Advances in Graph-Cut Optimization: Multi-Surface Models, Label Costs, and Hierarchical Costs

Computer vision is full of problems that are elegantly expressed in terms of mathematical optimization, or energy minimization. This is particularly true of low-level inference problems such as cleaning up noisy signals, clustering and classifying data, or estimating 3D points from images. Energies let us state each problem as a clear, precise objective function. Minimizing the correct energy would, hypothetically, yield a good solution to the corresponding problem. Unfortunately, even for low-level problems we are confronted by energies that are computationally hard—often NP-hard—to minimize. As a consequence, a rather large portion of computer vision research is dedicated to proposing better energies and better algorithms for energies. This dissertation presents work along the same line, specifically new energies and algorithms based on graph cuts. We present three distinct contributions. First we consider biomedical segmentation where the object of interest comprises multiple distinct regions of uncertain shape (e.g. blood vessels, airways, bone tissue). We show that this common yet difficult scenario can be modeled as an energy over multiple interacting surfaces, and can be globally optimized by a single graph cut. Second, we introduce multi-label energies with label costs and provide algorithms to minimize them. We show how label costs are useful for clustering and robust estimation problems in vision. Third, we characterize a class of energies with hierarchical costs and propose a novel hierarchical fusion algorithm with improved approximation guarantees. Hierarchical costs are natural for modeling an array of difficult problems, e.g. segmentation with hierarchical context, simultaneous estimation of motions and homographies, or detecting hierarchies of patterns

A Multiple Indicators, Multiple Causes Analysis of Farmers\u27 Information Use

A multiple indicators, multiple causes, or MIMIC, modeling framework can be used for analyzing a variety of farmer decision-making situations where multiple outcomes are possible. Example applications include analyses of farmer use of multiple information sources, management practices, or technologies. We applied the framework to analyze use of multiple information sources by beef cattle farmers. We provide measures of how farmer demographics, farm characteristics, and risk attitudes influenced farmer use of information from Extension, producer groups, popular press, the U.S. Department of Agriculture, the Internet, and other farmers. Education and greater willingness to take risk positively influenced information use among the farmers we studied. Our process has implications for broader use within Extension

The Impact of a Visual Cheap Talk Script in an Online Choice Experiment

Hypothetical bias causes willingness to pay (WTP) values to be inaccurate and is a prevalent issue in choice experiments. Research has shown that a “cheap talk” script may reduce hypothetical bias ; however, it is uncertain which cheap talk script format is the best at controlling hypothetical bias . Therefore, we conduct a choice experiment using a between-subjects design in which half of the articipants saw a “visual” cheap talk script and  half saw a “text” cheap talk script prior to the choice sets. Random parameter logit model results indicate hypothetical bias was more prevalent when participants saw the visual cheap talk script compared to the more conventional text cheap talk script. Text learners also appeared to be less prone to hypothetical bias than visual learners

Cow-Calf Producers’ Willingness to Pay for Bulls Resistant to Horn Flies (Diptera: Muscidae)

Horn flies (Haematobia irritans (L.)) have long posed animal health and welfare concerns. Economic losses to the cattle and dairy industries from their blood-feeding behavior include decreased weight gain, loss in milk productivity, and transmission of bacteria causing mastitis in cattle. Horn fly management strategies are labor intensive and can become ineffective due to the horn fly’s ability to develop insecticide resistance. Research indicates that for some cattle herds, genetically similar animals consistently have fewer flies suggesting those animals are horn fly resistant (HFR) and that the trait is heritable; however, it is currently unknown if cattle producers value this trait. Tennessee and Texas cow-calf producers were surveyed to estimate their willingness to pay for HFR bulls and to identify the factors affecting their decision to adopt a HFR bull in their herds. Results indicate that Tennessee and Texas cow-calf producers were willing to pay a premium of 51% and 59% above the base price, respectively, for a HFR bull with the intent to control horn flies within their herd. Producer perceptions of horn fly intensities and the HFR trait, along with their pest management practices, were factors that affected Tennessee and Texas producer willingness to adopt a HFR bull. In Texas, demographics of the producers and their farms also had a role. Knowing producers are willing to pay a premium for the HFR bull indicates that producers value the HFR trait and warrants additional research on the development, implementation, and assessment of the trait

Prognostic Accuracy of WHO Growth Standards to Predict Mortality in a Large-Scale Nutritional Program in Niger

Rebecca Grais and colleagues assess the accuracy of WHO growth standards in predicting death among malnourished children admitted to a large nutritional program in Niger

On the probability of cost-effectiveness using data from randomized clinical trials

BACKGROUND: Acceptability curves have been proposed for quantifying the probability that a treatment under investigation in a clinical trial is cost-effective. Various definitions and estimation methods have been proposed. Loosely speaking, all the definitions, Bayesian or otherwise, relate to the probability that the treatment under consideration is cost-effective as a function of the value placed on a unit of effectiveness. These definitions are, in fact, expressions of the certainty with which the current evidence would lead us to believe that the treatment under consideration is cost-effective, and are dependent on the amount of evidence (i.e. sample size). METHODS: An alternative for quantifying the probability that the treatment under consideration is cost-effective, which is independent of sample size, is proposed. RESULTS: Non-parametric methods are given for point and interval estimation. In addition, these methods provide a non-parametric estimator and confidence interval for the incremental cost-effectiveness ratio. An example is provided. CONCLUSIONS: The proposed parameter for quantifying the probability that a new therapy is cost-effective is superior to the acceptability curve because it is not sample size dependent and because it can be interpreted as the proportion of patients who would benefit if given the new therapy. Non-parametric methods are used to estimate the parameter and its variance, providing the appropriate confidence intervals and test of hypothesis

The weed community affects yield and quality of soybean (Glycine max (L.) Merr.)

The relationship between the weed community and soybean (Glycine max (L.) Merr.) seed yield and quality was assessed in two soybean experiments in Illinois, USA. One field was sown with different proportions of target weeds (Ambrosia trifida L., Amaranthus rudis J. Sauer, Setaria faberi F. Herrm), and the other was naturally infested with these and other weeds. The composition of the weed communities in both fields were compared to final yield and quality (% protein, oil, and water) of the crop using NMDS ordination. Biomass and canopy cover, and seed quality (% protein, relative water content, seed weight) of the crop, were related to the multivariate structure of the weed community in both experiments. Lower quality soybeans were harvested from plots dominated by the target weeds and a suite of subordinate volunteers. Analysis restricted to the volunteer weed community was also significantly related to seed protein and seed weight. Similar results from the two experiments lend generality to the findings and indicate that soybean producers need to manage the composition of the weed community