Histogram of Oriented Principal Components for Cross-View Action Recognition

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The Experimental results show that our techniques provide significant improvement over state-of-the-art methods

Three-dimensional decomposition of galaxies with bulge and long bar

Some observations indicate that the Milky Way has two inner components, a bulge and a long bar, which present a misalignment of about 20 degrees that is against the predictions of some theoretical models that are based on numerical simulations. In this paper, we wish to determine whether this misalignment between the bar and the bulge can be observed in barred galaxies other than the Milky Way. For that, each galaxy of our sample was decomposed based on its Ks-band 2MASS image by fitting and modelling in a three-dimensional (3D) space the following components: a disc, a bar, and a bulge. The chi-square goodness-of-fit estimation allowed retrieving the best-fit angle values for the bar and the bulge to detect any misalignment. From the 3D decomposition of six barred galaxies, we have detected at least three galaxies (NGC 2217, NGC 3992, and NGC 4593) that present a significant misalignment between the bar and the bulge of more than 20 degrees.Comment: 8 pages, 1 figure. Accepted for publication in A&A. Corrected typo

Hand gesture recognition with jointly calibrated Leap Motion and depth sensor

Novel 3D acquisition devices like depth cameras and the Leap Motion have recently reached the market. Depth cameras allow to obtain a complete 3D description of the framed scene while the Leap Motion sensor is a device explicitly targeted for hand gesture recognition and provides only a limited set of relevant points. This paper shows how to jointly exploit the two types of sensors for accurate gesture recognition. An ad-hoc solution for the joint calibration of the two devices is firstly presented. Then a set of novel feature descriptors is introduced both for the Leap Motion and for depth data. Various schemes based on the distances of the hand samples from the centroid, on the curvature of the hand contour and on the convex hull of the hand shape are employed and the use of Leap Motion data to aid feature extraction is also considered. The proposed feature sets are fed to two different classifiers, one based on multi-class SVMs and one exploiting Random Forests. Different feature selection algorithms have also been tested in order to reduce the complexity of the approach. Experimental results show that a very high accuracy can be obtained from the proposed method. The current implementation is also able to run in real-time

Assembly Bias and Splashback in Galaxy Clusters

We use publicly available data for the Millennium Simulation to explore the implications of the recent detection of assembly bias and splashback signatures in a large sample of galaxy clusters. These were identified in the SDSS/DR8 photometric data by the redMaPPer algorithm and split into high- and low-concentration subsamples based on the projected positions of cluster members. We use simplified versions of these procedures to build cluster samples of similar size from the simulation data. These match the observed samples quite well and show similar assembly bias and splashback signals. Previous theoretical work has found the logarithmic slope of halo density profiles to have a well-defined minimum whose depth decreases and whose radius increases with halo concentration. Projected profiles for the observed and simulated cluster samples show trends with concentration which are opposite to these predictions. In addition, for high-concentration clusters the minimum slope occurs at significantly smaller radius than predicted. We show that these discrepancies all reflect confusion between splashback features and features imposed on the profiles by the cluster identification and concentration estimation procedures. The strong apparent assembly bias is not reflected in the three-dimensional distribution of matter around clusters. Rather it is a consequence of the preferential contamination of low-concentration clusters by foreground or background groups.Comment: 17 pages, 16 figures, 3 tables, accepted versio

What is the orientation of the tip in a scanning tunneling microscope?

We introduce a statistical correlation analysis method to obtain information on the local geometry and orientation of the tip used in scanning tunneling microscopy (STM) experiments based on large scale simulations. The key quantity is the relative brightness correlation of constant-current topographs between experimental and simulated data. This correlation can be analyzed statistically for a large number of modeled tip orientations and geometries. Assuming a stable tip during the STM scans and based on the correlation distribution, it is possible to determine the tip orientations that are most likely present in an STM experiment, and exclude other orientations. This is especially important for substrates such as highly oriented pyrolytic graphite (HOPG) since its STM contrast is strongly tip dependent, which makes interpretation and comparison of STM images very challenging. We illustrate the applicability of our method considering the HOPG surface in combination with tungsten tip models of two different apex geometries and 18144 different orientations. We calculate constant-current profiles along the $$ direction of the HOPG(0001) surface in the $|V|\le 1$ V bias voltage range, and compare them with experimental data. We find that a blunt tip model provides better correlation with the experiment for a wider range of tip orientations and bias voltages than a sharp tip model. Such a combination of experiments and large scale simulations opens up the way for obtaining more detailed information on the structure of the tip apex and more reliable interpretation of STM data in the view of local tip geometry effects.Comment: Progress in Surface Science, accepted for publication, 25 pages manuscript, 9 figures, abstract shortene

Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201

Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

Registration of Standardized Histological Images in Feature Space

In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the similar intensities correspond to similar tissues meaning. Second, 2D histological images are mapped into a feature space where continuous variables are used as high confidence image features for accurate registration. Third, we propose an automatic best reference slice selection algorithm that improves reconstruction quality based on both image entropy and mean square error of the registration process. We demonstrate that the choice of reference slice has a significant impact on registration error, standardization, feature space and entropy information. After 2D histological slices are registered through an affine transformation with respect to an automatically chosen reference, the 3D volume is reconstructed by co-registering 2D slices elastically.Comment: SPIE Medical Imaging 2008 - submissio