of outliers in the proposed risk recognition methodology. The Physics Behaviour Feature (PBF) is presented, which uses an object's angular velocity obtained using Newtonian physics simulation as a descriptor. Furthermore, an extension of boosting techniques for learning is suggested in the form of the novel Complex and Hyper-Complex Adaboost, which greatly increase the computation efficiency of the original technique. In order to evaluate the proposed robust descriptors an enriched version of the 3D Risk Scenes (3DRS) dataset with extra objects, scenes and meta-data was utilised. A comparative study was conducted demonstrating that the suggested approach outperforms current state-of-the-art descriptors.

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

Cited by 28 publications

References 34 publications

Fractal generation method based on asymptote family of generalized Mandelbrot set and its application

Fractal generation method based on asymptote family of generalized Mandelbrot set and its application

Spatio-spectral networks for color-texture analysis

Risk analysis for smart homes and domestic robots using robust shape and physics descriptors, and complex boosting techniques

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