Hierarchical search strategy for the detection of gravitational waves from coalescing binaries: Extension to post-Newtonian wave forms

The detection of gravitational waves from coalescing compact binaries would be a computationally intensive process if a single bank of template wave forms (i.e., a one step search) is used. In an earlier paper we had presented a detection strategy, called a two step search}, that utilizes a hierarchy of template banks. It was shown that in the simple case of a family of Newtonian signals, an on-line two step search was about 8 times faster than an on-line one step search (for initial LIGO). In this paper we extend the two step search to the more realistic case of zero spin 1.5 post-Newtonian wave forms. We also present formulas for detection and false alarm probabilities which take statistical correlations into account. We find that for the case of a 1.5 post-Newtonian family of templates and signals, an on-line two step search requires about 1/21 the computing power that would be required for the corresponding on-line one step search. This reduction is achieved when signals having strength S = 10.34 are required to be detected with a probability of 0.95, at an average of one false event per year, and the noise power spectral density used is that of advanced LIGO. For initial LIGO, the reduction achieved in computing power is about 1/27 for S = 9.98 and the same probabilities for detection and false alarm as above.Comment: 30 page RevTeX file and 17 figures (postscript). Submitted to PRD Feb 21, 199

Learning shape correspondence with anisotropic convolutional neural networks

Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications. The problem is especially difficult in the setting of non-isometric deformations, as well as in the presence of topological noise and missing parts, mainly due to the limited capability to model such deformations axiomatically. Several recent works showed that invariance to complex shape transformations can be learned from examples. In this paper, we introduce an intrinsic convolutional neural network architecture based on anisotropic diffusion kernels, which we term Anisotropic Convolutional Neural Network (ACNN). In our construction, we generalize convolutions to non-Euclidean domains by constructing a set of oriented anisotropic diffusion kernels, creating in this way a local intrinsic polar representation of the data (`patch'), which is then correlated with a filter. Several cascades of such filters, linear, and non-linear operators are stacked to form a deep neural network whose parameters are learned by minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic dense correspondences between deformable shapes in very challenging settings, achieving state-of-the-art results on some of the most difficult recent correspondence benchmarks

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Multiresolutional Fault-Tolerant Sensor Integration and Object Recognition in Images.

This dissertation applies multiresolution methods to two important problems in signal analysis. The problem of fault-tolerant sensor integration in distributed sensor networks is addressed, and an efficient multiresolutional algorithm for estimating the sensors\u27 effective output is proposed. The problem of object/shape recognition in images is addressed in a multiresolutional setting using pyramidal decomposition of images with respect to an orthonormal wavelet basis. A new approach to efficient template matching to detect objects using computational geometric methods is put forward. An efficient paradigm for object recognition is described

A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation

Intrinsic isometric shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency, i.e., the metric structure of the whole manifold must not change significantly. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise (incomplete data and contacts), which is a common problem in real-world 3D scanner data. In this paper, we introduce a new approach to partial, intrinsic isometric matching. Our method is based on the observation that isometries are fully determined by purely local information: a map of a single point and its tangent space fixes an isometry for both global and the partial maps. From this idea, we develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent spaces. From this, we derive a local propagation algorithm that find such mappings efficiently. In contrast to previous heuristics based on RANSAC or expectation maximization, our method is based on a simple and sound theoretical model and fully deterministic. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure

Power Systems Monitoring and Control using Telecom Network Management Standards

Historically, different solutions have been developed for power systems control and telecommunications network management environments. The former was characterized by proprietary solutions, while the latter has been involved for years in a strong standardization process guided by criteria of openness. Today, power systems control standardization is in progress, but it is at an early stage compared to the telecommunications management area, especially in terms of information modeling. Today, control equipment tends to exhibit more computational power, and communication lines have increased their performance. These trends hint at some conceptual convergence between power systems and telecommunications networks from a management perspective. This convergence leads us to suggest the application of well-established telecommunications management standards for power systems control. This paper shows that this is a real medium-to-long term possibility