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You searched for subject:(low rank representation). Showing records 1 – 9 of 9 total matches.

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University of Vermont

1. Zhang, Yu. Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar.

Degree: PhD, Electrical Engineering, 2017, University of Vermont

  Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection,… (more)

Subjects/Keywords: back-projection algorithm; entropy; ground penetrating radar; low-rank and sparse representation; multistatic radar; signal processing; Electrical and Electronics

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APA (6th Edition):

Zhang, Y. (2017). Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/799

Chicago Manual of Style (16th Edition):

Zhang, Yu. “Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar.” 2017. Doctoral Dissertation, University of Vermont. Accessed December 12, 2019. https://scholarworks.uvm.edu/graddis/799.

MLA Handbook (7th Edition):

Zhang, Yu. “Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar.” 2017. Web. 12 Dec 2019.

Vancouver:

Zhang Y. Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar. [Internet] [Doctoral dissertation]. University of Vermont; 2017. [cited 2019 Dec 12]. Available from: https://scholarworks.uvm.edu/graddis/799.

Council of Science Editors:

Zhang Y. Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar. [Doctoral Dissertation]. University of Vermont; 2017. Available from: https://scholarworks.uvm.edu/graddis/799


University of Vermont

2. Zhang, Yu. Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar.

Degree: PhD, Electrical Engineering, 2017, University of Vermont

  Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection,… (more)

Subjects/Keywords: back-projection algorithm; entropy; ground penetrating radar; low-rank and sparse representation; multistatic radar; signal processing; Electrical and Electronics

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

APA (6th Edition):

Zhang, Y. (2017). Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/919

Chicago Manual of Style (16th Edition):

Zhang, Yu. “Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar.” 2017. Doctoral Dissertation, University of Vermont. Accessed December 12, 2019. https://scholarworks.uvm.edu/graddis/919.

MLA Handbook (7th Edition):

Zhang, Yu. “Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar.” 2017. Web. 12 Dec 2019.

Vancouver:

Zhang Y. Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar. [Internet] [Doctoral dissertation]. University of Vermont; 2017. [cited 2019 Dec 12]. Available from: https://scholarworks.uvm.edu/graddis/919.

Council of Science Editors:

Zhang Y. Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar. [Doctoral Dissertation]. University of Vermont; 2017. Available from: https://scholarworks.uvm.edu/graddis/919


University of Central Florida

3. Oreifej, Omar. Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition.

Degree: 2013, University of Central Florida

 In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these… (more)

Subjects/Keywords: low rank representation; low rank; sparse representation; sparse; activity recognition; turbulence mitigation; video denoising; complex event recognition; nuclear norm; augmented lagrange multiplier; camera motion estimation; trecvid; hoha; water waves; rank; trajectories; particle advection; registration; decomposition; moving object detection; background subtraction; atmospheric turbulence; Computer Engineering; Engineering; Dissertations, Academic  – Engineering and Computer Science, Engineering and Computer Science  – Dissertations, Academic

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APA (6th Edition):

Oreifej, O. (2013). Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition. (Doctoral Dissertation). University of Central Florida. Retrieved from https://stars.library.ucf.edu/etd/2569

Chicago Manual of Style (16th Edition):

Oreifej, Omar. “Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition.” 2013. Doctoral Dissertation, University of Central Florida. Accessed December 12, 2019. https://stars.library.ucf.edu/etd/2569.

MLA Handbook (7th Edition):

Oreifej, Omar. “Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition.” 2013. Web. 12 Dec 2019.

Vancouver:

Oreifej O. Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition. [Internet] [Doctoral dissertation]. University of Central Florida; 2013. [cited 2019 Dec 12]. Available from: https://stars.library.ucf.edu/etd/2569.

