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You searched for subject:(nonlinear modeling techniques). Showing records 1 – 2 of 2 total matches.

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Utah State University

1. Moisen, Gretchen Gengenbach. Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.

Degree: PhD, Mathematics and Statistics, 2000, Utah State University

Recent emphasis has been placed on merging regional forest inventory data with satellite-based information both to improve the efficiency of estimates of population totals, and to produce regional maps of forest variables. There are numerous ways in which forest class and structure variables may be modeled as functions of remotely sensed variables, yet surprisingly little work has been directed at surveying modem statistical techniques to determine which tools are best suited to the tasks given multiple objectives and logistical constraints. Here, a series of analyses to compare nonlinear and nonparametric modeling techniques for mapping a variety of forest variables, and for stratification of field plots, was conducted using data in the Interior Western United States. The analyses compared four statistical modeling techniques for predicting two discrete and four continuous forest inventory variables. The modeling techniques include generalized additive models (GAMs), classification and regression trees (CARTs), multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs). Alternative stratification schemes were also compared for estimating population totals. The analyses were conducted within six ecologically different regions using a variety of satellite-based predictor variables. The work resulted in the development of an objective modeling box that automatically models spatial response variables as functions of any assortment of predictor variables through the four nonlinear or nonparametric modeling techniques. In comparing the different modeling techniques, all proved themselves workable in an automated environment, though ANNs were more problematic. When their potential mapping ability was explored through a simple simulation, tremendous advantages were seen in use of MARS and ANN for prediction over GAMs, CART, and a simple linear model. However, much smaller differences were seen when using real data. In some instances, a simple linear approach worked virtually as well as the more complex models, while small gains were seen using more complex models in other instances. In real data runs, MARS performed (marginally) best most often for binary variables, while GAMs performed (marginally) best most often for continuous variables. After considering a subjective "ease of use" measure, computing time and other predictive performance measures, it was determined that MARS had many advantages over other modeling techniques. In addition, stratification tests illustrated cost-effective means to improve precision of estimates of forest population totals. Finally, the general effect of map accuracy on the relative precision of estimates of population totals obtained under simple random sampling compared to that obtained under stratified random sampling was established and graphically illustrated as a tool for management decisions. Advisors/Committee Members: D. Richard Cutler, Joseph V. Koebbe, Daniel C. Coster, ;.

Subjects/Keywords: nonlinear modeling techniques; nonparametric modeling techniques; mapping; stratification; forest inventories; Interior Western USA; Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Moisen, G. G. (2000). Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7108

Chicago Manual of Style (16th Edition):

Moisen, Gretchen Gengenbach. “Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.” 2000. Doctoral Dissertation, Utah State University. Accessed January 27, 2020. https://digitalcommons.usu.edu/etd/7108.

MLA Handbook (7th Edition):

Moisen, Gretchen Gengenbach. “Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.” 2000. Web. 27 Jan 2020.

Vancouver:

Moisen GG. Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA. [Internet] [Doctoral dissertation]. Utah State University; 2000. [cited 2020 Jan 27]. Available from: https://digitalcommons.usu.edu/etd/7108.

Council of Science Editors:

Moisen GG. Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA. [Doctoral Dissertation]. Utah State University; 2000. Available from: https://digitalcommons.usu.edu/etd/7108

2. Ha, Jin-cheol. Real-time visual tracking using image processing and filtering methods.

Degree: PhD, Aerospace Engineering, 2008, Georgia Tech

The main goal of this thesis is to develop real-time computer vision algorithms in order to detect and to track targets in uncertain complex environments purely based on a visual sensor. Two major subjects addressed by this work are: 1. The development of fast and robust image segmentation algorithms that are able to search and automatically detect targets in a given image. 2. The development of sound filtering algorithms to reduce the effects of noise in signals from the image processing. The main constraint of this research is that the algorithms should work in real-time with limited computing power on an onboard computer in an aircraft. In particular, we focus on contour tracking which tracks the outline of the target represented by contours in the image plane. This thesis is concerned with three specific categories, namely image segmentation, shape modeling, and signal filtering. We have designed image segmentation algorithms based on geometric active contours implemented via level set methods. Geometric active contours are deformable contours that automatically track the outlines of objects in images. In this approach, the contour in the image plane is represented as the zero-level set of a higher dimensional function. (One example of the higher dimensional function is a three-dimensional surface for a two-dimensional contour.) This approach handles the topological changes (e.g., merging, splitting) of the contour naturally. Although geometric active contours prevail in many fields of computer vision, they suffer from the high computational costs associated with level set methods. Therefore, simplified versions of level set methods such as fast marching methods are often used in problems of real-time visual tracking. This thesis presents the development of a fast and robust segmentation algorithm based on up-to-date extensions of level set methods and geometric active contours, namely a fast implementation of Chan-Vese's (active contour) model (FICVM). The shape prior is a useful cue in the recognition of the true target. For the contour tracker, the outline of the target can be easily disrupted by noise. In geometric active contours, to cope with deviations from the true outline of the target, a higher dimensional function is constructed based on the shape prior, and the contour tracks the outline of an object by considering the difference between the higher dimensional functions obtained from the shape prior and from a measurement in a given image. The higher dimensional function is often a distance map which requires high computational costs for construction. This thesis focuses on the extraction of shape information from only the zero-level set of the higher dimensional function. This strategy compensates for inaccuracies in the calculation of the shape difference that occur when a simplified higher dimensional function is used. This is named as contour-based shape modeling. Filtering is an essential element in tracking problems because of the presence of noise in system models and… Advisors/Committee Members: Eric N. Johnson (Committee Chair), Allen R. Tannenbaum (Committee Co-Chair), Anthony J. Calise (Committee Member), Eric Feron (Committee Member), Patricio A. Vela (Committee Member).

Subjects/Keywords: Visual tracking; Image processing; Nonlinear filtering; Statistical estimation; Shape modeling.; Computer vision; Image processing Digital techniques; Real-time data processing; Algorithms

…segmentation, shape modeling, and signal filtering. We have designed image segmentation algorithms… …dimensional function is used. This is named as contour-based shape modeling. Filtering is an… …linear models and Gaussian distributions (linear/Gaussian problems). For nonlinear… …FICVM. The dissimilarity measure is calculated by the contour based shape modeling method and… …on them by considering results from multiple frames and process modeling. Image processing… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ha, J. (2008). Real-time visual tracking using image processing and filtering methods. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/28177

Chicago Manual of Style (16th Edition):

Ha, Jin-cheol. “Real-time visual tracking using image processing and filtering methods.” 2008. Doctoral Dissertation, Georgia Tech. Accessed January 27, 2020. http://hdl.handle.net/1853/28177.

MLA Handbook (7th Edition):

Ha, Jin-cheol. “Real-time visual tracking using image processing and filtering methods.” 2008. Web. 27 Jan 2020.

Vancouver:

Ha J. Real-time visual tracking using image processing and filtering methods. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2020 Jan 27]. Available from: http://hdl.handle.net/1853/28177.

Council of Science Editors:

Ha J. Real-time visual tracking using image processing and filtering methods. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/28177

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