A brief introduction to cosmic topology

Whether we live in a spatially finite universe, and what its shape and size may be, are among the fundamental long-standing questions in cosmology. These questions of topological nature have become particularly topical, given the wealth of increasingly accurate astro-cosmological observations, especially the recent observations of the cosmic microwave background radiation. An overview of the basic context of cosmic topology, the detectability constraints from recent observations, as well as the main methods for its detection and some recent results are briefly presented.Comment: 14 pages, 5 figures. Short review of the topics addressed with details in the lectures. To appear in the proc. of the XIth Brazilian School of Cosmology and Gravitation, eds. M.Novelo and S.E. Perez Bergliaffa, American Institute of Physics Conference Proceedings (2005

Memory vectors for similarity search in high-dimensional spaces

We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory. This architecture is composed of several memory units, each of which summarizes a fraction of the database by a single representative vector. The potential similarity of the query to one of the vectors stored in the memory unit is gauged by a simple correlation with the memory unit's representative vector. This representative optimizes the test of the following hypothesis: the query is independent from any vector in the memory unit vs. the query is a simple perturbation of one of the stored vectors. Compared to exhaustive search, our approach finds the most similar database vectors significantly faster without a noticeable reduction in search quality. Interestingly, the reduction of complexity is provably better in high-dimensional spaces. We empirically demonstrate its practical interest in a large-scale image search scenario with off-the-shelf state-of-the-art descriptors.Comment: Accepted to IEEE Transactions on Big Dat

Evaluation of Hashing Methods Performance on Binary Feature Descriptors

In this paper we evaluate performance of data-dependent hashing methods on binary data. The goal is to find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors. A representative sample of recent unsupervised, semi-supervised and supervised hashing methods was experimentally evaluated on large datasets of labelled binary FREAK feature descriptors

View subspaces for indexing and retrieval of 3D models

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications (Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M. Baskurt ISBN: 9780819479198 Date: 2 February 201

Cosmic microwave background constraints on multi-connected spherical spaces

This article describes the Cosmic Microwave Background anisotropies expected in a closed universe with the topology of a lens space L(p,q) and with density parameter Omega_0 close to 1. It provides the first simulated maps for such spaces along with their corresponding power spectra. In spite of our initial expectations that increasing p (and thus decreasing the size of the fundamental domain) should suppress the quadrupole, we found just the opposite: increasing p elevates the relative power of the low multipoles, for reasons that have since become clear. For Omega_0 = 1.02, an informal ``by eye'' examination of the simulated power spectra suggests that $p$ must be less than 15 for consistency with WMAP's data, while geometric considerations imply that matching circles will exist (potentially revealing the multi-connected topology) only if p > 7. These bounds become less stringent for values of Omega_0 closer to 1.Comment: 4 pages, 9 figures, to appear in PR

Simulating Cosmic Microwave Background maps in multi-connected spaces

This article describes the computation of cosmic microwave background anisotropies in a universe with multi-connected spatial sections and focuses on the implementation of the topology in standard CMB computer codes. The key ingredient is the computation of the eigenmodes of the Laplacian with boundary conditions compatible with multi-connected space topology. The correlators of the coefficients of the decomposition of the temperature fluctuation in spherical harmonics are computed and examples are given for spatially flat spaces and one family of spherical spaces, namely the lens spaces. Under the hypothesis of Gaussian initial conditions, these correlators encode all the topological information of the CMB and suffice to simulate CMB maps.Comment: 33 pages, 55 figures, submitted to PRD. Higher resolution figures available on deman