+publisher:"University of Florida" +contributor:("Vemuri, Baba C.")

University of Florida

1. Sarov, Martin G. Constructing the Spline Atlas of a Free-Form Surface.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2017, University of Florida

► High-end shape design of product-defining surfaces and engineering analysis of the resulting objects employ vastly different representations. The resulting gap between design and analysis impedes… (more)

Subjects/Keywords: atlas – b-spline – bezier – bicubic – cagd – continuity – degree-reduction – design – differentiable – extraordinary-point – fea – fem – free-form – g-spline – genus – geometric – iga – knot-insertion – mesh – nested – parametric – patch – polycube – quad – refinable – reparameterization – spline – surface – t-junction – tensor-product – trimming

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APA (6^th Edition):

Sarov, M. G. (2017). Constructing the Spline Atlas of a Free-Form Surface. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050899

Chicago Manual of Style (16^th Edition):

Sarov, Martin G. “Constructing the Spline Atlas of a Free-Form Surface.” 2017. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0050899.

MLA Handbook (7^th Edition):

Sarov, Martin G. “Constructing the Spline Atlas of a Free-Form Surface.” 2017. Web. 16 Dec 2019.

Vancouver:

Sarov MG. Constructing the Spline Atlas of a Free-Form Surface. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0050899.

Council of Science Editors:

Sarov MG. Constructing the Spline Atlas of a Free-Form Surface. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0050899

University of Florida

2. Smith, Anthony O. Category Space Dimensionality Reduction for Supervised Learning.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2013, University of Florida

URL: http://ufdc.ufl.edu/UFE0045768

► In this research, we investigate the reasons that make the multiclass classification problem difficult and suggest that category ambiguity is at the heart of the… (more)

Subjects/Keywords: Datasets; Dimensionality reduction; Discriminants; Distance functions; Inner products; Learning; Machine learning; Mathematical vectors; Objective functions; Vector spaces; classification – multiclass – orthogonal – reduction

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APA (6^th Edition):

Smith, A. O. (2013). Category Space Dimensionality Reduction for Supervised Learning. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045768

Chicago Manual of Style (16^th Edition):

Smith, Anthony O. “Category Space Dimensionality Reduction for Supervised Learning.” 2013. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0045768.

MLA Handbook (7^th Edition):

Smith, Anthony O. “Category Space Dimensionality Reduction for Supervised Learning.” 2013. Web. 16 Dec 2019.

Vancouver:

Smith AO. Category Space Dimensionality Reduction for Supervised Learning. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0045768.

Council of Science Editors:

Smith AO. Category Space Dimensionality Reduction for Supervised Learning. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045768

University of Florida

3. Kaya, Gokhan. Signal Coding and Reconstruction through a Framework Which Utilizes Naturally-Inspired Deterministic Spiking Models.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2016, University of Florida

URL: http://ufdc.ufl.edu/UFE0050379

► Spiking is the common form of action adopted by neurons which cumulatively creates the notion of activity in the nervous system. Any attempt on defining… (more)

Subjects/Keywords: Arithmetic mean; Electric pulses; Error rates; Membrane potential; Modeling; Neurons; Receptors; Sensory system; Signals; Time signals; computationalneuroscience – neuroncoding – signalcoding – spikes

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APA (6^th Edition):

Kaya, G. (2016). Signal Coding and Reconstruction through a Framework Which Utilizes Naturally-Inspired Deterministic Spiking Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050379

Chicago Manual of Style (16^th Edition):

Kaya, Gokhan. “Signal Coding and Reconstruction through a Framework Which Utilizes Naturally-Inspired Deterministic Spiking Models.” 2016. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0050379.

MLA Handbook (7^th Edition):

Kaya, Gokhan. “Signal Coding and Reconstruction through a Framework Which Utilizes Naturally-Inspired Deterministic Spiking Models.” 2016. Web. 16 Dec 2019.

Vancouver:

Kaya G. Signal Coding and Reconstruction through a Framework Which Utilizes Naturally-Inspired Deterministic Spiking Models. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0050379.

Council of Science Editors:

Kaya G. Signal Coding and Reconstruction through a Framework Which Utilizes Naturally-Inspired Deterministic Spiking Models. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050379

University of Florida

4. Mirzargar, Seyyedeh Mahsa. A Reconstruction Framework for Common Sampling Lattices.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

URL: http://ufdc.ufl.edu/UFE0044621

► Sampling and reconstruction appear ubiquitously in many scientific and engineering applications. Conventionally, the Cartesian lattice has been used, while sampling on certain non-Cartesian lattices is… (more)

▼ Sampling and reconstruction appear ubiquitously in many scientific and engineering applications. Conventionally, the Cartesian lattice has been used, while sampling on certain non-Cartesian lattices is known to capture signal’s information content more efficiently. In the trivariate case, the Body Centered Cubic lattice (BCC) and the Face Centered Cubic lattice (FCC) are considered to be superior to the Cartesian. However, developing suitable reconstruction schemes for these lattices has been a challenge. This dissertation is devoted to the introduction of Voronoi splines as an unbiased reconstruction framework for common sampling lattices. The main challenge for reconstruction on non-Cartesian lattices is due to the non-separable nature of these lattices. This challenge has been addressed by the geometric analysis of the Voronoi cell of a lattice and its relation to box spline theory to derive an exact and closed form solution which we call the generalization of B-splines, Voronoi splines. This reconstruction framework provides unbiased approximation schemes among various lattices. Various properties of this family of reconstruction kernels will be discussed in detail including their approximation properties and their Fourier transforms. Voronoi splines can be constructed for different degrees of continuity. Going beyond interpolating kernels, and using higher order kernels, the over-smoothing artifact results in degradation of the approximation. Over-smoothing artifact has been studied in terms of polynomial reproduction power of a reconstruction kernel. This artifact has been addressed through developing a quasi interpolation scheme tailored for Voronoi splines on BCC and FCC lattices to attain their asymptotic approximation error behaviour. In order to overcome the burden of box spline evaluation, clustering algorithms have been used to derive the exact polynomial representation of Voronoi splines of second order on BCC and FCC lattices. The second order Voronoi splines on these lattices are equivalent to the widely used trilinear interpolation. The efficiency and accuracy of reconstruction using Voronoi splines have been studied on different types of data including synthetic data and real life CT datasets. In addition, scattered data reconstruction using Voronoi splines has been studied through the introduction of a variational framework to approximate the underlying signal on different choices of uniform lattices. As a whole, this dissertation investigates the potential benefits of the Voronoi spline framework along with sampling on non-Cartesian sampling lattices. ( en ) Advisors/Committee Members: Entezari, Alireza (committee chair), Banerjee, Arunava (committee member), Ritter, Gerhard (committee member), Vemuri, Baba C (committee member), Blackband, Stephen J (committee member).

Subjects/Keywords: Approximation; Boxes; Cartesianism; Conceptual lattices; Datasets; Face centered cubic lattices; Interpolation; Mathematical lattices; Polynomials; Signals; approximation – quasi – reconstruction – splines – voronoi

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APA (6^th Edition):

Mirzargar, S. M. (2012). A Reconstruction Framework for Common Sampling Lattices. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044621

Chicago Manual of Style (16^th Edition):

Mirzargar, Seyyedeh Mahsa. “A Reconstruction Framework for Common Sampling Lattices.” 2012. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0044621.

MLA Handbook (7^th Edition):

Mirzargar, Seyyedeh Mahsa. “A Reconstruction Framework for Common Sampling Lattices.” 2012. Web. 16 Dec 2019.

