A complex network approach for dynamic texture recognition

Gonçalves, Wesley Nunes; Machado, Bruno Brandoli; Bruno, Odemir Martinez

doi:10.1016/j.neucom.2014.11.034

Cited by 39 publications

(38 citation statements)

References 30 publications

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“…the oriented energy representations by Wildes and Bergen [11] and Derpanis and Wildes [12], where the latter represents pure dynamics of spatio-temporal textures by capturing space-time orientation by means of 3D Gaussian derivative filters. Gonçalves et al [13] instead jointly model appearance and dynamics using spatio-temporal Gabor filters with different preferred spatial orientations and speeds. Neither of these approaches utilise joint statistics of filter responses.…”

Section: Related Workmentioning

confidence: 99%

“…Computations are separable in all dimensions and performed frame by frame, utilising recursive smoothing along the temporal dimension. In contrast to previous methods utilising spatio-temporal filtering, such as [12,13], our method includes a diverse group of partial derivatives from the spatio-temporal N -jet as opposed to a single filter type.…”

Section: Receptive Field Responsesmentioning

confidence: 99%

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Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters

Jansson

Lindeberg

2017

Lecture Notes in Computer Science

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Abstract. This work presents an evaluation of using time-causal scalespace filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatiotemporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain. We evaluate one member in this family, constituting a joint binary histogram, on two widely used dynamic texture databases. The experimental evaluation shows competitive performance compared to previous methods for dynamic texture recognition, especially on the more complex DynTex database. These results support the descriptive power of time-causal spatio-temporal scale-space filters as primitives for video analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Receptive Field Responsesmentioning

confidence: 99%

Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters

Jansson

Lindeberg

2017

Lecture Notes in Computer Science

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“…Complex networks have become an important topic in science due to their ability to model a large number of complex systems such as interaction in society [14], processes in biology as protein interaction [1], financial markets [19], computer vision [17], and physics [16]. In computer science, complex networks have been used to understand the topology and dynamics of the Internet [36], characterization of social networks [20], text summarization [2], aspects of scientific co-authorship [29], and citation networks [32].…”

Section: Related Work On Complex Networkmentioning

confidence: 99%

A complex network approach for nanoparticle agglomeration analysis in nanoscale images

et al. 2017

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Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticles images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outstanding agglomerates in a nanoparticle image. Experimental results using images of Scanning Tunneling Microscopy (STM) of gold nanoparticles demonstrated the effectiveness of the pro- The results also demonstrated efficacy for both convex and non-convex agglomerates.

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“…The research of complex network lies at the intersection between graph theory and statistical mechanics. The problem is represented as a complex network followed by the analysis of its topological features obtained by a set of measurements (Gonçalves et al, 2014). The degree descriptors of complex network are rotation invariant and scale invariant.…”

Section: Introductionmentioning

confidence: 99%

Complex networks-based texture extraction and classification method for mineral flotation froth images

Xiao

Xie

et al. 2015

Minerals Engineering

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A complex network approach for dynamic texture recognition

Cited by 39 publications

References 30 publications

Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters

Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters

A complex network approach for nanoparticle agglomeration analysis in nanoscale images

Complex networks-based texture extraction and classification method for mineral flotation froth images

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