Council of Science Editors:

Oreifej O. Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition. [Doctoral Dissertation]. University of Central Florida; 2013. Available from: https://stars.library.ucf.edu/etd/2569

4. Cordolino Sobral, Andrews. Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs.

Degree: Docteur es, Informatique et applications, 2017, La Rochelle

Dans ce manuscrit de thèse, nous introduisons les avancées récentes sur la décomposition en matrices (et tenseurs) de rang faible et parcimonieuse ainsi que les… (more)

Subjects/Keywords: Détection d’objets mobiles; Soustraction de fond; ACP robuste; Décomposition en rang faible et parcimonieuse; Moving object detection; Background/foreground separation; Low-rank and sparse representation; Matrix decomposition tensor factorization

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APA (6th Edition):

Cordolino Sobral, A. (2017). Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs. (Doctoral Dissertation). La Rochelle. Retrieved from http://www.theses.fr/2017LAROS007

Chicago Manual of Style (16th Edition):

Cordolino Sobral, Andrews. “Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs.” 2017. Doctoral Dissertation, La Rochelle. Accessed December 12, 2019. http://www.theses.fr/2017LAROS007.

MLA Handbook (7th Edition):

Cordolino Sobral, Andrews. “Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs.” 2017. Web. 12 Dec 2019.

Vancouver:

Cordolino Sobral A. Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs. [Internet] [Doctoral dissertation]. La Rochelle; 2017. [cited 2019 Dec 12]. Available from: http://www.theses.fr/2017LAROS007.

Council of Science Editors:

Cordolino Sobral A. Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors : Détection d’objets mobiles dans des vidéos par décomposition en rang faible et parcimonieuse : de matrices à tenseurs. [Doctoral Dissertation]. La Rochelle; 2017. Available from: http://www.theses.fr/2017LAROS007


Brno University of Technology

5. Hrbáček, Radek. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci .

Degree: 2013, Brno University of Technology

 Tato práce se věnuje problematice nukleární magnetické rezonance, zejména spektroskopii a spektroskopickému zobrazování, řídké reprezentaci signálů a aproximaci s nízkou hodností. Využití spektroskopických zobrazovacích metod… (more)

Subjects/Keywords: nukleární magnetická rezonance; spektroskopie; spektroskopické zobrazování; řídká reprezentace signálů; komprimované snímání; aproximace s nízkou hodností; nuclear magnetic resonance; spectroscopy; spectroscopy imaging; sparse signal representation; compressed sensing; low-rank approximation

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APA (6th Edition):

Hrbáček, R. (2013). Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/26517

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hrbáček, Radek. “Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci .” 2013. Thesis, Brno University of Technology. Accessed December 12, 2019. http://hdl.handle.net/11012/26517.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hrbáček, Radek. “Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci .” 2013. Web. 12 Dec 2019.

Vancouver:

Hrbáček R. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci . [Internet] [Thesis]. Brno University of Technology; 2013. [cited 2019 Dec 12]. Available from: http://hdl.handle.net/11012/26517.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hrbáček R. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci . [Thesis]. Brno University of Technology; 2013. Available from: http://hdl.handle.net/11012/26517

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

6. Wen, Bihan. Adaptive nonlocal and structured sparse signal modeling and applications.

Degree: PhD, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. Many applications such as… (more)

Subjects/Keywords: machine learning; image restoration; transform learning; computational imaging; denoising; inpainting; magnetic resonance imaging; video denoising; online learning; overcomplete model; rotation invariant; medical imaging; sparse coding; sparse representation; low-rank model; joint optimization

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APA (6th Edition):

Wen, B. (2018). Adaptive nonlocal and structured sparse signal modeling and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102464

Chicago Manual of Style (16th Edition):

Wen, Bihan. “Adaptive nonlocal and structured sparse signal modeling and applications.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 12, 2019. http://hdl.handle.net/2142/102464.

MLA Handbook (7th Edition):

Wen, Bihan. “Adaptive nonlocal and structured sparse signal modeling and applications.” 2018. Web. 12 Dec 2019.

Vancouver:

Wen B. Adaptive nonlocal and structured sparse signal modeling and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Dec 12]. Available from: http://hdl.handle.net/2142/102464.