Vancouver:

Mirzargar SM. A Reconstruction Framework for Common Sampling Lattices. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0044621.

Council of Science Editors:

Mirzargar SM. A Reconstruction Framework for Common Sampling Lattices. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044621

University of Florida

5. Gurumoorthy,Karthik S. A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2011, University of Florida

URL: http://ufdc.ufl.edu/UFE0043099

Many computational techniques based on classical mechanics exist but surprisingly there isn't a concomitant Advisors/Committee Members: Banerjee, Arunava (committee chair), Ho, Jeffrey (committee member), Vemuri, Baba C (committee member), Gader, Paul D (committee member), Shabanov, Sergei (committee member).

Subjects/Keywords: Approximation; Distance functions; Eikonal equation; Error rates; Fourier transformations; Mathematics; Quantum mechanics; Steepest descent method; Wave equations; Wave functions

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APA (6^th Edition):

S, G. (2011). A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043099

Chicago Manual of Style (16^th Edition):

S, Gurumoorthy,Karthik. “A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation.” 2011. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0043099.

MLA Handbook (7^th Edition):

S, Gurumoorthy,Karthik. “A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation.” 2011. Web. 16 Dec 2019.

Vancouver:

S G. A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0043099.

Council of Science Editors:

S G. A Schrodinger Wave Mechanics Formalism for the Eikonal Problem and Its Associated Gradient Density Computation. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043099

University of Florida

6. Salehian, Hesamoddin. Incremental Algorithms for Statistical Analysis of Manifold Valued Data.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

URL: http://ufdc.ufl.edu/UFE0047484

► Manifold-valued features are ubiquitous in many applications in computer vision, machine learning and medical image analysis. Statistical analysis of a population of such data is… (more)

Subjects/Keywords: Computer vision; Distance functions; Estimators for the mean; Geometry; Gnomonic projection; Matrices; Principal components analysis; Riemann manifold; Statistical analysis; Tensors; incremental – manifold – statistics

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

APA (6^th Edition):

Salehian, H. (2014). Incremental Algorithms for Statistical Analysis of Manifold Valued Data. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0047484

Chicago Manual of Style (16^th Edition):

Salehian, Hesamoddin. “Incremental Algorithms for Statistical Analysis of Manifold Valued Data.” 2014. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0047484.

MLA Handbook (7^th Edition):

Salehian, Hesamoddin. “Incremental Algorithms for Statistical Analysis of Manifold Valued Data.” 2014. Web. 16 Dec 2019.

Vancouver:

Salehian H. Incremental Algorithms for Statistical Analysis of Manifold Valued Data. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0047484.

Council of Science Editors:

Salehian H. Incremental Algorithms for Statistical Analysis of Manifold Valued Data. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0047484

University of Florida

7. Cheng, Guang. Algorithms for Tracking on the Manifold of Symmetric Positive Definite Matrices.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

URL: http://ufdc.ufl.edu/UFE0044073

► The problem of tracking on the manifold of n × n symmetric positive definite (SPD) matrices is an important problem and has many applications in several… (more)

Subjects/Keywords: Covariance; Datasets; Distance functions; Euclidean space; Gaussian distributions; Kalman filters; Matrices; Rats; Riemann manifold; Tensors; kalman – manifold – matrix – spd – tracking – tractography

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APA (6^th Edition):

Cheng, G. (2012). Algorithms for Tracking on the Manifold of Symmetric Positive Definite Matrices. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044073

Chicago Manual of Style (16^th Edition):

Cheng, Guang. “Algorithms for Tracking on the Manifold of Symmetric Positive Definite Matrices.” 2012. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0044073.

MLA Handbook (7^th Edition):

Cheng, Guang. “Algorithms for Tracking on the Manifold of Symmetric Positive Definite Matrices.” 2012. Web. 16 Dec 2019.

Vancouver:

Cheng G. Algorithms for Tracking on the Manifold of Symmetric Positive Definite Matrices. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0044073.

Council of Science Editors:

Cheng G. Algorithms for Tracking on the Manifold of Symmetric Positive Definite Matrices. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044073

University of Florida

8. Kumar, Ritwik. Relighting, Pose Change and Recognition of Faces.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

URL: http://ufdc.ufl.edu/UFE0041052

► Relighting, pose change and recognition of faces from images are intimately connected fundamental problems in the field of Computer Vision and Graphics. These problems are… (more)

Subjects/Keywords: Approximation; Computer recognition; Computer vision; Databases; Datasets; Image classification; Lighting; Pies; Pixels; Tensors; abrdf, face, pose, recognition, reflectance, relighting

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

APA (6^th Edition):

Kumar, R. (2009). Relighting, Pose Change and Recognition of Faces. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041052

Chicago Manual of Style (16^th Edition):

Kumar, Ritwik. “Relighting, Pose Change and Recognition of Faces.” 2009. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0041052.

MLA Handbook (7^th Edition):

Kumar, Ritwik. “Relighting, Pose Change and Recognition of Faces.” 2009. Web. 16 Dec 2019.

Vancouver:

Kumar R. Relighting, Pose Change and Recognition of Faces. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0041052.

Council of Science Editors:

Kumar R. Relighting, Pose Change and Recognition of Faces. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0041052

University of Florida

9. Seo, Dohyung. A geometric approach to image matching and synthesis of diffeomorphic paths.

Degree: PhD, Electrical and Computer Engineering, 2013, University of Florida

URL: http://ufdc.ufl.edu/UFE0045283

► Image processing and analysis traditionally deals with methods that do not take the geometry of the image surface into account. Using the geometry of the… (more)

Subjects/Keywords: Cost functions; Datasets; Distance functions; Ground truth; Image classification; Keyframes; Retraining; Tensors; Three dimensional imaging; Vector fields; diffeomorphisms – image – registration

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

APA (6^th Edition):

Seo, D. (2013). A geometric approach to image matching and synthesis of diffeomorphic paths. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045283

Chicago Manual of Style (16^th Edition):

Seo, Dohyung. “A geometric approach to image matching and synthesis of diffeomorphic paths.” 2013. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0045283.

MLA Handbook (7^th Edition):

Seo, Dohyung. “A geometric approach to image matching and synthesis of diffeomorphic paths.” 2013. Web. 16 Dec 2019.

Vancouver:

Seo D. A geometric approach to image matching and synthesis of diffeomorphic paths. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0045283.

