Medical Image Segmentation by Water Flow

We present a new image segmentation technique based on the paradigm of water flow and apply it to medical images. The force field analogy is used to implement the major water flow attributes like water pressure, surface tension and adhesion so that the model achieves topological adaptability and geometrical flexibility. A new snake-like force functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method has been assessed qualitatively and quantitatively, and shows decent detection performance as well as ability to handle noise

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

An Index of Financial Stress for Canada

The authors develop an index of financial stress for the Canadian financial system. Stress is defined as the force exerted on economic agents by uncertainty and changing expectations of loss in financial markets and institutions. It is a continuous variable with a spectrum of values, where extreme values are called financial crises. Information about financial stress is extracted from a wide array of financial variables using several techniques, including factor analysis, econometric benchmarking, and generalized autoregressive conditional heteroscedasticity (GARCH) modelling. An internal Bank of Canada survey is used to condition the choice of variables and to evaluate their ability to reflect the responses to the survey regarding highly stressful financial events. The authors show that alternative measures of financial crises suggested by the literature do not accurately reflect the results of the survey, while several measures developed in this paper do reflect them.Financial institutions; Financial markets

Debt Policy, Corporate Taxes, and Discount Rates

This paper studies the valuation of assets with debt tax shields when debt policy is a general time-dependent function of the asset's unlevered cash flows, value, and history. In a continuous-time setting, it shows that the value of a project's debt tax shield satisfies a partial differential equation, which simplifies to an easily solved ordinary differential equation for most plausible debt policies. A large class of cases exhibits closed-form solutions for the value of a levered asset, the value of its tax shield, and the appropriate tax-adjusted cost of capital for discounting unlevered cash flows.

Transformations of polar Grassmannians preserving certain intersecting relations

Let $\Pi$ be a polar space of rank $n\ge 3$. Denote by ${\mathcal G}_{k}(\Pi)$ the polar Grassmannian formed by singular subspaces of $\Pi$ whose projective dimension is equal to $k$. Suppose that $k$ is an integer not greater than $n-2$ and consider the relation ${\mathfrak R}_{i,j}$, $0\le i\le j\le k+1$ formed by all pairs $(X,Y)\in {\mathcal G}_{k}(\Pi)\times {\mathcal G}_{k}(\Pi)$ such that $\dim_{p}(X^{\perp}\cap Y)=k-i$ and $\dim_{p} (X\cap Y)=k-j$ ($X^{\perp}$ consists of all points of $\Pi$ collinear to every point of $X$). We show that every bijective transformation of ${\mathcal G}_{k}(\Pi)$ preserving ${\mathfrak R}_{1,1}$ is induced by an automorphism of $\Pi$ and the same holds for the relation ${\mathfrak R}_{0,t}$ if $n\ge 2t\ge 4$ and $k=n-t-1$. In the case when $\Pi$ is a finite classical polar space, we establish that the valencies of ${\mathfrak R}_{i,j}$ and ${\mathfrak R}_{i',j'}$ are distinct if $(i,j)\ne (i',j')$.Comment: 13 page

TSO1 functions in cell division during Arabidopsis flower development

We describe an Arabidopsis mutant, tso1, which develops callus-like tissues in place of floral organs. The tso1 floral meristem lacks properly organized three cell layers, and the nuclei of these cells are irregular in size and shape. Further analyses reveal partially formed cell walls and increased DNA ploidy in tso1 floral meristem cells, indicating defects in mitosis and cytokinesis. Our finding that TSO1 is required for organ formation in floral tissues but not in other tissues indicates that TSO1 may encode a floral-specific cell division component, or that TSO1 function is redundant in nonfloral tissues

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

Efficient delay-tolerant particle filtering

This paper proposes a novel framework for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. Within this framework the informativeness of a delayed (out-of-sequence) measurement (OOSM) is estimated using a lightweight procedure and uninformative measurements are immediately discarded. The framework requires the identification of a threshold that separates informative from uninformative; this threshold selection task is formulated as a constrained optimization problem, where the goal is to minimize tracking error whilst controlling the computational requirements. We develop an algorithm that provides an approximate solution for the optimization problem. Simulation experiments provide an example where the proposed framework processes less than 40% of all OOSMs with only a small reduction in tracking accuracy