Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification

We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by decision tree based methods, which enables the visualization of the classification categories. Secondly, we investigate how the Mutual Information based Transductive Feature Selection (MINT) algorithm can be used to perform feature pre-selection. If one would like to provide only a small number of input features to a machine learning classification algorithm, feature pre-selection provides a method to determine which of the many possible input properties should be selected. Third is the use of the tree-interpreter package to enable popular decision tree based ensemble methods to be opened, visualized, and understood. This is done by additional analysis of the tree based model, determining not only which features are important to the model, but how important a feature is for a particular classification given its value. Lastly, we use decision boundaries from the model to revise an already existing method of classification, essentially asking the tree based method where decision boundaries are best placed and defining a new classification method. We showcase these techniques by applying them to the problem of star-galaxy separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We use the output of MINT and the ensemble methods to demonstrate how more complex decision boundaries improve star-galaxy classification accuracy over the standard SDSS frames approach (reducing misclassifications by up to $\approx33\%$). We then show how tree-interpreter can be used to explore how relevant each photometric feature is when making a classification on an object by object basis.Comment: 12 pages, 8 figures, 8 table

CMBPol Mission Concept Study: Prospects for polarized foreground removal

In this report we discuss the impact of polarized foregrounds on a future CMBPol satellite mission. We review our current knowledge of Galactic polarized emission at microwave frequencies, including synchrotron and thermal dust emission. We use existing data and our understanding of the physical behavior of the sources of foreground emission to generate sky templates, and start to assess how well primordial gravitational wave signals can be separated from foreground contaminants for a CMBPol mission. At the estimated foreground minimum of ~100 GHz, the polarized foregrounds are expected to be lower than a primordial polarization signal with tensor-to-scalar ratio r=0.01, in a small patch (~1%) of the sky known to have low Galactic emission. Over 75% of the sky we expect the foreground amplitude to exceed the primordial signal by about a factor of eight at the foreground minimum and on scales of two degrees. Only on the largest scales does the polarized foreground amplitude exceed the primordial signal by a larger factor of about 20. The prospects for detecting an r=0.01 signal including degree-scale measurements appear promising, with 5 sigma_r ~0.003 forecast from multiple methods. A mission that observes a range of scales offers better prospects from the foregrounds perspective than one targeting only the lowest few multipoles. We begin to explore how optimizing the composition of frequency channels in the focal plane can maximize our ability to perform component separation, with a range of typically 40 < nu < 300 GHz preferred for ten channels. Foreground cleaning methods are already in place to tackle a CMBPol mission data set, and further investigation of the optimization and detectability of the primordial signal will be useful for mission design.Comment: 42 pages, 14 figures, Foreground Removal Working Group contribution to the CMBPol Mission Concept Study, v2, matches AIP versio

Bio-Cryptosystem Using Fuzzy Vault Scheme

— In recent years most challenging problem is protection of information from unauthorized users. The conventional Cryptographic systems are insufficient to provide a security. The main problem is how to protect private keys from attackers and Intruder such as in case of Internet Banking. Cryptographic systems have been widely used in many information security systems. Hence in this paper we have proposed a framework of Biometric based cryptosystems. It provide reliable way of hiding private keys by using biometric features of individuals. A fuzzy vault approach is used to protect private keys and to release them only when legitimate individual enter their biometric sample. The main advantage of this system is there is no need of storing biometric information. However, fuzzy vault systems do not store directly these templates since they are encrypted with private keys by using novel cryptography algorithm. In proposed framework we are combining iris features with the encryption algorithm that can be a new research direction. The proposed approach provides high security and also image information can be protected. DOI: 10.17762/ijritcc2321-8169.150712

Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

Presented is a description of a Markov chain Monte Carlo (MCMC) parameter estimation routine for use with interferometric gravitational radiational data in searches for binary neutron star inspiral signals. Five parameters associated with the inspiral can be estimated, and summary statistics are produced. Advanced MCMC methods were implemented, including importance resampling and prior distributions based on detection probability, in order to increase the efficiency of the code. An example is presented from an application using realistic, albeit fictitious, data.Comment: submitted to Classical and Quantum Gravity. 14 pages, 5 figure