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You searched for +publisher:"Penn State University" +contributor:("William Evan Higgins, Committee Member"). Showing records 1 – 16 of 16 total matches.

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Penn State University

1. Wang, Tianhe. 3D Human pose estimation on Taiji sequence.

Degree: 2018, Penn State University

 Human pose estimation is a task that has been extensively studied in the field of computer vision. Given a video frame or an image, a… (more)

Subjects/Keywords: Pose estimation; Neural Networks; Motion Capture; Regression

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APA (6th Edition):

Wang, T. (2018). 3D Human pose estimation on Taiji sequence. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15682tzw43

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):

Wang, Tianhe. “3D Human pose estimation on Taiji sequence.” 2018. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/15682tzw43.

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

MLA Handbook (7th Edition):

Wang, Tianhe. “3D Human pose estimation on Taiji sequence.” 2018. Web. 30 Oct 2020.

Vancouver:

Wang T. 3D Human pose estimation on Taiji sequence. [Internet] [Thesis]. Penn State University; 2018. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/15682tzw43.

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

Council of Science Editors:

Wang T. 3D Human pose estimation on Taiji sequence. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15682tzw43

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


Penn State University

2. Li, Mu. video hashing: algorithm design and performane analysis.

Degree: 2014, Penn State University

 The fast growth of video data on Internet is making a big challenge on present near-duplicate detection (NDD) methods. Furthermore, the emergence of websites such… (more)

Subjects/Keywords: video hashing; video fingerprinting; near-duplicate detection; anti-piracy search

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APA (6th Edition):

Li, M. (2014). video hashing: algorithm design and performane analysis. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22621

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):

Li, Mu. “video hashing: algorithm design and performane analysis.” 2014. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/22621.

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

MLA Handbook (7th Edition):

Li, Mu. “video hashing: algorithm design and performane analysis.” 2014. Web. 30 Oct 2020.

Vancouver:

Li M. video hashing: algorithm design and performane analysis. [Internet] [Thesis]. Penn State University; 2014. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/22621.

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

Council of Science Editors:

Li M. video hashing: algorithm design and performane analysis. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22621

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


Penn State University

3. Mcpheron, Benjamin David. Flux Regulation in Powered Magnets: Enabling Magnetic Resonance Experiments with Pulsed Field Gradients.

Degree: 2014, Penn State University

 High magnetic fields can significantly improve the resolution and sensitivity of nuclear magnetic resonance (NMR) spectroscopy measurements, which presents exciting research opportunities in areas of… (more)

Subjects/Keywords: Field fluctuations; Cascade Regulation; Field Regulation; NMR; Powered Magnets; Pulsed Field Gradients

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APA (6th Edition):

Mcpheron, B. D. (2014). Flux Regulation in Powered Magnets: Enabling Magnetic Resonance Experiments with Pulsed Field Gradients. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22790

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):

Mcpheron, Benjamin David. “Flux Regulation in Powered Magnets: Enabling Magnetic Resonance Experiments with Pulsed Field Gradients.” 2014. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/22790.

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

MLA Handbook (7th Edition):

Mcpheron, Benjamin David. “Flux Regulation in Powered Magnets: Enabling Magnetic Resonance Experiments with Pulsed Field Gradients.” 2014. Web. 30 Oct 2020.

Vancouver:

Mcpheron BD. Flux Regulation in Powered Magnets: Enabling Magnetic Resonance Experiments with Pulsed Field Gradients. [Internet] [Thesis]. Penn State University; 2014. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/22790.

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

Council of Science Editors:

Mcpheron BD. Flux Regulation in Powered Magnets: Enabling Magnetic Resonance Experiments with Pulsed Field Gradients. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22790

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


Penn State University

4. Morgan, Jacob. Data Fusion for Additive Manufacturing Process Inspection.

Degree: 2019, Penn State University

 In-situ monitoring of the powder bed fusion additive manufacturing (PBFAM) process is a rapidly expanding area of interest because it offers insight into process physics… (more)

Subjects/Keywords: data fusion; additive manufacturing; computer vision

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APA (6th Edition):

Morgan, J. (2019). Data Fusion for Additive Manufacturing Process Inspection. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16433jpm5610

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):

Morgan, Jacob. “Data Fusion for Additive Manufacturing Process Inspection.” 2019. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/16433jpm5610.

