Real-Time Automatic Linear Feature Detection in Images

Linear feature detection in digital images is an important low-level operation in computer vision that has many applications. In remote sensing tasks, it can be used to extract roads, railroads, and rivers from satellite or low-resolution aerial images, which can be used for the capture or update of data for geographic information and navigation systems. In addition, it is useful in medical imaging for the extraction of blood vessels from an X-ray angiography or the bones in the skull from a CT or MR image. It also can be applied in horticulture for underground plant root detection in minirhizotron images. In this dissertation, a fast and automatic algorithm for linear feature extraction from images is presented. Under the assumption that linear feature is a sequence of contiguous pixels where the image intensity is locally maximal in the direction of the gradient, linear features are extracted as non-overlapping connected line segments consisting of these contiguous pixels. To perform this task, point process is used to model line segments network in images. Specific properties of line segments in an image are described by an intensity energy model. Aligned segments are favored while superposition is penalized. These constraints are enforced by an interaction energy model. Linear features are extracted from the line segments network by minimizing a modified Candy model energy function using a greedy algorithm whose parameters are determined in a data-driven manner. Experimental results from a collection of different types of linear features (underground plant roots, blood vessels and urban roads) in images demonstrate the effectiveness of the approach

The structure of f(R)f(R)-brane model

Recently, a family of interesting analytical brane solutions were found in $f(R)$ gravity with $f(R)=R+\alpha R^2$ in Ref. [Phys. Lett. B 729, 127 (2014)]. In these solutions, inner brane structure can be turned on by tuning the value of the parameter $\alpha$. In this paper, we investigate how the parameter $\alpha$ affects the localization and the quasilocalization of the tensorial gravitons around these solutions. It is found that, in a range of $\alpha$, despite the brane has an inner structure, there is no graviton resonance. However, in some other regions of the parameter space, although the brane has no internal structure, the effective potential for the graviton KK modes has a singular structure, and there exists a series of graviton resonant modes. The contribution of the massive graviton KK modes to the Newton's law of gravity is discussed shortly.Comment: v2: 10 pages, 8 figures, to be published in EPJ

Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution

Bayesian analysis is widely used in science and engineering for real-time forecasting, decision making, and to help unravel the processes that explain the observed data. These data are some deterministic and/or stochastic transformations of the underlying parameters. A key task is then to summarize the posterior distribution of these parameters. When models become too difficult to analyze analytically, Monte Carlo methods can be used to approximate the target distribution. Of these, Markov chain Monte Carlo (MCMC) methods are particularly powerful. Such methods generate a random walk through the parameter space and, under strict conditions of reversibility and ergodicity, will successively visit solutions with frequency proportional to the underlying target density. This requires a proposal distribution that generates candidate solutions starting from an arbitrary initial state. The speed of the sampled chains converging to the target distribution deteriorates rapidly, however, with increasing parameter dimensionality. In this paper, we introduce a new proposal distribution that enhances significantly the efficiency of MCMC simulation for highly parameterized models. This proposal distribution exploits the cross-covariance of model parameters, measurements and model outputs, and generates candidate states much alike the analysis step in the Kalman filter. We embed the Kalman-inspired proposal distribution in the DREAM algorithm during burn-in, and present several numerical experiments with complex, high-dimensional or multi-modal target distributions. Results demonstrate that this new proposal distribution can greatly improve simulation efficiency of MCMC. Specifically, we observe a speed-up on the order of 10-30 times for groundwater models with more than one-hundred parameters