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You searched for +publisher:"University of Dayton" +contributor:("Asari, Vijayan"). Showing records 1 – 30 of 32 total matches.

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University of Dayton

1. Xie, Zhiyuan. Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification.

Degree: MS(M.S.), Electrical Engineering, 2018, University of Dayton

 The main goal of this thesis is classification of multi-view objects by using convolutional neural networks (CNN), and evaluation of the recognition performance on images… (more)

Subjects/Keywords: Electrical Engineering; Multi-view object classification, Convolutional neural network, Machine Learning, Multilevel windowed inverse sigmoid function, Locally tuned sine nonlinearity function

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APA (6th Edition):

Xie, Z. (2018). Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1522937516903222

Chicago Manual of Style (16th Edition):

Xie, Zhiyuan. “Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification.” 2018. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1522937516903222.

MLA Handbook (7th Edition):

Xie, Zhiyuan. “Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification.” 2018. Web. 23 Apr 2021.

Vancouver:

Xie Z. Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification. [Internet] [Masters thesis]. University of Dayton; 2018. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1522937516903222.

Council of Science Editors:

Xie Z. Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification. [Masters Thesis]. University of Dayton; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1522937516903222


University of Dayton

2. Sargent, Garrett Craig. Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters.

Degree: MS(M.S.), Electrical and Computer Engineering, 2017, University of Dayton

  Division of focal plane imaging polarimeters have the distinct advantage of being capable of obtaining temporally synchronized intensity measurements across a scene; however, they… (more)

Subjects/Keywords: Electrical Engineering; Engineering; single image super resolution; microgrid polarimeter; machine learning; extreme learning machine

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APA (6th Edition):

Sargent, G. C. (2017). Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492782713231794

Chicago Manual of Style (16th Edition):

Sargent, Garrett Craig. “Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters.” 2017. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492782713231794.

MLA Handbook (7th Edition):

Sargent, Garrett Craig. “Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters.” 2017. Web. 23 Apr 2021.

Vancouver:

Sargent GC. Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters. [Internet] [Masters thesis]. University of Dayton; 2017. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492782713231794.

Council of Science Editors:

Sargent GC. Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters. [Masters Thesis]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492782713231794


University of Dayton

3. Rajan, Rachel. Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data.

Degree: MS(M.S.), Electrical and Computer Engineering, 2020, University of Dayton

 Recent advancements in Neural Networks have obtained immense popularity in thefield of computer vision applications including image classification, semanticsegmentation, object detection and many more. Studies… (more)

Subjects/Keywords: Computer Engineering; Semantic Segmentation, Semi-supervised Learning, Generative Adversarial Networks, Encoder-decoder, Computer Vision

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APA (6th Edition):

Rajan, R. (2020). Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1597082270750151

Chicago Manual of Style (16th Edition):

Rajan, Rachel. “Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data.” 2020. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1597082270750151.

MLA Handbook (7th Edition):

Rajan, Rachel. “Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data.” 2020. Web. 23 Apr 2021.

Vancouver:

Rajan R. Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data. [Internet] [Masters thesis]. University of Dayton; 2020. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1597082270750151.

Council of Science Editors:

Rajan R. Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data. [Masters Thesis]. University of Dayton; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1597082270750151


University of Dayton

4. Liu, Ruixu. Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Dayton

 Computer vision and artificial intelligence aim to give computers a high-level understanding of images or videos. Through imitating the human brain that perceives and understands… (more)

Subjects/Keywords: Computer Engineering

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APA (6th Edition):

Liu, R. (2019). Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948

Chicago Manual of Style (16th Edition):

Liu, Ruixu. “Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction.” 2019. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948.

MLA Handbook (7th Edition):

Liu, Ruixu. “Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction.” 2019. Web. 23 Apr 2021.

Vancouver:

Liu R. Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction. [Internet] [Doctoral dissertation]. University of Dayton; 2019. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948.

