Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2009 - 20102

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Subject: search.filters.subject.Partial Least Squares

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    Looking at People Using Partial Least Squares
    (2010) Schwartz, William Robson; Davis, Larry S; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Analysis of images involving humans is of significant interest in computer vision because problems such as detection, modeling, recognition, and tracking are fundamental to model interactions between people and understand high-level activities. Visual information contained in images is generally represented using descriptors (features). Many general classes of descriptors have been proposed focusing on different characteristics of images. Therefore, if one considers only a single descriptor, one might ignore useful information for a given task, compromising performance. In this research we consider a rich set of image descriptors analyzed by a statistical technique known as Partial Least Squares (PLS). PLS is a class of methods for modeling relations between sets of observations by means of latent variables and it is used to project exemplars from a very high dimensional feature space onto a low dimensional subspace. We demonstrate the effectiveness of combining a richer set of descriptors using PLS in two significant tasks in computer vision. First, we propose a method to detect humans, which is then extended to handle partial occlusion and finally a framework based on PLS regression models is incorporated to further reduce the computational cost. Second, an object recognition framework based on a one-against-all scheme is exploited for appearance-based person modeling and face identification.
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    Hyperspectral Reflectance as an Indicator of Foliar Nutrient Levels in Hybrid Poplar Clone OP-367 Grown on Biosolid Amended Soil
    (2009) Griffeth, Tommy; Felton, Gary; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Trees of the genus Populus are fast growing trees that require considerable amounts of water and nutrients to meet physiological growth demands. The determination of correlations between hybrid poplar leaf spectral reflectance in the 325-1100 nm range, laboratory foliar analysis of leaf macronutrient and micronutrient concentrations, and leaf water potential datasets were analyzed using Full Cross-Validation and Test Set Models via the partial least squares (PLS) method of regression analysis. Based on an evaluation of the slope of the Predicted vs. Measured regression line, the root mean squared error (RMSE), and r-squared, the majority of the models constructed did not adequately model foliar concentrations from spectral data. However, the models for H, N, P, K, Cu and Al had values (slope of the Predicted vs. Measured regression line greater than 0.50 and r-squared values greater than 0.50 in at least one type of model) that warrant future study.