Completed Local Structure Patterns on Three Orthogonal Planes for Dynamic Texture Recognition

International audienceDynamic texture (DT) is a challenging problem in computer vision because of the chaotic motion of textures. We address in this paper a new dynamic texture operator by considering local structure patterns (LSP) and completed local binary patterns (CLBP) for static images in three orthogonal planes to capture spatial-temporal texture structures. Since the typical operator of local binary patterns (LBP), which uses center pixel for thresholding, has some limitations such as sensitivity to noise and near uniform regions, the proposed approach can deal with these drawbacks by using global and local texture information for adaptive thresholding and CLBP for exploiting complementary texture information in three orthogonal planes. Evaluations on different datasets of dynamic textures (UCLA, DynTex, DynTex++) show that our proposal significantly outper-forms recent results in the state-of-the-art approaches

Volumes of Blurred-Invariant Gaussians for Dynamic Texture Classification

International audienceAn effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second , a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions

Encoding/Decoding Capitals of Classical Architectural Orders by Using Fractal Geometry: Establishing Methodology

In most cases, artefacts are differentiated in terms of style they belong to – mainly visually, not mathematically. So, the main research questions of this study are both how to numerically encode stylistic regularities (peculiarities) as geometric indicators of artefacts morphology and how to decode them, namely to identify architectural style those artefacts belong to. Columns, namely their capitals are chosen as the most distinctive elements among artefacts. To elaborate on the validity of the defined principles of the aim-related methodology, a few representatives (capital samples) from each of three fundamental classical architectural orders (Doric, Ionic, and Corinthian) are used. The subject of this Paper is to establish relevant indicators of capital qualification, capital classification, and thus, referred architectural order identification. The verification of those indicators is performed by processing two sets of capitals contours (that belong to the mutually equidistant transverse and equiangular radial section planes) of each of the selected samples (namely digital 3D models). The narrower research aim is to point out that it is possible to encode not only chosen but also any other capital – by using the mentioned indicators of fractal and non-fractal nature (as a control one). The wider research aim refers to a possibility to identify order a concrete fragment of capital belongs to in terms of recognising it computationally (as confidently as possible from the mathematical probability point of view) based on the established research methodology principles. Finally, it is possible to conclude that changes of the analysed indicators trendlines behaviour (expressed by changes of its slope, roughness, continuation, etc.) accurately/precisely describe morphology-wise variations of a form that could point out subject-related stylistic variation, as well. So, this Paper demonstrates not only the fact that architectural orders capitals are true fractal objects, but rather how fractal analysis as a tool can be used to scientifically numerically encode/decode their certain characteristics (fractal features) of single- or multi-scale nature

Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field 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 and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure

Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition

International audienceRepresentation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenging problem in video analysis due to disorientation of motion features. Analyzing DTs to make them "under-standable" plays an important role in different applications of computer vision. In this paper, an efficient approach for DT description is proposed by addressing the following novel concepts. First, beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of Local Vector Pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, we present a new framework, called Directional Dense Trajectory Patterns , which takes advantage of directional beams of dense trajectories along with spatio-temporal features of their motion points in order to construct dense-trajectory-based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e., UCLA, DynTex, and DynTex++) have verified the interest of our proposal