Council of Science Editors:

Liu R. Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction. [Doctoral Dissertation]. University of Dayton; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948


University of Dayton

5. Long, Cameron E. Quaternion Temporal Convolutional Neural Networks.

Degree: MSin Computer Engineering, Electrical and Computer Engineering, 2019, University of Dayton

 Sequence Processing and Modeling are a domain of problems recently receiving significant attention for significant advancements in research and technology. While traditionally sequence processing using… (more)

Subjects/Keywords: Computer Science; Engineering; Quaternion Temporal Convolutional Network; QTCN; Quaternion Neural Network; Sequence Processing; Machine Learning

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APA (6th Edition):

Long, C. E. (2019). Quaternion Temporal Convolutional Neural Networks. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597

Chicago Manual of Style (16th Edition):

Long, Cameron E. “Quaternion Temporal Convolutional Neural Networks.” 2019. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597.

MLA Handbook (7th Edition):

Long, Cameron E. “Quaternion Temporal Convolutional Neural Networks.” 2019. Web. 23 Apr 2021.

Vancouver:

Long CE. Quaternion Temporal Convolutional Neural Networks. [Internet] [Masters thesis]. University of Dayton; 2019. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597.

Council of Science Editors:

Long CE. Quaternion Temporal Convolutional Neural Networks. [Masters Thesis]. University of Dayton; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597


University of Dayton

6. Ragb, Hussin Khalifa Alfitouri. Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment.

Degree: PhD, Electrical and Computer Engineering, 2018, University of Dayton

 Over the last decade, detection of human beings become one of the most significant tasks in computer vision due to its extended applications that include… (more)

Subjects/Keywords: Electrical Engineering; Artificial Intelligence; Phase congruency, human detection, color space, shape features

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APA (6th Edition):

Ragb, H. K. A. (2018). Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1541210403653549

Chicago Manual of Style (16th Edition):

Ragb, Hussin Khalifa Alfitouri. “Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment.” 2018. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1541210403653549.

MLA Handbook (7th Edition):

Ragb, Hussin Khalifa Alfitouri. “Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment.” 2018. Web. 23 Apr 2021.

Vancouver:

Ragb HKA. Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment. [Internet] [Doctoral dissertation]. University of Dayton; 2018. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1541210403653549.

Council of Science Editors:

Ragb HKA. Multi-Hypothesis Approach for Efficient Human Detection in Complex Environment. [Doctoral Dissertation]. University of Dayton; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1541210403653549


University of Dayton

7. Sorg, Bradley R. Multi-Task Learning SegNet Architecture for Semantic Segmentation.

Degree: MSin Computer Engineering, Engineering, 2018, University of Dayton

 Semantic segmentation has been a complex problem in the field of computer vision and is essential for image analysis tasks. Currently, most state-of-the-art algorithms rely… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; Artificial Intelligence; semantic segmentation; SegNet; scene labeling; boundary detection; multi-task learning

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APA (6th Edition):

Sorg, B. R. (2018). Multi-Task Learning SegNet Architecture for Semantic Segmentation. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1542726487025455

Chicago Manual of Style (16th Edition):

Sorg, Bradley R. “Multi-Task Learning SegNet Architecture for Semantic Segmentation.” 2018. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1542726487025455.

MLA Handbook (7th Edition):

Sorg, Bradley R. “Multi-Task Learning SegNet Architecture for Semantic Segmentation.” 2018. Web. 23 Apr 2021.

Vancouver:

Sorg BR. Multi-Task Learning SegNet Architecture for Semantic Segmentation. [Internet] [Masters thesis]. University of Dayton; 2018. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1542726487025455.

Council of Science Editors:

Sorg BR. Multi-Task Learning SegNet Architecture for Semantic Segmentation. [Masters Thesis]. University of Dayton; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1542726487025455


University of Dayton

8. Krieger, Evan. Adaptive Fusion Approach for Multiple Feature Object Tracking.

Degree: PhD, Engineering, 2018, University of Dayton

 Visual object tracking is an important research area within computer vision. Object tracking has applications in security, surveillance, robotics, and safety systems. In generic single… (more)

Subjects/Keywords: Electrical Engineering; Tracker Fusion; Adaptive Fusion; Object Tracking; Computer Vision

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APA (6th Edition):

Krieger, E. (2018). Adaptive Fusion Approach for Multiple Feature Object Tracking. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton15435905735447

Chicago Manual of Style (16th Edition):

Krieger, Evan. “Adaptive Fusion Approach for Multiple Feature Object Tracking.” 2018. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton15435905735447.

MLA Handbook (7th Edition):

Krieger, Evan. “Adaptive Fusion Approach for Multiple Feature Object Tracking.” 2018. Web. 23 Apr 2021.