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

MLA Handbook (7th Edition):

Morgan, Jacob. “Data Fusion for Additive Manufacturing Process Inspection.” 2019. Web. 30 Oct 2020.

Vancouver:

Morgan J. Data Fusion for Additive Manufacturing Process Inspection. [Internet] [Thesis]. Penn State University; 2019. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/16433jpm5610.

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

Council of Science Editors:

Morgan J. Data Fusion for Additive Manufacturing Process Inspection. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16433jpm5610

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


Penn State University

5. Srinivas, Umamahesh. Discriminative models for robust image classification.

Degree: 2013, Penn State University

 A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image… (more)

Subjects/Keywords: Robust image classification; image processing; graphical models; sparse signal representations.

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

APA (6th Edition):

Srinivas, U. (2013). Discriminative models for robust image classification. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/19014

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):

Srinivas, Umamahesh. “Discriminative models for robust image classification.” 2013. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/19014.

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

MLA Handbook (7th Edition):

Srinivas, Umamahesh. “Discriminative models for robust image classification.” 2013. Web. 30 Oct 2020.

Vancouver:

Srinivas U. Discriminative models for robust image classification. [Internet] [Thesis]. Penn State University; 2013. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/19014.

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

Council of Science Editors:

Srinivas U. Discriminative models for robust image classification. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/19014

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


Penn State University

6. Nedorezov, Adam Jonathan. Active Contour Motion Planning in the Inverse Perspective Mapping Frame.

Degree: 2016, Penn State University

 This work explores the use of active contours for the purposes of motion planning in a highway environment. Traditionally active contours have been used in… (more)

Subjects/Keywords: Active Contour; Path Planning; Vanishing Point Estimation; Inverse Perspective Mapping; Motion Planning; Obstacle Detection

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

APA (6th Edition):

Nedorezov, A. J. (2016). Active Contour Motion Planning in the Inverse Perspective Mapping Frame. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13364ajn5049

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):

Nedorezov, Adam Jonathan. “Active Contour Motion Planning in the Inverse Perspective Mapping Frame.” 2016. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/13364ajn5049.

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

MLA Handbook (7th Edition):

Nedorezov, Adam Jonathan. “Active Contour Motion Planning in the Inverse Perspective Mapping Frame.” 2016. Web. 30 Oct 2020.

Vancouver:

Nedorezov AJ. Active Contour Motion Planning in the Inverse Perspective Mapping Frame. [Internet] [Thesis]. Penn State University; 2016. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/13364ajn5049.

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

Council of Science Editors:

Nedorezov AJ. Active Contour Motion Planning in the Inverse Perspective Mapping Frame. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/13364ajn5049

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


Penn State University

7. Mo, Xuan. adaptive sparse representations for video anomaly detection.

Degree: 2014, Penn State University

 Video surveillance systems are widely used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and… (more)

Subjects/Keywords: video anomaly detection; sparsity model; kernel function; multi-object; low rank sparsity prior; outlier rejection

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

APA (6th Edition):

Mo, X. (2014). adaptive sparse representations for video anomaly detection. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22603

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):

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/22603.

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

MLA Handbook (7th Edition):

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Web. 30 Oct 2020.

Vancouver:

Mo X. adaptive sparse representations for video anomaly detection. [Internet] [Thesis]. Penn State University; 2014. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/22603.

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

Council of Science Editors:

Mo X. adaptive sparse representations for video anomaly detection. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22603

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


Penn State University

8. Vu, Tiep. Signal classification under structured sparsity constraints.

Degree: 2019, Penn State University

 Object Classification is a key direction of research in signal and image processing, computer vision and artificial intelligence. The goal is to come up with… (more)

Subjects/Keywords: Signal Classification; Sparsity Coding; Dictionary Learning; Image Classification; Optimization

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

APA (6th Edition):

Vu, T. (2019). Signal classification under structured sparsity constraints. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16112thv102

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):

Vu, Tiep. “Signal classification under structured sparsity constraints.” 2019. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/16112thv102.

