Evaluating some computer enhancement algorithms that improve the visibility of cometary morphology

The observed morphology of cometary comae is determined by ejection circumstances and the interaction of the ejected material with the local environment. Anisotropic emission can provide useful information on such things as orientation of the nucleus, location of active areas on the nucleus, and the formation of ion structure near the nucleus. However, discrete coma features are usually diffuse, of low amplitude, and superimposed on a steep intensity gradient radial to the nucleus. To improve the visibility of these features, a variety of digital enhancement algorithms were employed with varying degrees of success. They usually produce some degree of spatial filtering, and are chosen to optimize visibility of certain detail. Since information in the image is altered, it is important to understand the effects of parameter selection and processing artifacts can have on subsequent interpretation. Using the criteria that the ideal algorithm must enhance low contrast features while not introducing misleading artifacts (or features that cannot be seen in the stretched, unprocessed image), the suitability of various algorithms that aid cometary studies were assessed. The strong and weak points of each are identified in the context of maintaining positional integrity of features at the expense of photometric information

Q^2-evolution of nucleon-to-resonance transition form factors in a QCD-inspired vector-meson-dominance model

We adopt the vector-meson-dominance approach to investigate Q^2-evolution of N-R transition form factors (N denotes nucleon and R an excited resonance) in the first and second resonance regions. The developed model is based upon conventional NR\gamma-interaction Lagrangians, introducing three form factors for spin-3/2 resonances and two form factors for spin-1/2 nucleon excitations. Lagrangian form factors are expressed as dispersionlike expansions with four or five poles corresponding to the lowest excitations of the mesons \rho(770) and \omega(782). Correct high-Q^2 form factor behavior predicted by perturbative QCD is due to phenomenological logarithmic renormalization of electromagnetic coupling constants and linear superconvergence relations between the parameters of the meson spectrum. The model is found to be in good agreement with all the experimental data on Q^2-dependence of the transitions N-\Delta(1232), N-N(1440), N-N(1520), N-N(1535). We present fit results and model predictions for high-energy experiments proposed by JLab. Besides, we make special emphasis on the transition to perturbative domain of N-\Delta(1232) form factors.Comment: 22 pages, 22 PS figures, REVTeX 4; v2: +3 refs, minor editorial change

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

A Landslide Climate Indicator from Machine Learning

In order to create a Landslide Hazard Index, we accessed rain, snow, and a dozen other variables from the National Climate Assessment Land Data Assimilation System. These predictors were converted to probabilities of landslide occurrence with XGBoost, a major machine-learning tool. The model was fitted with thousands of historical landslides from the Pacific Northwest Landslide Inventory (PNLI)