Vancouver:

Krieger E. Adaptive Fusion Approach for Multiple Feature Object Tracking. [Internet] [Doctoral dissertation]. University of Dayton; 2018. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton15435905735447.

Council of Science Editors:

Krieger E. Adaptive Fusion Approach for Multiple Feature Object Tracking. [Doctoral Dissertation]. University of Dayton; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton15435905735447


University of Dayton

9. Varney, Nina M. LiDAR Data Analysis for Automatic Region Segmentation and Object Classification.

Degree: MS(M.S.), Electrical Engineering, 2015, University of Dayton

 Light Detection and Ranging, (LiDAR) presents a series of unique challenges, the foremost of these being object identification. Because of the ease of aerial collection… (more)

Subjects/Keywords: Electrical Engineering; LiDAR; classification; segmentation; aerial; SELF; NORM

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APA (6th Edition):

Varney, N. M. (2015). LiDAR Data Analysis for Automatic Region Segmentation and Object Classification. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790

Chicago Manual of Style (16th Edition):

Varney, Nina M. “LiDAR Data Analysis for Automatic Region Segmentation and Object Classification.” 2015. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790.

MLA Handbook (7th Edition):

Varney, Nina M. “LiDAR Data Analysis for Automatic Region Segmentation and Object Classification.” 2015. Web. 23 Apr 2021.

Vancouver:

Varney NM. LiDAR Data Analysis for Automatic Region Segmentation and Object Classification. [Internet] [Masters thesis]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790.

Council of Science Editors:

Varney NM. LiDAR Data Analysis for Automatic Region Segmentation and Object Classification. [Masters Thesis]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790


University of Dayton

10. Krieger, Evan. Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery.

Degree: MS(M.S.), Electrical Engineering, 2015, University of Dayton

 Object tracking in the wide area motion imagery (WAMI) data may be subjected to many challenges including object occlusion, rotation, scaling, illumination changes, and background… (more)

Subjects/Keywords: Electrical Engineering; object tracking; Kirsch mask; Gaussian ringlet; image enhancement; super-resolution; feature extraction

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APA (6th Edition):

Krieger, E. (2015). Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509

Chicago Manual of Style (16th Edition):

Krieger, Evan. “Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery.” 2015. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509.

MLA Handbook (7th Edition):

Krieger, Evan. “Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery.” 2015. Web. 23 Apr 2021.

Vancouver:

Krieger E. Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery. [Internet] [Masters thesis]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509.

Council of Science Editors:

Krieger E. Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery. [Masters Thesis]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509


University of Dayton

11. Krucki, Kevin C. Person Re-identification in Multi-Camera Surveillance Systems.

Degree: MS(M.S.), Electrical Engineering, 2015, University of Dayton

 In a system of cameras, it can be beneficial to track and identify people as they move through the scene. To solve this problem (called… (more)

Subjects/Keywords: Electrical Engineering; Computer Engineering

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APA (6th Edition):

Krucki, K. C. (2015). Person Re-identification in Multi-Camera Surveillance Systems. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1448997579

Chicago Manual of Style (16th Edition):

Krucki, Kevin C. “Person Re-identification in Multi-Camera Surveillance Systems.” 2015. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1448997579.

MLA Handbook (7th Edition):

Krucki, Kevin C. “Person Re-identification in Multi-Camera Surveillance Systems.” 2015. Web. 23 Apr 2021.

Vancouver:

Krucki KC. Person Re-identification in Multi-Camera Surveillance Systems. [Internet] [Masters thesis]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1448997579.

Council of Science Editors:

Krucki KC. Person Re-identification in Multi-Camera Surveillance Systems. [Masters Thesis]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1448997579


University of Dayton

12. Arigela, Sai Babu. A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions.

Degree: PhD, Electrical Engineering, 2015, University of Dayton

 In wide area video surveillance, there is a possibility of having extremely dark, bright and hazy regions in some image frames of a video sequence.… (more)

Subjects/Keywords: Electrical Engineering; Engineering

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APA (6th Edition):

Arigela, S. B. (2015). A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449185835

Chicago Manual of Style (16th Edition):

Arigela, Sai Babu. “A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions.” 2015. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449185835.

MLA Handbook (7th Edition):

Arigela, Sai Babu. “A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions.” 2015. Web. 23 Apr 2021.

Vancouver:

Arigela SB. A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions. [Internet] [Doctoral dissertation]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449185835.

Council of Science Editors:

Arigela SB. A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions. [Doctoral Dissertation]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449185835


University of Dayton

13. Prince, Daniel Paul. Automatic Building Change Detection Through Linear Feature Fusion and Difference of Gaussian Classification.

Degree: MSin Computer Engineering, Electrical and Computer Engineering, 2016, University of Dayton

 Many applications in infrastructure planning and maintenance are currently aided by the collection of aerial image data and manual examination by human analysts. The increasing… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; change detection; building detection; feature fusion; difference of Gaussian; NDVI

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APA (6th Edition):

Prince, D. P. (2016). Automatic Building Change Detection Through Linear Feature Fusion and Difference of Gaussian Classification. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480418487590701

Chicago Manual of Style (16th Edition):

Prince, Daniel Paul. “Automatic Building Change Detection Through Linear Feature Fusion and Difference of Gaussian Classification.” 2016. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480418487590701.

MLA Handbook (7th Edition):

Prince, Daniel Paul. “Automatic Building Change Detection Through Linear Feature Fusion and Difference of Gaussian Classification.” 2016. Web. 23 Apr 2021.

Vancouver:

Prince DP. Automatic Building Change Detection Through Linear Feature Fusion and Difference of Gaussian Classification. [Internet] [Masters thesis]. University of Dayton; 2016. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480418487590701.

Council of Science Editors:

Prince DP. Automatic Building Change Detection Through Linear Feature Fusion and Difference of Gaussian Classification. [Masters Thesis]. University of Dayton; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480418487590701


University of Dayton

14. Sargent, Garrett Craig. A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters.

Degree: PhD, Electrical and Computer Engineering, 2020, University of Dayton

 Division of focal plane (DoFP), or integrated microgrid polarimeters, typically consist of a 2x2 mosaic of linear polarization filters overlaid upon a focal plane array… (more)

Subjects/Keywords: Electrical Engineering; deep learning; GAN; demosaicing; polarimetric imaging; microgrid polarimeters

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APA (6th Edition):

Sargent, G. C. (2020). A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606050550958383

Chicago Manual of Style (16th Edition):

Sargent, Garrett Craig. “A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters.” 2020. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606050550958383.

MLA Handbook (7th Edition):

Sargent, Garrett Craig. “A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters.” 2020. Web. 23 Apr 2021.

Vancouver:

Sargent GC. A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters. [Internet] [Doctoral dissertation]. University of Dayton; 2020. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606050550958383.

Council of Science Editors:

Sargent GC. A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters. [Doctoral Dissertation]. University of Dayton; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606050550958383


University of Dayton

15. Graehling, Quinn R. Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds.

Degree: MSin Computer Engineering, Electrical and Computer Engineering, 2020, University of Dayton

 Image registration is a major field within computer vision and is often a required step in properly fulfilling other computer vision and pattern recognition tasks… (more)

Subjects/Keywords: Computer Engineering; Computer Science; Artificial Intelligence; Electrical Engineering; Engineering; Registration; Point Clouds; LiDAR; Iterative Closest Point; 3D Computer Vision

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APA (6th Edition):

Graehling, Q. R. (2020). Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017

Chicago Manual of Style (16th Edition):

Graehling, Quinn R. “Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds.” 2020. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017.

MLA Handbook (7th Edition):

Graehling, Quinn R. “Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds.” 2020. Web. 23 Apr 2021.

Vancouver:

Graehling QR. Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds. [Internet] [Masters thesis]. University of Dayton; 2020. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017.

Council of Science Editors:

Graehling QR. Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds. [Masters Thesis]. University of Dayton; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017

16. Essa, Almabrok Essa. High Order Volumetric Directional Pattern for Robust Face Recognition.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Dayton

 The texture of objects in digital images is an important property that has been utilized in many computer vision and image analysis applications, such as… (more)

Subjects/Keywords: Engineering; Electrical Engineering; Face recognition; high order local directional pattern; volumetric directional pattern; high order volumetric directional pattern

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APA (6th Edition):

Essa, A. E. (2017). High Order Volumetric Directional Pattern for Robust Face Recognition. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500901918995427

Chicago Manual of Style (16th Edition):

Essa, Almabrok Essa. “High Order Volumetric Directional Pattern for Robust Face Recognition.” 2017. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500901918995427.

MLA Handbook (7th Edition):

Essa, Almabrok Essa. “High Order Volumetric Directional Pattern for Robust Face Recognition.” 2017. Web. 23 Apr 2021.

Vancouver:

Essa AE. High Order Volumetric Directional Pattern for Robust Face Recognition. [Internet] [Doctoral dissertation]. University of Dayton; 2017. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500901918995427.

Council of Science Editors:

Essa AE. High Order Volumetric Directional Pattern for Robust Face Recognition. [Doctoral Dissertation]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1500901918995427

17. Jackovitz, Kevin S. Integrated Coarse to Fine and Shot Break Detection Approach for Fast and Efficient Registration of Aerial Image Sequences.

Degree: MS(M.S.), Electrical Engineering, 2013, University of Dayton

 Image registration is a task that has been focused on in many fields that deal with object detection and tracking on video sequences. When tracking… (more)

Subjects/Keywords: Engineering; Electrical Engineering; Image Registration; Object Tracking; Shot Break Detection; SSIM; Aerial Image; Registration; Wide Area Motion Imagery; Full Motion Imagery; Computer Vision; Image Sequence Registration

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APA (6th Edition):

Jackovitz, K. S. (2013). Integrated Coarse to Fine and Shot Break Detection Approach for Fast and Efficient Registration of Aerial Image Sequences. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366306702

Chicago Manual of Style (16th Edition):

Jackovitz, Kevin S. “Integrated Coarse to Fine and Shot Break Detection Approach for Fast and Efficient Registration of Aerial Image Sequences.” 2013. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366306702.

MLA Handbook (7th Edition):

Jackovitz, Kevin S. “Integrated Coarse to Fine and Shot Break Detection Approach for Fast and Efficient Registration of Aerial Image Sequences.” 2013. Web. 23 Apr 2021.

Vancouver:

Jackovitz KS. Integrated Coarse to Fine and Shot Break Detection Approach for Fast and Efficient Registration of Aerial Image Sequences. [Internet] [Masters thesis]. University of Dayton; 2013. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366306702.

Council of Science Editors:

Jackovitz KS. Integrated Coarse to Fine and Shot Break Detection Approach for Fast and Efficient Registration of Aerial Image Sequences. [Masters Thesis]. University of Dayton; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366306702

18. Aspiras, Theus Herrera. Hierarchical Autoassociative Polynomial Network for Deep Learning of Complex Manifolds.

Degree: PhD, Electrical Engineering, 2015, University of Dayton

 Artificial neural networks are an area of research that has been explored extensively. With the formation of these networks, models of biological neural networks can… (more)

Subjects/Keywords: Electrical Engineering; Computer Engineering; Polynomial Neural Network; Complex Manifolds; Deep Learning; Nonlinear Weighting; Modular; Classification; MNIST; HAP net

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APA (6th Edition):

Aspiras, T. H. (2015). Hierarchical Autoassociative Polynomial Network for Deep Learning of Complex Manifolds. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449104879

Chicago Manual of Style (16th Edition):

Aspiras, Theus Herrera. “Hierarchical Autoassociative Polynomial Network for Deep Learning of Complex Manifolds.” 2015. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449104879.

MLA Handbook (7th Edition):

Aspiras, Theus Herrera. “Hierarchical Autoassociative Polynomial Network for Deep Learning of Complex Manifolds.” 2015. Web. 23 Apr 2021.

Vancouver:

Aspiras TH. Hierarchical Autoassociative Polynomial Network for Deep Learning of Complex Manifolds. [Internet] [Doctoral dissertation]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449104879.

Council of Science Editors:

Aspiras TH. Hierarchical Autoassociative Polynomial Network for Deep Learning of Complex Manifolds. [Doctoral Dissertation]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449104879

19. Aspiras, Theus H. Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals.

Degree: MS(M.S.), Electrical Engineering, 2012, University of Dayton

 Emotion recognition using electroencephalographic (EEG) recordings is a new area of research which focuses on recognition of emotional states of mind rather than impulsive responses.… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; Engineering; Neurosciences; Psychology; Emotion Recognition; Electroencephalography; Wavelet Decomposition; Multilayer Perceptron; Laplacian Montage; International Affective Picture System

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APA (6th Edition):

Aspiras, T. H. (2012). Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574

Chicago Manual of Style (16th Edition):

Aspiras, Theus H. “Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals.” 2012. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574.

MLA Handbook (7th Edition):

Aspiras, Theus H. “Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals.” 2012. Web. 23 Apr 2021.

Vancouver:

Aspiras TH. Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals. [Internet] [Masters thesis]. University of Dayton; 2012. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574.

Council of Science Editors:

Aspiras TH. Emotion Recognition using Spatiotemporal Analysis of Electroencephalographic Signals. [Masters Thesis]. University of Dayton; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343992574

20. Foytik, Jacob D. Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation.

Degree: PhD, Electrical Engineering, 2011, University of Dayton

 Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction systems. It is… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; Head Pose Estimation; Piecewise Linear Manifold; Pose Sensitive Representations; Coarse to Fine; Head Orientation; Phase Congruency

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APA (6th Edition):

Foytik, J. D. (2011). Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1311968002

Chicago Manual of Style (16th Edition):

Foytik, Jacob D. “Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation.” 2011. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1311968002.

MLA Handbook (7th Edition):

Foytik, Jacob D. “Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation.” 2011. Web. 23 Apr 2021.

Vancouver:

Foytik JD. Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation. [Internet] [Doctoral dissertation]. University of Dayton; 2011. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1311968002.

Council of Science Editors:

Foytik JD. Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation. [Doctoral Dissertation]. University of Dayton; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1311968002

21. Mathew, Alex. Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery.

Degree: PhD, Electrical Engineering, 2014, University of Dayton

 Object detection and tracking in imagery captured by aerial systems are becoming increasingly important in computer vision research. In aerial imagery, objects can appear in… (more)

Subjects/Keywords: Electrical Engineering; Computer Science; Computer Engineering; Engineering; Rotation invariant feature; Aerial object detection; Pattern recognition; Object detection; Object tracking; Integral DFT

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APA (6th Edition):

Mathew, A. (2014). Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849

Chicago Manual of Style (16th Edition):

Mathew, Alex. “Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery.” 2014. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849.

MLA Handbook (7th Edition):

Mathew, Alex. “Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery.” 2014. Web. 23 Apr 2021.

Vancouver:

Mathew A. Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery. [Internet] [Doctoral dissertation]. University of Dayton; 2014. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849.

Council of Science Editors:

Mathew A. Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery. [Doctoral Dissertation]. University of Dayton; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849

22. Powar, Nilesh U. An Approach for the Extraction of Thermal Facial Signatures for Evaluating Threat and Challenge Emotional States.

Degree: PhD, Electrical Engineering, 2013, University of Dayton

 Emotional state assessment of humans has been traditionally studied using various direct physiological measures and psychological self-reports. In this dissertation, we demonstrate that the variability… (more)

Subjects/Keywords: Electrical Engineering; Thermal Imaging, Eigen Analysis, Human State Assessment

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APA (6th Edition):

Powar, N. U. (2013). An Approach for the Extraction of Thermal Facial Signatures for Evaluating Threat and Challenge Emotional States. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386697359

Chicago Manual of Style (16th Edition):

Powar, Nilesh U. “An Approach for the Extraction of Thermal Facial Signatures for Evaluating Threat and Challenge Emotional States.” 2013. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386697359.

MLA Handbook (7th Edition):

Powar, Nilesh U. “An Approach for the Extraction of Thermal Facial Signatures for Evaluating Threat and Challenge Emotional States.” 2013. Web. 23 Apr 2021.

Vancouver:

Powar NU. An Approach for the Extraction of Thermal Facial Signatures for Evaluating Threat and Challenge Emotional States. [Internet] [Doctoral dissertation]. University of Dayton; 2013. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386697359.

Council of Science Editors:

Powar NU. An Approach for the Extraction of Thermal Facial Signatures for Evaluating Threat and Challenge Emotional States. [Doctoral Dissertation]. University of Dayton; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386697359

23. Martell, Patrick Keith. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.

Degree: MS(M.S.), Electrical Engineering, 2017, University of Dayton

 Convolutional neural networks (CNNs) lack ample methods to improve performance without either adding more input data, modifying existing data, or changing network design. This work… (more)

Subjects/Keywords: Electrical Engineering; Convolutional Neural Network; Polynomial; CNN; Classification; MNIST

…effect of polynomials. However, within the University of Dayton Vision Lab, there have been… 

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APA (6th Edition):

Martell, P. K. (2017). Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038

Chicago Manual of Style (16th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

MLA Handbook (7th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Web. 23 Apr 2021.

Vancouver:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Internet] [Masters thesis]. University of Dayton; 2017. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

Council of Science Editors:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Masters Thesis]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038

24. Albalooshi, Fatema A. Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments.

Degree: PhD, Electrical Engineering, 2015, University of Dayton

 Boundary extraction for object region segmentation is one of the most challenging tasks in image processing and computer vision areas. The complexity of large variations… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; active contour models; level set function; self organizing map; lattice Boltzmann method; object segmentation

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APA (6th Edition):

Albalooshi, F. A. (2015). Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429545327

Chicago Manual of Style (16th Edition):

Albalooshi, Fatema A. “Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments.” 2015. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429545327.

MLA Handbook (7th Edition):

Albalooshi, Fatema A. “Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments.” 2015. Web. 23 Apr 2021.

Vancouver:

Albalooshi FA. Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments. [Internet] [Doctoral dissertation]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429545327.

Council of Science Editors:

Albalooshi FA. Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments. [Doctoral Dissertation]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429545327

25. Diskin, Yakov. Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models.

Degree: PhD, Electrical Engineering, 2015, University of Dayton

 We present a 3D change detection technique designed to support various wide-area-surveillance (WAS) applications in changing environmental conditions. The novelty of the work lies in… (more)

Subjects/Keywords: Electrical Engineering; volumetric change detection; 3D reconstruction; aerial surveillance; point cloud registration; illumination invariant; noise suppression; Dense Point-cloud Representation

…of the same scene. The camera is positioned in Kettering Laboratories (University of… …Dayton, Ohio) and pointing West (265.9731◦ ) to capture Baujan Sports Field, the… 

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APA (6th Edition):

Diskin, Y. (2015). Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660

Chicago Manual of Style (16th Edition):

Diskin, Yakov. “Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models.” 2015. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660.

MLA Handbook (7th Edition):

Diskin, Yakov. “Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models.” 2015. Web. 23 Apr 2021.

Vancouver:

Diskin Y. Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models. [Internet] [Doctoral dissertation]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660.

Council of Science Editors:

Diskin Y. Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models. [Doctoral Dissertation]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660

26. Nair, Binu Muraleedharan. Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks.

Degree: PhD, Electrical Engineering, 2015, University of Dayton

 Recognizing multiple types of actions appearing in a continuous temporal order from a streaming video is the key to many possible applications ranging from real-time… (more)

Subjects/Keywords: Computer Engineering; Computer Science; Electrical Engineering; Statistics; motion descriptors, shape descriptors, principal component analysis, neural networks, autoencoder, restricted Boltzmann machine, conditional restricted Boltzmann machine, deep learning, action recognition, latent temporal manifold, action localization

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APA (6th Edition):

Nair, B. M. (2015). Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429532297

Chicago Manual of Style (16th Edition):

Nair, Binu Muraleedharan. “Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks.” 2015. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429532297.

MLA Handbook (7th Edition):

Nair, Binu Muraleedharan. “Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks.” 2015. Web. 23 Apr 2021.

Vancouver:

Nair BM. Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks. [Internet] [Doctoral dissertation]. University of Dayton; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429532297.

Council of Science Editors:

Nair BM. Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks. [Doctoral Dissertation]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429532297

27. Santhaseelan, Varun. Robust Feature Based Reconstruction Technique to Remove Rain from Video.

Degree: PhD, Electrical Engineering, 2013, University of Dayton

 In the context of extracting information from video, especially in the case of surveillance videos, bad weather conditions can pose a huge challenge. They affect… (more)

Subjects/Keywords: Electrical Engineering; Computer Engineering; rain removal; snow removal; phase congruency; monogenic signal; optical flow

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APA (6th Edition):

Santhaseelan, V. (2013). Robust Feature Based Reconstruction Technique to Remove Rain from Video. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386526450

Chicago Manual of Style (16th Edition):

Santhaseelan, Varun. “Robust Feature Based Reconstruction Technique to Remove Rain from Video.” 2013. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386526450.

MLA Handbook (7th Edition):

Santhaseelan, Varun. “Robust Feature Based Reconstruction Technique to Remove Rain from Video.” 2013. Web. 23 Apr 2021.

Vancouver:

Santhaseelan V. Robust Feature Based Reconstruction Technique to Remove Rain from Video. [Internet] [Doctoral dissertation]. University of Dayton; 2013. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386526450.

Council of Science Editors:

Santhaseelan V. Robust Feature Based Reconstruction Technique to Remove Rain from Video. [Doctoral Dissertation]. University of Dayton; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386526450

28. Cui, Chen. Convolutional Polynomial Neural Network for Improved Face Recognition.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Dayton

 Deep learning is the state-of-art technology in pattern recognition, especially in face recognition. The robustness of the deep network leads a better performance when the… (more)

Subjects/Keywords: Artificial Intelligence; Bioinformatics; Computer Engineering; Electrical Engineering; Deep Learning, Convolutional Polynomial Neural Network, Face Recognition, Computer Vision, Image Processing

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APA (6th Edition):

Cui, C. (2017). Convolutional Polynomial Neural Network for Improved Face Recognition. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497628776210369

Chicago Manual of Style (16th Edition):

Cui, Chen. “Convolutional Polynomial Neural Network for Improved Face Recognition.” 2017. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497628776210369.

MLA Handbook (7th Edition):

Cui, Chen. “Convolutional Polynomial Neural Network for Improved Face Recognition.” 2017. Web. 23 Apr 2021.

Vancouver:

Cui C. Convolutional Polynomial Neural Network for Improved Face Recognition. [Internet] [Doctoral dissertation]. University of Dayton; 2017. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497628776210369.

Council of Science Editors:

Cui C. Convolutional Polynomial Neural Network for Improved Face Recognition. [Doctoral Dissertation]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497628776210369

29. Paheding, Sidike. Progressively Expanded Neural Network for Automatic Material Identification in Hyperspectral Imagery.

Degree: PhD, Electrical and Computer Engineering, 2016, University of Dayton

 The science of hyperspectral remote sensing focuses on the exploitation of the spectral signatures of various materials to enhance capabilities including object detection, recognition, and… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; Remote Sensing; Hyperspectral imagery; neural network; object detection; classification; joint transform correlation; progressively expanded neural network; spectral-spatial features

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APA (6th Edition):

Paheding, S. (2016). Progressively Expanded Neural Network for Automatic Material Identification in Hyperspectral Imagery. (Doctoral Dissertation). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481031970630722

Chicago Manual of Style (16th Edition):

Paheding, Sidike. “Progressively Expanded Neural Network for Automatic Material Identification in Hyperspectral Imagery.” 2016. Doctoral Dissertation, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481031970630722.

MLA Handbook (7th Edition):

Paheding, Sidike. “Progressively Expanded Neural Network for Automatic Material Identification in Hyperspectral Imagery.” 2016. Web. 23 Apr 2021.

Vancouver:

Paheding S. Progressively Expanded Neural Network for Automatic Material Identification in Hyperspectral Imagery. [Internet] [Doctoral dissertation]. University of Dayton; 2016. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481031970630722.

Council of Science Editors:

Paheding S. Progressively Expanded Neural Network for Automatic Material Identification in Hyperspectral Imagery. [Doctoral Dissertation]. University of Dayton; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481031970630722

30. Cui, Chen. Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition.

Degree: MS(M.S.), Electrical Engineering, 2013, University of Dayton

 Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. Several algorithms have been developed for extracting… (more)

Subjects/Keywords: Electrical Engineering; Enhanced Local Binary Pattern; Weighted Modular Principal Component Analysis; Face Recognition; Feature Extraction

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APA (6th Edition):

Cui, C. (2013). Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158

Chicago Manual of Style (16th Edition):

Cui, Chen. “Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition.” 2013. Masters Thesis, University of Dayton. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158.

MLA Handbook (7th Edition):

Cui, Chen. “Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition.” 2013. Web. 23 Apr 2021.

Vancouver:

Cui C. Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition. [Internet] [Masters thesis]. University of Dayton; 2013. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158.

Council of Science Editors:

Cui C. Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition. [Masters Thesis]. University of Dayton; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158

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