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

MLA Handbook (7th Edition):

Vu, Tiep. “Signal classification under structured sparsity constraints.” 2019. Web. 30 Oct 2020.

Vancouver:

Vu T. Signal classification under structured sparsity constraints. [Internet] [Thesis]. Penn State University; 2019. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/16112thv102.

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

Council of Science Editors:

Vu T. Signal classification under structured sparsity constraints. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16112thv102

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


Penn State University

9. Pezeshk, Aria. FEATURE EXTRACTION AND TEXT RECOGNITION FROM SCANNED COLOR TOPOGRAPHIC MAPS.

Degree: 2011, Penn State University

 Topographic maps are one the best and most abundant sources of geographic information. In most countries these maps are prepared by dedicated national organizations and… (more)

Subjects/Keywords: image processing; document recognition; mathematical morphology; text recognition

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

APA (6th Edition):

Pezeshk, A. (2011). FEATURE EXTRACTION AND TEXT RECOGNITION FROM SCANNED COLOR TOPOGRAPHIC MAPS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12051

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):

Pezeshk, Aria. “FEATURE EXTRACTION AND TEXT RECOGNITION FROM SCANNED COLOR TOPOGRAPHIC MAPS.” 2011. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/12051.

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

MLA Handbook (7th Edition):

Pezeshk, Aria. “FEATURE EXTRACTION AND TEXT RECOGNITION FROM SCANNED COLOR TOPOGRAPHIC MAPS.” 2011. Web. 30 Oct 2020.

Vancouver:

Pezeshk A. FEATURE EXTRACTION AND TEXT RECOGNITION FROM SCANNED COLOR TOPOGRAPHIC MAPS. [Internet] [Thesis]. Penn State University; 2011. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/12051.

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

Council of Science Editors:

Pezeshk A. FEATURE EXTRACTION AND TEXT RECOGNITION FROM SCANNED COLOR TOPOGRAPHIC MAPS. [Thesis]. Penn State University; 2011. Available from: https://submit-etda.libraries.psu.edu/catalog/12051

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


Penn State University

10. Kwon, Yangsoo. TARGET DETECTION IN ULTRA-WIDEBAND NOISE RADAR SYSTEMS.

Degree: 2012, Penn State University

 Ultra-wideband (UWB) noise radar has been widely considered as a promising technique for covert high-resolution detection of multiple targets due to several advantages such as… (more)

Subjects/Keywords: noise radar; target detection; MIMO radar

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APA (6th Edition):

Kwon, Y. (2012). TARGET DETECTION IN ULTRA-WIDEBAND NOISE RADAR SYSTEMS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15716

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):

Kwon, Yangsoo. “TARGET DETECTION IN ULTRA-WIDEBAND NOISE RADAR SYSTEMS.” 2012. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/15716.

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

MLA Handbook (7th Edition):

Kwon, Yangsoo. “TARGET DETECTION IN ULTRA-WIDEBAND NOISE RADAR SYSTEMS.” 2012. Web. 30 Oct 2020.

Vancouver:

Kwon Y. TARGET DETECTION IN ULTRA-WIDEBAND NOISE RADAR SYSTEMS. [Internet] [Thesis]. Penn State University; 2012. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/15716.

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

Council of Science Editors:

Kwon Y. TARGET DETECTION IN ULTRA-WIDEBAND NOISE RADAR SYSTEMS. [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/15716

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


Penn State University

11. Duarte, David Enrique. Clock Network and Phase-Locked Loop Power Estimation and Experimentation.

Degree: 2008, Penn State University

 The clock distribution network and the generation circuitry are critical components of current synchronous digital systems and are known to consume more than a quarter… (more)

Subjects/Keywords: CPU clock energy modeling; power estimation; PLL design; low power VLSI design

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APA (6th Edition):

Duarte, D. E. (2008). Clock Network and Phase-Locked Loop Power Estimation and Experimentation. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/5919

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):

Duarte, David Enrique. “Clock Network and Phase-Locked Loop Power Estimation and Experimentation.” 2008. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/5919.

