The Ellipticity of the Disks of Spiral Galaxies

The disks of spiral galaxies are generally elliptical rather than circular. The distribution of ellipticities can be fit with a log-normal distribution. For a sample of 12,764 galaxies from the Sloan Digital Sky Survey Data Release 1 (SDSS DR1), the distribution of apparent axis ratios in the i band is best fit by a log-normal distribution of intrinsic ellipticities with ln epsilon = -1.85 +/- 0.89. For a sample of nearly face-on spiral galaxies, analyzed by Andersen and Bershady using both photometric and spectroscopic data, the best fitting distribution of ellipticities has ln epsilon = -2.29 +/- 1.04. Given the small size of the Andersen-Bershady sample, the two distribution are not necessarily inconsistent. If the ellipticity of the potential were equal to that of the light distribution of the SDSS DR1 galaxies, it would produce 1.0 magnitudes of scatter in the Tully-Fisher relation, greater than is observed. The Andersen-Bershady results, however, are consistent with a scatter as small as 0.25 magnitudes in the Tully-Fisher relation.Comment: 19 pages, 5 figures; ApJ, accepte

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

Reconstructing vectorised photographic images

We address the problem of representing captured images in the continuous mathematical space more usually associated with certain forms of drawn ('vector') images. Such an image is resolution-independent so can be used as a master for varying resolution-specific formats. We briefly describe the main features of a vectorising codec for photographic images, whose significance is that drawing programs can access images and image components as first-class vector objects. This paper focuses on the problem of rendering from the isochromic contour form of a vectorised image and demonstrates a new fill algorithm which could also be used in drawing generally. The fill method is described in terms of level set diffusion equations for clarity. Finally we show that image warping is both simplified and enhanced in this form and that we can demonstrate real histogram equalisation with genuinely rectangular histograms

Flame Detection for Video-based Early Fire Warning Systems and 3D Visualization of Fire Propagation

Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.The original article was published by ACTAPRESS and is available here: http://www.actapress.com/Content_of_Proceeding.aspx?proceedingid=73

Image and Volume Segmentation by Water Flow

A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624