Council of Science Editors:

Seo D. A geometric approach to image matching and synthesis of diffeomorphic paths. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045283

University of Florida

10. Subakan, Ozlem. Continuous Mixture Models for Feature Preserving Smoothing and Segmentation.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

URL: http://ufdc.ufl.edu/UFE0024915

► Image smoothing and segmentation are fundamental tasks in computer vision. Although there are numerous algorithms that have been developed and applied to these tasks in… (more)

▼ Image smoothing and segmentation are fundamental tasks in computer vision. Although there are numerous algorithms that have been developed and applied to these tasks in various application domains, several challenges remain unconquered. In this dissertation, we consider the challenge of achieving smoothing and segmentation while preserving complicated and detailed features present in the image, be it a gray level or a color image. We present novel approaches that do not make use of any prior information about the objects in the image being processed, and yet produce promising results. The key idea here is to model the derived local orientation information via a continuous mixture of appropriate basis functions. We present several such models considering grayscale and color images separately. We propose two models for grayscale images; one involving a continuous mixture over the covariance matrices of Gaussian basis functions, and another involving a continuous mixture over the mean direction vectors of Watson basis functions. For color image processing, we introduce a novel Quaternionic Gabor Filter (QGF) which can combine the color channels and the orientations in the image plane. The local orientation information in the color images can be extracted using the QGFs. We show that these filters are optimally localized both in the spatial and frequency domains and provide a good approximation to quaternionic quadrature filters. In a second logical step, we propose continuous mixtures of appropriate hypercomplex exponential basis functions to model this local orientation information. We derive closed form solutions for the proposed models. These continuous mixture models are then used to construct spatially varying kernels which are convolved with the color image or the signed distance function of an evolving contour (placed in the color image) to achieve feature preserving smoothing or segmentation, respectively. We compare and quantitatively validate the proposed models with numerous experimental results on real images including the images drawn from Berkeley Segmentation Data Set. These comparisons revealed that the techniques we developed are superior than the state-of-the-art algorithms described in the literature. Finally, the dissertation is concluded with a list of potential directions for future research. ( en ) Advisors/Committee Members: Vemuri, Baba C. (committee chair), Rangarajan, Anand (committee member), Ho, Jeffrey (committee member), Banerjee, Arunava (committee member), Vallejos, Carlos E. (committee member).

Subjects/Keywords: Computer vision; Curvature; Data smoothing; Gabor filters; Image enhancement; Image filters; Image processing; Images; Matrices; Quaternions; bingham, color, continuous, directional, distributions, filters, fisher, gabor, gaussian, image, mixture, models, quaternions, segmentation, smoothing, watson, wishart

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APA (6^th Edition):

Subakan, O. (2009). Continuous Mixture Models for Feature Preserving Smoothing and Segmentation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024915

Chicago Manual of Style (16^th Edition):

Subakan, Ozlem. “Continuous Mixture Models for Feature Preserving Smoothing and Segmentation.” 2009. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0024915.

MLA Handbook (7^th Edition):

Subakan, Ozlem. “Continuous Mixture Models for Feature Preserving Smoothing and Segmentation.” 2009. Web. 16 Dec 2019.

Vancouver:

Subakan O. Continuous Mixture Models for Feature Preserving Smoothing and Segmentation. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0024915.

Council of Science Editors:

Subakan O. Continuous Mixture Models for Feature Preserving Smoothing and Segmentation. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024915

University of Florida

11. Yashtini, Maryam. Fast TV-Regularized Large-Scale and Ill-Conditioned Linear Inversion with Application to Partially Parallel Imaging.

Degree: PhD, Mathematics, 2014, University of Florida

URL: http://ufdc.ufl.edu/UFE0047095

► This research focuses on development and analysis of fast algorithms for image reconstruction problems that arise from an emerging magnetic resonance medical imaging technique known… (more)

Subjects/Keywords: Algorithms; Ergodic theory; Error rates; Image reconstruction; Imaging; Infinity; Inverse problems; Magnetic resonance imaging; Objective functions; Payment protection insurance; algorithms – convergence – ill-condition – mri – optimization – regularization

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APA (6^th Edition):

Yashtini, M. (2014). Fast TV-Regularized Large-Scale and Ill-Conditioned Linear Inversion with Application to Partially Parallel Imaging. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0047095

Chicago Manual of Style (16^th Edition):

Yashtini, Maryam. “Fast TV-Regularized Large-Scale and Ill-Conditioned Linear Inversion with Application to Partially Parallel Imaging.” 2014. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0047095.

MLA Handbook (7^th Edition):

Yashtini, Maryam. “Fast TV-Regularized Large-Scale and Ill-Conditioned Linear Inversion with Application to Partially Parallel Imaging.” 2014. Web. 16 Dec 2019.

Vancouver:

Yashtini M. Fast TV-Regularized Large-Scale and Ill-Conditioned Linear Inversion with Application to Partially Parallel Imaging. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0047095.

Council of Science Editors:

Yashtini M. Fast TV-Regularized Large-Scale and Ill-Conditioned Linear Inversion with Application to Partially Parallel Imaging. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0047095

University of Florida

12. Rajwade, Ajit. Probabilistic Approaches to Image Registration and Denoising.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2010, University of Florida

URL: http://ufdc.ufl.edu/UFE0042349

► PROBABILISTIC APPROACHES TO IMAGE REGISTRATION AND DENOISING We present probabilistically driven approaches to two major applications in computer vision and image processing: image alignment (registration)… (more)

▼ PROBABILISTIC APPROACHES TO IMAGE REGISTRATION AND DENOISING We present probabilistically driven approaches to two major applications in computer vision and image processing: image alignment (registration) and filtering of intensity values corrupted by noise. Some existing methods for these applications require the estimation of the probability density of the intensity values defined on the image domain. Most of the contemporary density estimation techniques employ different types of kernel functions for smoothing the estimated density values. These kernels are unrelated to the structure or geometry of the image. The present work chooses to depart from this conventional approach to one which seeks to approximate the image as a continuous or piecewise continuous function of the spatial coordinates, and subsequently expresses the probability density in terms of some key geometric properties of the image, such as its gradients and iso-intensity level sets. This framework, which regards an image as a signal as opposed to a bunch of samples, is then extended to the case of joint probability densities between two or more images and for different domains (2D and 3D). A biased density estimate that expressly favors the higher gradient regions of the image is also presented. These techniques for probability density estimation are used (1) for the task of affine registration of images drawn from different sensing modalities, and (2) to build neighborhood filters in the well-known mean shift framework, for the denoising of corrupted gray-scale and color images, chromaticity fields and gray-scale video. Using our new density estimators, we demonstrate improvement in the performance of these applications. A new approach for the estimation of the probability density of spherical data is also presented, taking into account the fact that the source of such data are commonly known or assumed to be Euclidean, particularly within the field of image analysis. We also develop two patch-based image denoising algorithms that revisit the old patch-based singular value decomposition (SVD) technique proposed in the seventies. Noise does not affect only the singular values of an image patch, but also severely affects its SVD bases leading to poor quality denoising if those bases are used. With this in mind, we provide motivation for manipulating the SVD bases of the image patches for improving denoising performance. To this end, we develop a probabilistic non-local framework which learns spatially adaptive orthonormal bases that are derived by exploiting the similarity between patches from different regions of an image. These bases act as a common SVD for the group of patches similar to any reference patch in the image. The reference image patches are then filtered by projection onto these learned bases, manipulation of the transform coefficients and inversion of the transform. We present or use principled criteria for the notion of similarity between patches under noise and manipulation of the coefficients, assuming a fixed known noise model.… Advisors/Committee Members: Rangarajan, Anand (committee chair), Ho, Jeffrey (committee member), Vemuri, Baba C. (committee member), Presnell, Brett D. (committee member).

Subjects/Keywords: Datasets; Density estimation; Estimation methods; Estimators; Image filters; Image processing; Level curves; Matrices; Pixels; Signals; bases, denoising, density, directional, estimation, filtering, hosvd, image, interpolant, interpolation, isocontours, isosurfaces, learning, orthonormal, probabilistic, probability, processing, random, restoration, singular, statistical, statistics, svd, tensor, transform, transformation, variable

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

APA (6^th Edition):

Rajwade, A. (2010). Probabilistic Approaches to Image Registration and Denoising. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0042349

Chicago Manual of Style (16^th Edition):

Rajwade, Ajit. “Probabilistic Approaches to Image Registration and Denoising.” 2010. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0042349.

MLA Handbook (7^th Edition):

Rajwade, Ajit. “Probabilistic Approaches to Image Registration and Denoising.” 2010. Web. 16 Dec 2019.

Vancouver:

Rajwade A. Probabilistic Approaches to Image Registration and Denoising. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0042349.

Council of Science Editors:

Rajwade A. Probabilistic Approaches to Image Registration and Denoising. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0042349

University of Florida

13. Hwang, Min. Non-invasive Investigation of Cardiac Tissue Compartments and Components using MR Microscopy.

Degree: PhD, Biomedical Engineering, 2010, University of Florida

URL: http://ufdc.ufl.edu/UFE0041200

► NON-INVASIVE INVESTIGATION OF CARDIAC TISSUE COMPARTMENTS AND COMPONENTS USING MR MICROSCOPY The heart responds to the ever-changing demands that the whole body places on the… (more)

▼ NON-INVASIVE INVESTIGATION OF CARDIAC TISSUE COMPARTMENTS AND COMPONENTS USING MR MICROSCOPY The heart responds to the ever-changing demands that the whole body places on the cardiovascular system. This life-time pump expels the blood out by generating a forceful and efficient contraction. The homogeneous function is the orchestrated outcome of morphologic, electrophysiological, molecular constituents that exhibit prominent heterogeneity in the heart. Non-invasive imaging of anatomical structure of the whole heart has increasing demand for the understanding of its role to normal function and pathological development. Our hypothesis is that magnetic resonance imaging at high magnetic fields can distinguish different tissue compartments and cellular components in the mammalian heart and monitor their pathophysiological alterations, in-situ. In this thesis, we investigate non-invasively tissue compartments and cellular components comprising the rabbit heart using 17.6 T and 11.1 T magnets. The attenuation of MR diffusion signal intensity is used to reflect change in the tissue compartments. Magnetic resonance micro images contrasted by diffusion and transverse relaxation time of water molecules reveal anatomical features previously unavailable. Besides the myocardium, we visualize anatomical details of the cardiac conduction system from the bundle of His to terminal ramification of the Purkinje fibers in the ventricles using three-dimensional high resolution MR imaging and microscopic high angular resolution diffusion imaging (MHARDI). Anatomical relationship of constituents in the ventricular muscular interseptum is explored. The combined investigative technique of microscopic three-dimensional high resolution MRI and MHARDI is observed to be an effective method of monitoring morphological changes occurring in cardiac tissue with advancing age. ( en ) Advisors/Committee Members: Forder, John (committee chair), Sadleir, Rosalind Jane (committee member), Vemuri, Baba C. (committee member), Blackband, Stephen J. (committee member).

Subjects/Keywords: Cells; Diffusion coefficient; Heart; Imaging; Magnetic resonance imaging; Magnetism; Myocardium; Rabbits; Signals; Tensors; cardiac, imaging, magnetic, microscopy, mri

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

APA (6^th Edition):

Hwang, M. (2010). Non-invasive Investigation of Cardiac Tissue Compartments and Components using MR Microscopy. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041200

Chicago Manual of Style (16^th Edition):

Hwang, Min. “Non-invasive Investigation of Cardiac Tissue Compartments and Components using MR Microscopy.” 2010. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0041200.

MLA Handbook (7^th Edition):

Hwang, Min. “Non-invasive Investigation of Cardiac Tissue Compartments and Components using MR Microscopy.” 2010. Web. 16 Dec 2019.

Vancouver:

Hwang M. Non-invasive Investigation of Cardiac Tissue Compartments and Components using MR Microscopy. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0041200.

Council of Science Editors:

Hwang M. Non-invasive Investigation of Cardiac Tissue Compartments and Components using MR Microscopy. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0041200

University of Florida

14. Barmpoutis, Angelos. Tensor Based Representation and Analysis of Diffusion Weighted Magnetic Resonance Images.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

URL: http://ufdc.ufl.edu/UFE0024350

► Cartesian tensor bases have been widely used to model spherical functions. In medical imaging, tensors of various orders can approximate the diffusivity function at each… (more)

Subjects/Keywords: Approximation; Datasets; Diffusion coefficient; Distance functions; Estimation methods; Fiber orientation; Matrices; Rats; Signals; Tensors; diffusion, dti, estimation, hardi, mri, registration, segmentation, tensor

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APA (6^th Edition):

Barmpoutis, A. (2009). Tensor Based Representation and Analysis of Diffusion Weighted Magnetic Resonance Images. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024350

Chicago Manual of Style (16^th Edition):

Barmpoutis, Angelos. “Tensor Based Representation and Analysis of Diffusion Weighted Magnetic Resonance Images.” 2009. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0024350.

MLA Handbook (7^th Edition):

Barmpoutis, Angelos. “Tensor Based Representation and Analysis of Diffusion Weighted Magnetic Resonance Images.” 2009. Web. 16 Dec 2019.

Vancouver:

Barmpoutis A. Tensor Based Representation and Analysis of Diffusion Weighted Magnetic Resonance Images. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0024350.

Council of Science Editors:

Barmpoutis A. Tensor Based Representation and Analysis of Diffusion Weighted Magnetic Resonance Images. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024350

University of Florida

15. Deng, Yan. Novel Algorithms for Uni- and Multi-Modal Image Segmentation.

Degree: PhD, Computer Science - Computer and Information Science and Engineering, 2016, University of Florida

URL: http://ufdc.ufl.edu/UFE0049872

► Automated Brain MRI segmentation methods have gained immense interest in the past couple of decades. Due to the inhomogeneity caused by bias field from the… (more)

Subjects/Keywords: Algorithms; Amygdala; Atlases; Datasets; Geometric shapes; Ground truth; Hippocampus; Image contrast; Learning; Objective functions; atlas – brain – clouds – contrast – fusion – labeled – matching – mri – partially – point – segmentation – synthesis

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APA (6^th Edition):

Deng, Y. (2016). Novel Algorithms for Uni- and Multi-Modal Image Segmentation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049872

Chicago Manual of Style (16^th Edition):

Deng, Yan. “Novel Algorithms for Uni- and Multi-Modal Image Segmentation.” 2016. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0049872.

MLA Handbook (7^th Edition):

Deng, Yan. “Novel Algorithms for Uni- and Multi-Modal Image Segmentation.” 2016. Web. 16 Dec 2019.

Vancouver:

Deng Y. Novel Algorithms for Uni- and Multi-Modal Image Segmentation. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0049872.

Council of Science Editors:

Deng Y. Novel Algorithms for Uni- and Multi-Modal Image Segmentation. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0049872

University of Florida

16. Ye, Wenxing. Efficient Sampling Geometries and Reconstruction Algorithms for Estimation of Diffusion Propagators.

Degree: PhD, Electrical and Computer Engineering, 2012, University of Florida

URL: http://ufdc.ufl.edu/UFE0044054

► Diffusion weighted magnetic resonance imaging (DW-MRI) is a non-invasive techniques that can reveal the anatomical structure of living organism. Through measuring directional diffusion of water… (more)

Subjects/Keywords: Body centered cubic lattices; Boxes; Cartesianism; Conceptual lattices; Datasets; Fourier transformations; Geometry; Imaging; Mathematical lattices; Signals; diffusion – geometry – hardi – mri – reconstruction – sampling

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APA (6^th Edition):

Ye, W. (2012). Efficient Sampling Geometries and Reconstruction Algorithms for Estimation of Diffusion Propagators. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044054

Chicago Manual of Style (16^th Edition):

Ye, Wenxing. “Efficient Sampling Geometries and Reconstruction Algorithms for Estimation of Diffusion Propagators.” 2012. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0044054.

MLA Handbook (7^th Edition):

Ye, Wenxing. “Efficient Sampling Geometries and Reconstruction Algorithms for Estimation of Diffusion Propagators.” 2012. Web. 16 Dec 2019.

Vancouver:

Ye W. Efficient Sampling Geometries and Reconstruction Algorithms for Estimation of Diffusion Propagators. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0044054.

Council of Science Editors:

Ye W. Efficient Sampling Geometries and Reconstruction Algorithms for Estimation of Diffusion Propagators. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044054

University of Florida

17. Jian, Bing. Mathematical Modeling for Multi-Fiber Reconstruction from Diffusion-Weighted Magnetic Resonance Images.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

URL: http://ufdc.ufl.edu/UFE0021691

► Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive imaging technique that allows neural tissue architecture to be probed at a microscopic scale in vivo. By… (more)

▼ Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive imaging technique that allows neural tissue architecture to be probed at a microscopic scale in vivo. By measuring quantitative data sensitive to the water molecular diffusion, DW-MRI provides valuable information for neuronal connectivity inference and brain developmental studies. The broad aim of this dissertation is to develop mathematical models and computational tools for quantifying and extracting information contained in diffusion MR images. One of the fundamental problems in DW-MRI analysis is the mathematical modeling of the MR signal attenuation in a voxel in the presence of multiple fiber bundles. In this dissertation, we present a novel mathematical model and accompanying efficient algorithms for this problem. Our model uses a continuous probability distribution over the space of symmetric positive definite matrices and is general enough to model water molecular diffusion in a variety of situations involving complex tissue geometry including single and multiple fiber bundle occurrences. We show that the diffusion MR signals and the probability distributions for positive definite matrix-valued random variables are related by the Laplace transform defined on the space of symmetric positive definite (SPD) matrices. Another interesting observation is that when the mixing distribution is parameterized by Wishart distributions, the resulting close form of Laplace transform leads to a Rigaut-type fractal expression. This Rigaut-type function exhibits the expected asymptotic power-law behavior and has been phenomenologically used in the past to explain the MR signal decay but never with a rigorous mathematical justification until the development of the proposed model. Furthermore, both the traditional diffusion tensor model and the multi-tensor model can be interpreted as special cases of this continuous mixture of tensors model. In tackling the challenging problem of multi-fiber reconstruction from the diffusion MR images, we further develop the mixture of Wisharts (MOW) model, as a natural parametrization of the desired tensor distribution function, to describe complex tissue structure involving multiple fiber populations. The multi-fiber reconstruction using the proposed MOW model essentially leads to an inverse problem. Computational methods for solving this inverse problem are investigated under a unified deconvolution framework which also includes several existing model-based approaches. Finally, the theoretical framework we have developed for modeling and reconstruction of diffusion weighted MRI has been tested on simulated data and real rat brain data sets. The comparisons with several competing methods empirically suggest that the proposed model combined with a non-negative least squares deconvolution method yields efficient and accurate solution for the multi-fiber reconstruction problem in the presence of intra voxel orientational heterogeneity. ( en ) Advisors/Committee Members: Vemuri, Baba C. (committee chair), Rangarajan, Anand (committee member), Banerjee, Arunava (committee member), Sahni, Sartaj (committee member), Lawphongpanich, Siriphong (committee member).

Subjects/Keywords: Diffusion coefficient; Diffusion magnetic resonance imaging; Fiber orientation; Imaging; Laplace transformation; Least squares; Magnetic resonance imaging; Matrices; Signals; Tensors; deconvolution, diffusion, dtmri, dwmri, hardi, mow, nnls

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

APA (6^th Edition):

Jian, B. (2009). Mathematical Modeling for Multi-Fiber Reconstruction from Diffusion-Weighted Magnetic Resonance Images. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0021691

Chicago Manual of Style (16^th Edition):

Jian, Bing. “Mathematical Modeling for Multi-Fiber Reconstruction from Diffusion-Weighted Magnetic Resonance Images.” 2009. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0021691.

MLA Handbook (7^th Edition):

Jian, Bing. “Mathematical Modeling for Multi-Fiber Reconstruction from Diffusion-Weighted Magnetic Resonance Images.” 2009. Web. 16 Dec 2019.

Vancouver:

Jian B. Mathematical Modeling for Multi-Fiber Reconstruction from Diffusion-Weighted Magnetic Resonance Images. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0021691.

Council of Science Editors:

Jian B. Mathematical Modeling for Multi-Fiber Reconstruction from Diffusion-Weighted Magnetic Resonance Images. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0021691

University of Florida

18. Liu, Meizhu. Total Bregman Divergence, a Robust Divergence Measure, and Its Applications.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2011, University of Florida

URL: http://ufdc.ufl.edu/UFE0043601

► Divergence measures provide a means to measure the pairwise dissimilarity between "objects", e.g., vectors and probability density functions (pdfs). Kullback-Leibler (KL) divergence and the square… (more)

Subjects/Keywords: Algorithms; Computer conferencing; Computer pattern recognition; Computer vision; Databases; Datasets; Information retrieval; Machine learning; Outliers; Tensors; boosting – bregman – divergence – dti – metric – robust

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

APA (6^th Edition):

Liu, M. (2011). Total Bregman Divergence, a Robust Divergence Measure, and Its Applications. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043601

Chicago Manual of Style (16^th Edition):

Liu, Meizhu. “Total Bregman Divergence, a Robust Divergence Measure, and Its Applications.” 2011. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0043601.

MLA Handbook (7^th Edition):

Liu, Meizhu. “Total Bregman Divergence, a Robust Divergence Measure, and Its Applications.” 2011. Web. 16 Dec 2019.

Vancouver:

Liu M. Total Bregman Divergence, a Robust Divergence Measure, and Its Applications. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0043601.

Council of Science Editors:

Liu M. Total Bregman Divergence, a Robust Divergence Measure, and Its Applications. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043601

University of Florida

19. Kodipaka, Santhosh. A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2009, University of Florida

URL: http://ufdc.ufl.edu/UFE0024624

► Several pattern recognition problems in computer vision and medical diagnosis can be posed in the general framework of supervised learning. However, the high-dimensionality of the… (more)

Subjects/Keywords: Conic sections; Datasets; Discriminants; Eggshells; Epilepsy; Hyperplanes; Hyperspheres; Learning; Machine learning; Polynomials; boundaries, class, classifier, concept, conic, constraints, design, discriminant, eccentricity, evaluation, geometry, large, learning, machine, margin, nonlinear, sections, stiffness

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

APA (6^th Edition):

Kodipaka, S. (2009). A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0024624

Chicago Manual of Style (16^th Edition):

Kodipaka, Santhosh. “A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms.” 2009. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0024624.

MLA Handbook (7^th Edition):

Kodipaka, Santhosh. “A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms.” 2009. Web. 16 Dec 2019.

Vancouver:

Kodipaka S. A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0024624.

Council of Science Editors:

Kodipaka S. A Novel Conic Section Classifier with Tractable Geometric Learning Algorithms. [Doctoral Dissertation]. University of Florida; 2009. Available from: http://ufdc.ufl.edu/UFE0024624

University of Florida

20. Xie, Yuchen. Geometric Approaches to Group-Wise Image Analysis.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

URL: http://ufdc.ufl.edu/UFE0044361

► Group-wise analysis of a set of images is a fundamental problem in computer vision and medical image analysis. A structured representation of the image collection… (more)

Subjects/Keywords: Algorithms; Computer vision; Datasets; Distance functions; Factorization; Image analysis; Image rotation; Matrices; Riemann manifold; Tensors; image

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

APA (6^th Edition):

Xie, Y. (2012). Geometric Approaches to Group-Wise Image Analysis. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044361

Chicago Manual of Style (16^th Edition):

Xie, Yuchen. “Geometric Approaches to Group-Wise Image Analysis.” 2012. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0044361.

MLA Handbook (7^th Edition):

Xie, Yuchen. “Geometric Approaches to Group-Wise Image Analysis.” 2012. Web. 16 Dec 2019.

Vancouver:

Xie Y. Geometric Approaches to Group-Wise Image Analysis. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0044361.

Council of Science Editors:

Xie Y. Geometric Approaches to Group-Wise Image Analysis. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044361

University of Florida

21. Chi, Yu-Tseh. Block, Group, and Affine Regularized Sparse Coding and Dictionary Learning.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2013, University of Florida

URL: http://ufdc.ufl.edu/UFE0044995

► I first propose a novel approach for sparse coding that further improves upon the sparse representation-based classification (SRC) framework. This proposed framework, affine constrained group… (more)

▼ I first propose a novel approach for sparse coding that further improves upon the sparse representation-based classification (SRC) framework. This proposed framework, affine constrained group sparse coding, extends the current SRC framework to classification problems with multiple inputs. Geometrically, the constrained group sparse coding essentially searches for the vector in the convex hull spanned by the input vectors that can best be sparse coded using the given dictionary. The resulting objective function is still convex and can be efficiently optimized using iterative block-coordinate descend scheme that is guaranteed to converge. Furthermore, I provide a form of sparse recovery result that guarantees, at least theoretically, that the classification performance of the constrained group sparse coding should be at least as good as the group sparse coding. While utilizing the affine combination of multiple input test samples can improve the performance of the conventional sparse representation-based classification framework, it is difficult to integrate this approach into a dictionary learning framework. Therefore, we propose to combine (1) imposing group structure on data (2) imposing block structure on the dictionary and (3) using different regularizer term to sparsely encode the data. We call this approach either block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding . Incorporating either one of them with the novel Intra-block Coherence Suppression Dictionary Learning (ICS-DL), which, as the name suggests, suppress the coherence of atoms within the same block, algorithm results in a novel dictionary learning framework. An important and distinguishing feature of the proposed framework is that all dictionary blocks are trained simultaneously with respect to each data group while the intra-block coherence being explicitly minimized as an important objective. We provide both empirical evidence and heuristic support for this feature that can be considered as a direct consequence of incorporating both the group structure for the input data and the block structure for the dictionary in the learning process. The optimization problems for both the dictionary learning and sparse coding can be solved efficiently using block-coordinate descent, and the details of the optimization algorithms are presented. In both parts of this work, the proposed methods are evaluated on several classification (supervised) and clustering (unsupervised) problems using well-known datasets. Favorable comparisons with state-of-the-art methods demonstrate the viability and validity of the proposed frameworks. ( en ) Advisors/Committee Members: Ho, Jeffrey Yih Chian (committee chair), Peters, Jorg (committee member), Vemuri, Baba C (committee member), Banerjee, Arunava (committee member), Burks, Thomas Francis (committee member).

Subjects/Keywords: Algorithms; Atoms; Computer vision; Datasets; Error rates; Image classification; Linear programming; Machine learning; Matrices; Signals; algorithm – coding – dictionary – learning – optimization – sparse

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

APA (6^th Edition):

Chi, Y. (2013). Block, Group, and Affine Regularized Sparse Coding and Dictionary Learning. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044995

Chicago Manual of Style (16^th Edition):

Chi, Yu-Tseh. “Block, Group, and Affine Regularized Sparse Coding and Dictionary Learning.” 2013. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0044995.

MLA Handbook (7^th Edition):

Chi, Yu-Tseh. “Block, Group, and Affine Regularized Sparse Coding and Dictionary Learning.” 2013. Web. 16 Dec 2019.

Vancouver:

Chi Y. Block, Group, and Affine Regularized Sparse Coding and Dictionary Learning. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0044995.

Council of Science Editors:

Chi Y. Block, Group, and Affine Regularized Sparse Coding and Dictionary Learning. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0044995

22. Wang, Yuanxiang. An Intrinsic Unscented Kalman Filter based Tracker for Manifold-Valued Data and Its Applications.

Degree: PhD, Electrical and Computer Engineering, 2015, University of Florida

URL: http://ufdc.ufl.edu/UFE0049628

► The unscented Kalman filter (UKF) is a nonlinear generalization of the well known Kalman filter, which is a linear dynamical system. It is commonly used… (more)

Subjects/Keywords: Blood vessels; Covariance; Distance functions; Kalman filters; Matrices; Plant roots; Retinal images; Signals; Tensors; Tubular structures; filter – intrinsic – kalman – manifold

…80 9 Abstract of Dissertation Presented to the Graduate School of the University of… …Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AN…

11 more images…

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APA (6^th Edition):

Wang, Y. (2015). An Intrinsic Unscented Kalman Filter based Tracker for Manifold-Valued Data and Its Applications. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049628

Chicago Manual of Style (16^th Edition):

Wang, Yuanxiang. “An Intrinsic Unscented Kalman Filter based Tracker for Manifold-Valued Data and Its Applications.” 2015. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0049628.

MLA Handbook (7^th Edition):

Wang, Yuanxiang. “An Intrinsic Unscented Kalman Filter based Tracker for Manifold-Valued Data and Its Applications.” 2015. Web. 16 Dec 2019.

Vancouver:

Wang Y. An Intrinsic Unscented Kalman Filter based Tracker for Manifold-Valued Data and Its Applications. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0049628.

Council of Science Editors:

Wang Y. An Intrinsic Unscented Kalman Filter based Tracker for Manifold-Valued Data and Its Applications. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049628

23. Qi, Shaoyu. Image Retargeting and Smart Projectors.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2013, University of Florida

URL: http://ufdc.ufl.edu/UFE0045374

► Image retargeting algorithms aim to adjust the image to the display screen whose aspect ratio or size differ significantly from that of the image by… (more)

Subjects/Keywords: Algorithms; Binocular vision; Carving; Computer vision; Data smoothing; Graphics; Pixels; Saliency; Seams; Stereo; applications – camera – image – retargeting – stereo

…57 8 Abstract of Dissertation Presented to the Graduate School of the University of… …Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy IMAGE…

10 more images…

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APA (6^th Edition):

Qi, S. (2013). Image Retargeting and Smart Projectors. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045374

Chicago Manual of Style (16^th Edition):

Qi, Shaoyu. “Image Retargeting and Smart Projectors.” 2013. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0045374.

MLA Handbook (7^th Edition):

Qi, Shaoyu. “Image Retargeting and Smart Projectors.” 2013. Web. 16 Dec 2019.

Vancouver:

Qi S. Image Retargeting and Smart Projectors. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0045374.

Council of Science Editors:

Qi S. Image Retargeting and Smart Projectors. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045374

University of Florida

24. Peter, Adrian. Information Geometry for Shape Analysis Probabilistic Models for Shape Matching and Indexing.

Degree: PhD, Electrical and Computer Engineering, 2008, University of Florida

URL: http://ufdc.ufl.edu/UFE0022484

► The study of shape analysis is a core field in computer vision. It is a fundamental building block of higher level cognitive tasks such as… (more)

▼ The study of shape analysis is a core field in computer vision. It is a fundamental building block of higher level cognitive tasks such as recognition. This research introduces novel approaches to basic shape analysis tasks, including shape matching and defining metrics for shape similarity. Our investigations into these methods yielded two supporting statistical tools that have general applicability outside the realm of shape analysis: a new wavelet density estimation procedure and a geometrically motivated model selection criterion to select the wavelet's multiscale decomposition levels. All of the derived techniques are theoretically grounded in the framework of information geometry. Information geometry is an emerging math discipline that applies differential geometry to space of probability distributions. This work will for the first time illustrate a systematic approach to applying information geometry to shape analysis. Our basic approach to shape analysis is simple: represent shapes as probability densities, then use the intrinsic geometry of the space of densities to establish geodesics between shapes. We can obtain valid intermediate densities (shapes) by walking along the geodesics and the length of the geodesic immediately gives us a similarity measure between shapes. We always assume an unstructured, point-set representation for the underlying shape. Hence, unlike many contemporary methods, there are no topological restrictions (like requiring shapes to be closed curves) on our shape models. We illustrate these concepts by using two types of models to represent the densities: Gaussian mixture models and wavelet densities. Our development of a wavelet-density, shape model also resulted in a new density estimation procedure. We expand the square root of the density in a multiscale, wavelet basis and then obtain a bona fide density by squaring the expansion. This new method estimates the coefficients of the wavelet expansion using a constrained maximum likelihood objective. It is shown that under this representation wavelet densities are essentially points on a unit hypersphere. The choice of the number of decomposition levels density estimation is determined using a model selection framework. We use the geometry of the space to apply the MDL criterion that selects the best model among set of competing ones by judiciously balancing a model's accuracy and complexity. ( en ) Advisors/Committee Members: Rangarajan, Anand (committee chair), Principe, Jose C. (committee member), Vemuri, Baba C. (committee member), Ho, Jeffrey (committee member).

Subjects/Keywords: Density; Density estimation; Distance functions; Estimators; Geometric shapes; Geometry; Hyperspheres; Landmarks; Maximum likelihood estimations; Parametric models; analysis, assignment, charvat, density, description, divergence, estimation, fisher, gaussian, geometry, havrda, hellinger, information, length, linear, matching, metric, minimum, mixture, model, rao, root, selection, shape, square, wavelets

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

APA (6^th Edition):

Peter, A. (2008). Information Geometry for Shape Analysis Probabilistic Models for Shape Matching and Indexing. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0022484

Chicago Manual of Style (16^th Edition):

Peter, Adrian. “Information Geometry for Shape Analysis Probabilistic Models for Shape Matching and Indexing.” 2008. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0022484.

MLA Handbook (7^th Edition):

Peter, Adrian. “Information Geometry for Shape Analysis Probabilistic Models for Shape Matching and Indexing.” 2008. Web. 16 Dec 2019.

Vancouver:

Peter A. Information Geometry for Shape Analysis Probabilistic Models for Shape Matching and Indexing. [Internet] [Doctoral dissertation]. University of Florida; 2008. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0022484.

Council of Science Editors:

Peter A. Information Geometry for Shape Analysis Probabilistic Models for Shape Matching and Indexing. [Doctoral Dissertation]. University of Florida; 2008. Available from: http://ufdc.ufl.edu/UFE0022484

University of Florida

25. Sun, Jiaqi. Sparse Recovery of Ensemble Average Propagators from Compressed Sensing of Diffusion MRI.

Degree: PhD, Computer Science - Computer and Information Science and Engineering, 2017, University of Florida

URL: http://ufdc.ufl.edu/UFE0050953

Subjects/Keywords: dmri; ml; sensing

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

APA (6^th Edition):

Sun, J. (2017). Sparse Recovery of Ensemble Average Propagators from Compressed Sensing of Diffusion MRI. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050953

Chicago Manual of Style (16^th Edition):

Sun, Jiaqi. “Sparse Recovery of Ensemble Average Propagators from Compressed Sensing of Diffusion MRI.” 2017. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0050953.

MLA Handbook (7^th Edition):

Sun, Jiaqi. “Sparse Recovery of Ensemble Average Propagators from Compressed Sensing of Diffusion MRI.” 2017. Web. 16 Dec 2019.

Vancouver:

Sun J. Sparse Recovery of Ensemble Average Propagators from Compressed Sensing of Diffusion MRI. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0050953.

Council of Science Editors:

Sun J. Sparse Recovery of Ensemble Average Propagators from Compressed Sensing of Diffusion MRI. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0050953

University of Florida

26. Lord, Nicholas Andrew. Shape Analysis via Unified Segmentation, Smoothing, and Registration of Riemannian Structures.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2007, University of Florida

URL: http://ufdc.ufl.edu/UFE0021640

► Locality is an important but oft-ignored aspect of shape asymmetry quantification, and segmentation is one method by which to make the locality of an analysis… (more)

Subjects/Keywords: Asymmetry; Coordinate systems; Curvature; Data smoothing; Distance functions; Hippocampus; Image processing; Maps; Matrices; Pixels; asymmetry, deformation, geometry, hippocampus, intrinsic, level, multiple, mumford, nonrigid, parcellation, reconstruction, registration, riemannian, segmentation, set, shah, simultaneous, smoothing, surface, variational

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APA (6^th Edition):

Lord, N. A. (2007). Shape Analysis via Unified Segmentation, Smoothing, and Registration of Riemannian Structures. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0021640

Chicago Manual of Style (16^th Edition):

Lord, Nicholas Andrew. “Shape Analysis via Unified Segmentation, Smoothing, and Registration of Riemannian Structures.” 2007. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0021640.

MLA Handbook (7^th Edition):

Lord, Nicholas Andrew. “Shape Analysis via Unified Segmentation, Smoothing, and Registration of Riemannian Structures.” 2007. Web. 16 Dec 2019.

Vancouver:

Lord NA. Shape Analysis via Unified Segmentation, Smoothing, and Registration of Riemannian Structures. [Internet] [Doctoral dissertation]. University of Florida; 2007. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0021640.

Council of Science Editors:

Lord NA. Shape Analysis via Unified Segmentation, Smoothing, and Registration of Riemannian Structures. [Doctoral Dissertation]. University of Florida; 2007. Available from: http://ufdc.ufl.edu/UFE0021640

University of Florida

27. Chen, Ting. Groupwise Analysis of Neuroimaging Data.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2011, University of Florida

URL: http://ufdc.ufl.edu/UFE0043689

This dissertation proposes techniques for automatic brain structure segmentation Advisors/Committee Members: Vemuri, Baba C (committee chair), Banerjee, Arunava (committee member), Ho, Jeffrey (committee member), Presnell, Brett D (committee member).

Subjects/Keywords: Algorithms; Atlases; Computer vision; Cumulative distribution functions; Datasets; Epilepsy; Geometric shapes; Ground truth; Hippocampus; Learning; analysis – atlas – classification – complex – epilepsy – groupwise – hippocampus – registration – segmentation – shape

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

APA (6^th Edition):

Chen, T. (2011). Groupwise Analysis of Neuroimaging Data. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043689

Chicago Manual of Style (16^th Edition):

Chen, Ting. “Groupwise Analysis of Neuroimaging Data.” 2011. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0043689.

MLA Handbook (7^th Edition):

Chen, Ting. “Groupwise Analysis of Neuroimaging Data.” 2011. Web. 16 Dec 2019.

Vancouver:

Chen T. Groupwise Analysis of Neuroimaging Data. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0043689.

Council of Science Editors:

Chen T. Groupwise Analysis of Neuroimaging Data. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043689

University of Florida

28. Athawale, Tushar M. Quantification and Visualization of Spatial Uncertainty in Isosurfaces for Parametric and Nonparametric Noise Models.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2015, University of Florida

URL: http://ufdc.ufl.edu/UFE0047747

Isosurfaces are the key entities in visualization, and recent research has focused Advisors/Committee Members: ENTEZARI,ALIREZA (committee chair), VEMURI,BABA C (committee member), BANERJEE,ARUNAVA (committee member), AYTUG,HALDUN (committee member).

Subjects/Keywords: Bandwidth; Datasets; Density estimation; Linear interpolation; Nonparametric models; Parallelograms; Random variables; Statistical discrepancies; Topology; Vertices; geometry – isosurfaces – topology – uncertainty – visualization

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

APA (6^th Edition):

Athawale, T. M. (2015). Quantification and Visualization of Spatial Uncertainty in Isosurfaces for Parametric and Nonparametric Noise Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0047747

Chicago Manual of Style (16^th Edition):

Athawale, Tushar M. “Quantification and Visualization of Spatial Uncertainty in Isosurfaces for Parametric and Nonparametric Noise Models.” 2015. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0047747.

MLA Handbook (7^th Edition):

Athawale, Tushar M. “Quantification and Visualization of Spatial Uncertainty in Isosurfaces for Parametric and Nonparametric Noise Models.” 2015. Web. 16 Dec 2019.

Vancouver:

Athawale TM. Quantification and Visualization of Spatial Uncertainty in Isosurfaces for Parametric and Nonparametric Noise Models. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0047747.

Council of Science Editors:

Athawale TM. Quantification and Visualization of Spatial Uncertainty in Isosurfaces for Parametric and Nonparametric Noise Models. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0047747

University of Florida

29. Garita, Barbara. Guided Surgery Using Rapid Prototyping Patient-Specific Guides A Methodology to Quantify Mechanical Stability and Uniqueness of Fit.

Degree: PhD, Biomedical Engineering, 2007, University of Florida

URL: http://ufdc.ufl.edu/UFE0012401

► Rapid prototyping technology (3-dimensional printing) in conjunction with the virtual manipulation of computer-generated 3-D anatomical models rendered from diagnostic images (CT and MRI) is a… (more)

▼ Rapid prototyping technology (3-dimensional printing) in conjunction with the virtual manipulation of computer-generated 3-D anatomical models rendered from diagnostic images (CT and MRI) is a novel technique for directing image-guided surgery. By incorporating a graphical user interface, the surgeon can plan an intracranial surgical procedure by fabricating a patient-specific reference frame (mask-like facial frame) through the use of rapid prototyping technology. Early testing of these patient-specific frames revealed the necessity to include a step prior to fabrication to analyze the design of the frame for stability of fit. In response to this need, the objective in the present investigation is to develop a method that numerically assesses the frame?s stability. The approach was to create a finite-element simulation that models the patient-specific reference frame as a rigid layer composed of linear tetrahedron elements under a deformable material simulated by a bed of linear spring elements. The program analyzes the displacement of the rigid layer by simulating external forces distributed around its perimeter. The stability of fit is characterized by the response of the linear springs. Three patients with different facial anatomical features were selected as training data for testing the simulation program. For each case two patient-specific reference frames, one that involves more surface area and surface variations such as contours of the nose bridge and another that consists of less surface area and less surface variations, were built with Rapid prototyping based on the patient?s 3-D anatomical facial models. In all three cases the methodology correlated a stability score with the stability of the real reference frames. In addition two more cases were evaluated; the first did not incorporate the facial structures and was design to hold onto the top of the head in a cap-like manner, the second case was a virtually created half sphere. In both cases it was expected to find no a unique fit, and correspondingly the simulation found both cases to be unstable. ( en ) Advisors/Committee Members: Bova, Frank J. (committee chair), Friedman, William A. (committee member), Gilland, David R. (committee member), Vemuri, Baba C. (committee member), Kim, Nam Ho (committee member).

Subjects/Keywords: Asians; Boxes; Coordinate systems; Histograms; Infants; Linear programming; Stiffness; Stiffness matrix; Three dimensional modeling; Trusses; contact, fem, guided, image, itk, mechanics, prototyping, rapid, surgery, vtk

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

APA (6^th Edition):

Garita, B. (2007). Guided Surgery Using Rapid Prototyping Patient-Specific Guides A Methodology to Quantify Mechanical Stability and Uniqueness of Fit. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0012401

Chicago Manual of Style (16^th Edition):

Garita, Barbara. “Guided Surgery Using Rapid Prototyping Patient-Specific Guides A Methodology to Quantify Mechanical Stability and Uniqueness of Fit.” 2007. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0012401.

MLA Handbook (7^th Edition):

Garita, Barbara. “Guided Surgery Using Rapid Prototyping Patient-Specific Guides A Methodology to Quantify Mechanical Stability and Uniqueness of Fit.” 2007. Web. 16 Dec 2019.

Vancouver:

Garita B. Guided Surgery Using Rapid Prototyping Patient-Specific Guides A Methodology to Quantify Mechanical Stability and Uniqueness of Fit. [Internet] [Doctoral dissertation]. University of Florida; 2007. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0012401.

Council of Science Editors:

Garita B. Guided Surgery Using Rapid Prototyping Patient-Specific Guides A Methodology to Quantify Mechanical Stability and Uniqueness of Fit. [Doctoral Dissertation]. University of Florida; 2007. Available from: http://ufdc.ufl.edu/UFE0012401

University of Florida

30. Myles, Ashish. Curvature-Continuous Bicubic Subdivision Surfaces for Polar Configurations.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2008, University of Florida

URL: http://ufdc.ufl.edu/UFE0022832

► Subdivision surfaces are popular in animation as a way of smoothing coarse control meshes. On the other hand, the Computer-Aided Design (CAD) industry typically prefers… (more)

Subjects/Keywords: Approximation; Computer aided design; Computer graphics; Connectivity; Curvature; Eigenvalues; Eigenvectors; Grid refinement; Polynomials; Vertices; bicubic, c2, configuration, connectivity, continuous, curvature, mesh, non, polar, quad, stationary, subdivision, surface

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

APA (6^th Edition):

Myles, A. (2008). Curvature-Continuous Bicubic Subdivision Surfaces for Polar Configurations. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0022832

Chicago Manual of Style (16^th Edition):

Myles, Ashish. “Curvature-Continuous Bicubic Subdivision Surfaces for Polar Configurations.” 2008. Doctoral Dissertation, University of Florida. Accessed December 16, 2019. http://ufdc.ufl.edu/UFE0022832.

MLA Handbook (7^th Edition):

Myles, Ashish. “Curvature-Continuous Bicubic Subdivision Surfaces for Polar Configurations.” 2008. Web. 16 Dec 2019.

Vancouver:

Myles A. Curvature-Continuous Bicubic Subdivision Surfaces for Polar Configurations. [Internet] [Doctoral dissertation]. University of Florida; 2008. [cited 2019 Dec 16]. Available from: http://ufdc.ufl.edu/UFE0022832.

Council of Science Editors:

Myles A. Curvature-Continuous Bicubic Subdivision Surfaces for Polar Configurations. [Doctoral Dissertation]. University of Florida; 2008. Available from: http://ufdc.ufl.edu/UFE0022832

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