Council of Science Editors:

Wen B. Adaptive nonlocal and structured sparse signal modeling and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102464


Brno University of Technology

7. Kolbábková, Anežka. Algoritmy doplňování chybějících dat v audiosignálech .

Degree: 2014, Brno University of Technology

 Tato práce se zabývá doplňováním chybějících dat do audio signálů a algoritmy řešícími problém založenými na řídké reprezentaci audio signálu. Práce se zaměřuje na některé… (more)

Subjects/Keywords: řídká reprezentace; doplňování chybějících dat do audio signálů; matce s nízkou hodností; framy; totální variace; diskrétní Gaborova tranformace; sparse representation; audio inpainting; low-rank matrix; frames; total variation; Discrete Gabor Transform

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

APA (6th Edition):

Kolbábková, A. (2014). Algoritmy doplňování chybějících dat v audiosignálech . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/33612

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kolbábková, Anežka. “Algoritmy doplňování chybějících dat v audiosignálech .” 2014. Thesis, Brno University of Technology. Accessed December 12, 2019. http://hdl.handle.net/11012/33612.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kolbábková, Anežka. “Algoritmy doplňování chybějících dat v audiosignálech .” 2014. Web. 12 Dec 2019.

Vancouver:

Kolbábková A. Algoritmy doplňování chybějících dat v audiosignálech . [Internet] [Thesis]. Brno University of Technology; 2014. [cited 2019 Dec 12]. Available from: http://hdl.handle.net/11012/33612.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kolbábková A. Algoritmy doplňování chybějících dat v audiosignálech . [Thesis]. Brno University of Technology; 2014. Available from: http://hdl.handle.net/11012/33612

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

8. Mangová, Marie. Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance .

Degree: 2014, Brno University of Technology

 Perfúzní zobrazování pomocí magnetické rezonance je lékařská diagnostická metoda, která se zdá být v dnešní době velmi slibnou. Tato práce se zabývá řídkými reprezentacemi, rekonstrukcí… (more)

Subjects/Keywords: komprimované snímání; perfúzní zobrazování; magnetická rezonance; řídká reprezentace signálů; rekonstrukce matic s nízkou hodností; proximální gradientní metoda; compressed sensing; perfusion imaging; magnetic resonance; sparse representation of signals; low-rank matrix recovery; proximal gradient method

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APA (6th Edition):

Mangová, M. (2014). Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/33620

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mangová, Marie. “Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance .” 2014. Thesis, Brno University of Technology. Accessed December 12, 2019. http://hdl.handle.net/11012/33620.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mangová, Marie. “Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance .” 2014. Web. 12 Dec 2019.

Vancouver:

Mangová M. Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance . [Internet] [Thesis]. Brno University of Technology; 2014. [cited 2019 Dec 12]. Available from: http://hdl.handle.net/11012/33620.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mangová M. Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance . [Thesis]. Brno University of Technology; 2014. Available from: http://hdl.handle.net/11012/33620

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. LU CANYI. STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS.

Degree: 2017, National University of Singapore

Subjects/Keywords: nonconvex low-rank minimization, block diagonal representation; tensor robust PCA; unified framework of ADMM

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APA (6th Edition):

CANYI, L. (2017). STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/136504

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

CANYI, LU. “STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS.” 2017. Thesis, National University of Singapore. Accessed December 12, 2019. http://scholarbank.nus.edu.sg/handle/10635/136504.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

CANYI, LU. “STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS.” 2017. Web. 12 Dec 2019.

Vancouver:

CANYI L. STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS. [Internet] [Thesis]. National University of Singapore; 2017. [cited 2019 Dec 12]. Available from: http://scholarbank.nus.edu.sg/handle/10635/136504.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

CANYI L. STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS. [Thesis]. National University of Singapore; 2017. Available from: http://scholarbank.nus.edu.sg/handle/10635/136504

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.