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

MLA Handbook (7th Edition):

Duarte, David Enrique. “Clock Network and Phase-Locked Loop Power Estimation and Experimentation.” 2008. Web. 30 Oct 2020.

Vancouver:

Duarte DE. Clock Network and Phase-Locked Loop Power Estimation and Experimentation. [Internet] [Thesis]. Penn State University; 2008. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/5919.

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

Council of Science Editors:

Duarte DE. Clock Network and Phase-Locked Loop Power Estimation and Experimentation. [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/5919

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


Penn State University

12. Chappalli, Mahesh B. IMAGE ENHANCEMENT USING SGW SUPERRESOLUTION AND ITERATIVE BLIND DECONVOLUTION.

Degree: 2008, Penn State University

 The field of superresolution has seen a tremendous growth in interest over the past decade. High resolution images are crucial in several applications including medical… (more)

Subjects/Keywords: superresolution; second generation wavelets; blind deconvolution; optimal thresholding; blur support determination

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

APA (6th Edition):

Chappalli, M. B. (2008). IMAGE ENHANCEMENT USING SGW SUPERRESOLUTION AND ITERATIVE BLIND DECONVOLUTION. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/6723

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):

Chappalli, Mahesh B. “IMAGE ENHANCEMENT USING SGW SUPERRESOLUTION AND ITERATIVE BLIND DECONVOLUTION.” 2008. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/6723.

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

MLA Handbook (7th Edition):

Chappalli, Mahesh B. “IMAGE ENHANCEMENT USING SGW SUPERRESOLUTION AND ITERATIVE BLIND DECONVOLUTION.” 2008. Web. 30 Oct 2020.

Vancouver:

Chappalli MB. IMAGE ENHANCEMENT USING SGW SUPERRESOLUTION AND ITERATIVE BLIND DECONVOLUTION. [Internet] [Thesis]. Penn State University; 2008. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/6723.

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

Council of Science Editors:

Chappalli MB. IMAGE ENHANCEMENT USING SGW SUPERRESOLUTION AND ITERATIVE BLIND DECONVOLUTION. [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/6723

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


Penn State University

13. Papson, Scott. The Exploitation of Multi-look Synthetic Aperture Radar and Inverse Synthetic Aperture Radar Images for Non-cooperative Target Recognition.

Degree: 2008, Penn State University

 Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) have proven capabilities for non-cooperative target recognition (NCTR) applications. Both sensing modalities have been able… (more)

Subjects/Keywords: radar imaging; target recognition; multi-look; data fusion; SAR; ISAR; NCTR

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

APA (6th Edition):

Papson, S. (2008). The Exploitation of Multi-look Synthetic Aperture Radar and Inverse Synthetic Aperture Radar Images for Non-cooperative Target Recognition. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/7763

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):

Papson, Scott. “The Exploitation of Multi-look Synthetic Aperture Radar and Inverse Synthetic Aperture Radar Images for Non-cooperative Target Recognition.” 2008. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/7763.

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

MLA Handbook (7th Edition):

Papson, Scott. “The Exploitation of Multi-look Synthetic Aperture Radar and Inverse Synthetic Aperture Radar Images for Non-cooperative Target Recognition.” 2008. Web. 30 Oct 2020.

Vancouver:

Papson S. The Exploitation of Multi-look Synthetic Aperture Radar and Inverse Synthetic Aperture Radar Images for Non-cooperative Target Recognition. [Internet] [Thesis]. Penn State University; 2008. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/7763.

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

Council of Science Editors:

Papson S. The Exploitation of Multi-look Synthetic Aperture Radar and Inverse Synthetic Aperture Radar Images for Non-cooperative Target Recognition. [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/7763

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


Penn State University

14. Ahuja, Nilesh A. Design of Large Field-of-View High-Resolution Miniaturized Imaging System.

Degree: 2008, Penn State University

 In recent years, there has been an interest in the design of computational imaging systems to meet specific imaging goals. Such systems comprise of an… (more)

Subjects/Keywords: wavelets; imaging system; large field of view; superresolution

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APA (6th Edition):

Ahuja, N. A. (2008). Design of Large Field-of-View High-Resolution Miniaturized Imaging System. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/8109

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):

Ahuja, Nilesh A. “Design of Large Field-of-View High-Resolution Miniaturized Imaging System.” 2008. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/8109.

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

MLA Handbook (7th Edition):

Ahuja, Nilesh A. “Design of Large Field-of-View High-Resolution Miniaturized Imaging System.” 2008. Web. 30 Oct 2020.

Vancouver:

Ahuja NA. Design of Large Field-of-View High-Resolution Miniaturized Imaging System. [Internet] [Thesis]. Penn State University; 2008. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/8109.

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

Council of Science Editors:

Ahuja NA. Design of Large Field-of-View High-Resolution Miniaturized Imaging System. [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/8109

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


Penn State University

15. Gupta, Ankur. EFFICIENT AND SCALABLE BIOLOGICALLY PLAUSIBLE SPIKING NEURAL NETWORKS WITH LEARNING APPLIED TO VISION .

Degree: 2010, Penn State University

 Spiking neural networks are more biologically plausible than rate-based neural networks. By incorporating the aspect of time into the model itself, spiking networks are more… (more)

Subjects/Keywords: biologically plausible learning; spiking neural networks; scalable; vision

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

APA (6th Edition):

Gupta, A. (2010). EFFICIENT AND SCALABLE BIOLOGICALLY PLAUSIBLE SPIKING NEURAL NETWORKS WITH LEARNING APPLIED TO VISION . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/11372

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):

Gupta, Ankur. “EFFICIENT AND SCALABLE BIOLOGICALLY PLAUSIBLE SPIKING NEURAL NETWORKS WITH LEARNING APPLIED TO VISION .” 2010. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/11372.

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

MLA Handbook (7th Edition):

Gupta, Ankur. “EFFICIENT AND SCALABLE BIOLOGICALLY PLAUSIBLE SPIKING NEURAL NETWORKS WITH LEARNING APPLIED TO VISION .” 2010. Web. 30 Oct 2020.

Vancouver:

Gupta A. EFFICIENT AND SCALABLE BIOLOGICALLY PLAUSIBLE SPIKING NEURAL NETWORKS WITH LEARNING APPLIED TO VISION . [Internet] [Thesis]. Penn State University; 2010. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/11372.

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

Council of Science Editors:

Gupta A. EFFICIENT AND SCALABLE BIOLOGICALLY PLAUSIBLE SPIKING NEURAL NETWORKS WITH LEARNING APPLIED TO VISION . [Thesis]. Penn State University; 2010. Available from: https://submit-etda.libraries.psu.edu/catalog/11372

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


Penn State University

16. Lim, Hwasup. DYNAMIC MOTION AND APPEARANCE MODELING FOR ROBUST VISUAL TRACKING .

Degree: 2008, Penn State University

 Visual tracking is one of the most active areas in computer vision and it has many promising applications such as human motion capture, human computer… (more)

Subjects/Keywords: visual tracking; caratheodory-fejer; appearance modeling; motion modeling; object tracking

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

APA (6th Edition):

Lim, H. (2008). DYNAMIC MOTION AND APPEARANCE MODELING FOR ROBUST VISUAL TRACKING . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/7514

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):

Lim, Hwasup. “DYNAMIC MOTION AND APPEARANCE MODELING FOR ROBUST VISUAL TRACKING .” 2008. Thesis, Penn State University. Accessed October 30, 2020. https://submit-etda.libraries.psu.edu/catalog/7514.

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

MLA Handbook (7th Edition):

Lim, Hwasup. “DYNAMIC MOTION AND APPEARANCE MODELING FOR ROBUST VISUAL TRACKING .” 2008. Web. 30 Oct 2020.

Vancouver:

Lim H. DYNAMIC MOTION AND APPEARANCE MODELING FOR ROBUST VISUAL TRACKING . [Internet] [Thesis]. Penn State University; 2008. [cited 2020 Oct 30]. Available from: https://submit-etda.libraries.psu.edu/catalog/7514.

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

Council of Science Editors:

Lim H. DYNAMIC MOTION AND APPEARANCE MODELING FOR ROBUST VISUAL TRACKING . [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/7514

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

.