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

A fiber-guided motorised rotation laser scanning thermography technique for impact damage crack inspection in composites

Laser Thermography manifests superior sensitivity and compatibility to detect cracks and small subsurface defects. However, the existing related systems have limitations on either inspection efficiency or unknown directional cracks due to the utilization of stationary heat sources. This article reports a Fiber-guided Motorised Rotation Laser-line Scanning Thermography (FMRLST) system aiming to rapidly inspect cracks of impact damage with unknown direction in composite laminates. An optical head with fibre delivery integrated with a rotation motor is designed and developed to generate novel scanning heating in a circumferential rotation manner. A FEM model is first proposed to simulate the principle of FMRLST testing and produce thermograms for the development of post-processing methods. A damage enhancement method based on Curvelet Transform is developed to enhance the visualization of thermal features of cracks, and purify the resulting image by suppressing the laser-line heating pattern and cancelling noise. The validation on three composite specimens with different levels of impact damage suggests the developed FMRLST system can extract unknown impact surface cracks efficiently. The remarkable sensitivity and flexibility of FMRLST to arbitrary cracks, along with the miniaturized probe-like inspection unit, present its potential in on-site thermographic inspection, and its design is promising to push the LST towards

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