Flexible shape extraction for micro/nano scale structured surfaces.

Surface feature is the one of the most important factors affecting the functionality and reliability of micro scale patterned surfaces. For micro scale patterned surface characterisation, it’s important to extract the surface feature effectively and accurately. The active contours, known as “snakes”, have been successfully used to segment, match and track the objects of interest. The active contours have been applied to facial boundary detection, medical image processing, motion correction, etc. In this paper, surface feature extraction techniques based on active contours have been investigated. Parametric active contour models and geometric active contour models have been presented. Also, a group of examples has been selected here to demonstrate the feasibility and applicability of the surface pattern extraction techniques based on active contours. At last, experimental results will be given and discussed

Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters

We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both the spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hours before strong solar flare events

Feature-based Lucas-Kanade and Active Appearance Models

Lucas-Kanade and Active Appearance Models are among the most commonly used methods for image alignment and facial fitting, respectively. They both utilize non-linear gradient descent, which is usually applied on intensity values. In this paper, we propose the employment of highly-descriptive, densely-sampled image features for both problems. We show that the strategy of warping the multi-channel dense feature image at each iteration is more beneficial than extracting features after warping the intensity image at each iteration. Motivated by this observation, we demonstrate robust and accurate alignment and fitting performance using a variety of powerful feature descriptors. Especially with the employment of HOG and SIFT features, our method significantly outperforms the current state-of-the-art results on in-the-wild databases

AGN Dusty Tori as a Clumpy Two-Phase Medium: The 10 Micron Silicate Feature

We investigated the emission of active galactic nuclei dusty tori in the infrared domain, with a focus on the 10 micron silicate feature. We modeled the dusty torus as a clumpy two-phase medium with high-density clumps and a low-density medium filling the space between the clumps. We employed a three-dimensional radiative transfer code to obtain spectral energy distributions and images of tori at different wavelengths. We calculated a grid of models for different parameters and analyzed the influence of these parameters on the shape of the mid-infrared emission. A corresponding set of clumps-only models and models with a smooth dust distribution is calculated for comparison. We found that the dust distribution, the optical depth and a random arrangement of clumps in the innermost region, all have an impact on the shape and strength of the silicate feature. The 10 micron silicate feature can be suppressed for some parameters, but models with smooth dust distribution are also able to produce a wide range of the silicate feature strength.Comment: 5 pages, 2 figures. Proceedings of the "8th Serbian Conference on Spectral Line Shapes in Astrophysics", Divcibare, Serbia, June 6-10 2011. Model SEDs available for download at https://sites.google.com/site/skirtorus

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

Quantum-Gravity Decoherence Effects in Neutrino Oscillations: Expected Constraints from CNGS and J-PARC

Quantum decoherence, the evolution of pure states into mixed states, may be a feature of quantum-gravity models. In most cases, such models lead to fewer neutrinos of all active flavours being detected in a long baseline experiment as compared to three-flavour standard neutrino oscillations. We discuss the potential of the CNGS and J-PARC beams in constraining models of quantum-gravity induced decoherence using neutrino oscillations as a probe. We use as much as possible model-independent parameterizations, even though they are motivated by specific microscopic models, for fits to the expected experimental data which yield bounds on quantum-gravity decoherence parameters.Comment: 40 pages, 8 figures, minor correction