Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface.Comment: Accepted in Medical Image Analysi

Disconnected Skeleton: Shape at its Absolute Scale

We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on the stable properties of the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of the skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV: Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape Recognition. Masters thesis, Department of Computer Engineering, Middle East Technical University, May 200

From Weak Lensing to non-Gaussianity via Minkowski Functionals

We present a new harmonic-domain approach for extracting morphological information, in the form of Minkowski Functionals (MFs), from weak lensing (WL) convergence maps. Using a perturbative expansion of the MFs, which is expected to be valid for the range of angular scales probed by most current weak-lensing surveys, we show that the study of three generalized skewness parameters is equivalent to the study of the three MFs defined in two dimensions. We then extend these skewness parameters to three associated skew-spectra which carry more information about the convergence bispectrum than their one-point counterparts. We discuss various issues such as noise and incomplete sky coverage in the context of estimation of these skew-spectra from realistic data. Our technique provides an alternative to the pixel-space approaches typically used in the estimation of MFs, and it can be particularly useful in the presence of masks with non-trivial topology. Analytical modeling of weak lensing statistics relies on an accurate modeling of the statistics of underlying density distribution. We apply three different formalisms to model the underlying dark-matter bispectrum: the hierarchical ansatz, halo model and a fitting function based on numerical simulations; MFs resulting from each of these formalisms are computed and compared. We investigate the extent to witch late-time gravity-induced non-Gaussianity (to which weak lensing is primarily sensitive) can be separated from primordial non-Gaussianity and how this separation depends on source redshift and angular scale.Comment: 22 Pages, 12 Figures. Submitting To MNRA

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Young and middle age pulsar light-curve morphology: Comparison of Fermi observations with gamma-ray and radio emission geometries

Thanks to the huge amount of gamma-ray pulsar photons collected by the Fermi Large Area Telescope since June 2008, it is now possible to constrain gamma-ray geometrical models by comparing simulated and observed light-curve morphological characteristics. We assumed vacuum-retarded dipole pulsar magnetic field and tested simulated and observed morphological light-curve characteristics in the framework of two pole emission geometries, Polar Cap (PC), radio, and Slot Gap (SG), and Outer Gap (OG)/One Pole Caustic (OPC) emission geometries. We compared simulated and observed/estimated light-curve morphological parameters as a function of observable and non-observable pulsar parameters. The PC model gives the poorest description of the LAT pulsar light-curve morphology. The OPC best explains both the observed gamma-ray peak multiplicity and shape classes. The OPC and SG models describe the observed gamma-ray peak-separation distribution for low- and high-peak separations, respectively. This suggests that the OPC geometry best explains the single-peak structure but does not manage to describe the widely separated peaks predicted in the framework of the SG model as the emission from the two magnetic hemispheres. The OPC radio-lag distribution shows higher agreement with observations suggesting that assuming polar radio emission, the gamma-ray emission regions are likely to be located in the outer magnetosphere. The larger agreement between simulated and LAT estimations in the framework of the OPC suggests that the OPC model best predicts the observed variety of profile shapes. The larger agreement between observations and the OPC model jointly with the need to explain the abundant 0.5 separated peaks with two-pole emission geometries, calls for thin OPC gaps to explain the single-peak geometry but highlights the need of two-pole caustic emission geometry to explain widely separated peaks.Comment: 28 pages, 20 figures, 8 tables; accepted for publication in Astronomy and Astrophysic