Statistical Analysis of Dynamic Actions

Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents

Laser Surface Texturing of TiAl Multilayer Films—Effects of Microstructure and Topography on Friction and Wear

Laser surface texturing is an efficient way to control the friction and wear properties of materials. Although described in many papers, most previous work relates to a pure topographic view of laser-textured surfaces. As lasers are heat sources, their thermal impact during treatment can be high enough to modify the material’s microstructure or surface chemistry and affect tribological properties as well. This research took a closer look at the microstructure of laser-textured TiAl multilayers, besides topographic aspects. Direct laser interference patterning was used to create well-defined line-like surface textures in TiAl multilayers with differing lateral feature sizes in the micron range. High-resolution techniques such as TEM and XRD highlighted the effect of this method on microstructure, and in particular, the phase situation of the TiAl multilayer. Thermal simulations demonstrated that the maximum achievable temperatures were around 2000 K, thus being high enough to melt Ti and Al. Cooling rates on the order of 109 K/s depended on the lateral feature size, potentially leading to metastable microstructures. Finally, ball-on-disk tests on as-textured TiAl specimens showed a reduction in wear under dry conditions depending on the periodicity of the line-like textures used

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label

Hybrid chiral domain walls and skyrmions in magnetic multilayers

Noncollinear spin textures in ferromagnetic ultrathin films are currently the subject of renewed interest since the discovery of the interfacial Dzyaloshinskii-Moriya interaction (DMI). This antisymmetric exchange interaction selects a given chirality for the spin textures and allows stabilising configurations with nontrivial topology. Moreover, it has many crucial consequences on the dynamical properties of these topological structures, including chiral domain walls (DWs) and magnetic skyrmions. In the recent years the study of noncollinear spin textures has been extended from single ultrathin layers to magnetic multilayers with broken inversion symmetry. This extension of the structures in the vertical dimension allows very efficient current-induced motion and room-temperature stability for both N\'eel DWs and skyrmions. Here we show how in such multilayered systems the interlayer interactions can actually lead to more complex, hybrid chiral magnetisation arrangements. The described thickness-dependent reorientation of DWs is experimentally confirmed by studying demagnetised multilayers through circular dichroism in x-ray resonant magnetic scattering. We also demonstrate a simple yet reliable method for determining the magnitude of the DMI from static domains measurements even in the presence of these hybrid chiral structures, by taking into account the actual profile of the DWs. The advent of these novel hybrid chiral textures has far-reaching implications on how to stabilise and manipulate DWs as well as skymionic structures in magnetic multilayers.Comment: 22 pages, 5 figure

Similarity Measures for Automatic Defect Detection on Patterned Textures

Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure