subspace selection
Recently Published Documents


TOTAL DOCUMENTS

71
(FIVE YEARS 7)

H-INDEX

12
(FIVE YEARS 0)

2021 ◽  
Vol 104 (5) ◽  
Author(s):  
A. M. Romero ◽  
J. M. Yao ◽  
B. Bally ◽  
T. R. Rodríguez ◽  
J. Engel


2021 ◽  
Author(s):  
Kaustav Chatterjee ◽  
Kaveri Mahapatra ◽  
Nilanjan Ray Chaudhuri
Keyword(s):  


2021 ◽  
Author(s):  
J Kosinka ◽  
M Sabin ◽  
Neil Dodgson

Our goal is to find subdivision rules at creases in arbitrary degree subdivision for piece-wise polynomial curves, but without introducing new control points e.g. by knot insertion. Crease rules are well understood for low degree (cubic and lower) curves. We compare three main approaches: knot insertion, ghost points, and modifying subdivision rules. While knot insertion and ghost points work for arbitrary degrees for B-splines, these methods introduce unnecessary (ghost) control points. The situation is not so simple in modifying subdivision rules. Based on subdivision and subspace selection matrices, a novel approach to finding boundary and sharp subdivision rules that generalises to any degree is presented. Our approach leads to new higher-degree polynomial subdivision schemes with crease control without introducing new control points. © 2014 The Authors. Published by Elsevier Inc.



2021 ◽  
Author(s):  
J Kosinka ◽  
M Sabin ◽  
Neil Dodgson

Our goal is to find subdivision rules at creases in arbitrary degree subdivision for piece-wise polynomial curves, but without introducing new control points e.g. by knot insertion. Crease rules are well understood for low degree (cubic and lower) curves. We compare three main approaches: knot insertion, ghost points, and modifying subdivision rules. While knot insertion and ghost points work for arbitrary degrees for B-splines, these methods introduce unnecessary (ghost) control points. The situation is not so simple in modifying subdivision rules. Based on subdivision and subspace selection matrices, a novel approach to finding boundary and sharp subdivision rules that generalises to any degree is presented. Our approach leads to new higher-degree polynomial subdivision schemes with crease control without introducing new control points. © 2014 The Authors. Published by Elsevier Inc.



2021 ◽  
Author(s):  
Azadeh Fakhrzadeh

In this thesis, the problem of data denoising is considered and a new data denoising method is developed. This approach is an adaptive, data-driven thresholding method that is based on Minimum Noiseless Description Length (MNDL). MNDL is an approach to subspace selection which estimates bounds on the desired Mean Square Error (MSE). The subspace minimizing these bounds is chosen as the optimum one. In this research, we explore application of MNDL Subspace Selection (MNDL-SS) as a thresholding method. Although the basic idea and desired criterion of MNDL thresholding and MNDL-SS are the same, the challenges in calculation of the desired criterion in MNDL thresholding are very different. In MNDL-SS, the additive noise effects are in the form of samples of a Chi-Square random variable. However, this assumption does not hold for MNDL thresholding anymore. In this research, we developed a new method for calculation of the desired criterion based on characteristics of noise in thresholding. Our simulation results show that MNDL thresholding outperforms the compared methods. In this thesis, we also explore the area of image denoising. In image denoising approaches, some properties of the image are considered. One of the well known image denoising methods, that outperforms other methods, is BayesShrink. We compare our method with BayesShrink. We show that the results of MNDS thresholding are comparable with BayesShrink in our simulations.



2021 ◽  
Author(s):  
Azadeh Fakhrzadeh

In this thesis, the problem of data denoising is considered and a new data denoising method is developed. This approach is an adaptive, data-driven thresholding method that is based on Minimum Noiseless Description Length (MNDL). MNDL is an approach to subspace selection which estimates bounds on the desired Mean Square Error (MSE). The subspace minimizing these bounds is chosen as the optimum one. In this research, we explore application of MNDL Subspace Selection (MNDL-SS) as a thresholding method. Although the basic idea and desired criterion of MNDL thresholding and MNDL-SS are the same, the challenges in calculation of the desired criterion in MNDL thresholding are very different. In MNDL-SS, the additive noise effects are in the form of samples of a Chi-Square random variable. However, this assumption does not hold for MNDL thresholding anymore. In this research, we developed a new method for calculation of the desired criterion based on characteristics of noise in thresholding. Our simulation results show that MNDL thresholding outperforms the compared methods. In this thesis, we also explore the area of image denoising. In image denoising approaches, some properties of the image are considered. One of the well known image denoising methods, that outperforms other methods, is BayesShrink. We compare our method with BayesShrink. We show that the results of MNDS thresholding are comparable with BayesShrink in our simulations.



2021 ◽  
pp. 106733
Author(s):  
Mahboobeh Riahi-Madvar ◽  
Ahmad Akbari Azirani ◽  
Babak Nasersharif ◽  
Bijan Raahemi


2020 ◽  
Author(s):  
J Kosinka ◽  
M Sabin ◽  
Neil Dodgson

Our goal is to find subdivision rules at creases in arbitrary degree subdivision for piece-wise polynomial curves, but without introducing new control points e.g. by knot insertion. Crease rules are well understood for low degree (cubic and lower) curves. We compare three main approaches: knot insertion, ghost points, and modifying subdivision rules. While knot insertion and ghost points work for arbitrary degrees for B-splines, these methods introduce unnecessary (ghost) control points. The situation is not so simple in modifying subdivision rules. Based on subdivision and subspace selection matrices, a novel approach to finding boundary and sharp subdivision rules that generalises to any degree is presented. Our approach leads to new higher-degree polynomial subdivision schemes with crease control without introducing new control points. © 2014 The Authors. Published by Elsevier Inc.



2020 ◽  
Author(s):  
J Kosinka ◽  
M Sabin ◽  
Neil Dodgson

Our goal is to find subdivision rules at creases in arbitrary degree subdivision for piece-wise polynomial curves, but without introducing new control points e.g. by knot insertion. Crease rules are well understood for low degree (cubic and lower) curves. We compare three main approaches: knot insertion, ghost points, and modifying subdivision rules. While knot insertion and ghost points work for arbitrary degrees for B-splines, these methods introduce unnecessary (ghost) control points. The situation is not so simple in modifying subdivision rules. Based on subdivision and subspace selection matrices, a novel approach to finding boundary and sharp subdivision rules that generalises to any degree is presented. Our approach leads to new higher-degree polynomial subdivision schemes with crease control without introducing new control points. © 2014 The Authors. Published by Elsevier Inc.



2020 ◽  
Vol 11 (4) ◽  
pp. 3346-3358 ◽  
Author(s):  
Kaustav Chatterjee ◽  
Kaveri Mahapatra ◽  
Nilanjan Ray Chaudhuri
Keyword(s):  


Sign in / Sign up

Export Citation Format

Share Document