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You searched for +publisher:"Colorado State University" +contributor:("Beveridge, J. Ross"). Showing records 1 – 14 of 14 total matches.

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Colorado State University

1. Mulay, Gururaj. Adapting RGB pose estimation to new domains.

Degree: MS(M.S.), Computer Science, 2019, Colorado State University

 Many multi-modal human computer interaction (HCI) systems interact with users in real-time by estimating the user's pose. Generally, they estimate human poses using depth sensors… (more)

Subjects/Keywords: CWC; human pose estimation; RMPE; HCI; convolutional neural networks; Microsoft Kinect

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APA (6th Edition):

Mulay, G. (2019). Adapting RGB pose estimation to new domains. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/195409

Chicago Manual of Style (16th Edition):

Mulay, Gururaj. “Adapting RGB pose estimation to new domains.” 2019. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/195409.

MLA Handbook (7th Edition):

Mulay, Gururaj. “Adapting RGB pose estimation to new domains.” 2019. Web. 27 Feb 2021.

Vancouver:

Mulay G. Adapting RGB pose estimation to new domains. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/195409.

Council of Science Editors:

Mulay G. Adapting RGB pose estimation to new domains. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/195409


Colorado State University

2. D'Souza, Wimroy. Evaluating the role of context in 3D theater stage reconstruction.

Degree: MS(M.S.), Electrical and Computer Engineering, 2014, Colorado State University

 Recovering the 3D structure from 2D images is a problem dating back to the 1960s. It is only recently, with the advancement of computing technology,… (more)

Subjects/Keywords: 3D theater stage reconstruction; scene understanding; computer vision

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APA (6th Edition):

D'Souza, W. (2014). Evaluating the role of context in 3D theater stage reconstruction. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/88518

Chicago Manual of Style (16th Edition):

D'Souza, Wimroy. “Evaluating the role of context in 3D theater stage reconstruction.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/88518.

MLA Handbook (7th Edition):

D'Souza, Wimroy. “Evaluating the role of context in 3D theater stage reconstruction.” 2014. Web. 27 Feb 2021.

Vancouver:

D'Souza W. Evaluating the role of context in 3D theater stage reconstruction. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/88518.

Council of Science Editors:

D'Souza W. Evaluating the role of context in 3D theater stage reconstruction. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/88518


Colorado State University

3. McNeely-White, David G. Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.

Degree: MS(M.S.), Computer Science, 2020, Colorado State University

 Deep convolutional neural networks (CNNs) are the dominant technology in computer vision today. Much of the recent computer vision literature can be thought of as… (more)

Subjects/Keywords: convolutional neural networks; feature space; machine learning; feature mapping; computer vision; ImageNet

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APA (6th Edition):

McNeely-White, D. G. (2020). Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/208467

Chicago Manual of Style (16th Edition):

McNeely-White, David G. “Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/208467.

MLA Handbook (7th Edition):

McNeely-White, David G. “Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.” 2020. Web. 27 Feb 2021.

Vancouver:

McNeely-White DG. Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/208467.

Council of Science Editors:

McNeely-White DG. Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/208467


Colorado State University

4. Dragan, Matthew R. Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs.

Degree: MS(M.S.), Computer Science, 2020, Colorado State University

 Deep convolutional neural networks (DCNNs) have achieved state of the art performance on a variety of tasks. These high-performing networks require large and diverse training… (more)

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APA (6th Edition):

Dragan, M. R. (2020). Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/219571

Chicago Manual of Style (16th Edition):

Dragan, Matthew R. “Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/219571.

MLA Handbook (7th Edition):

Dragan, Matthew R. “Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs.” 2020. Web. 27 Feb 2021.

Vancouver:

Dragan MR. Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/219571.

Council of Science Editors:

Dragan MR. Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/219571


Colorado State University

5. Bolme, David Scott. Theory and applications of optimized correlation output filters.

Degree: PhD, Computer Science, 2011, Colorado State University

 Correlation filters are a standard way to solve many problems in signal processing, image processing, and computer vision. This research introduces two new filter training… (more)

Subjects/Keywords: computer vision; object detection; correlation filters

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APA (6th Edition):

Bolme, D. S. (2011). Theory and applications of optimized correlation output filters. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/47326

Chicago Manual of Style (16th Edition):

Bolme, David Scott. “Theory and applications of optimized correlation output filters.” 2011. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/47326.

MLA Handbook (7th Edition):

Bolme, David Scott. “Theory and applications of optimized correlation output filters.” 2011. Web. 27 Feb 2021.

Vancouver:

Bolme DS. Theory and applications of optimized correlation output filters. [Internet] [Doctoral dissertation]. Colorado State University; 2011. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/47326.

Council of Science Editors:

Bolme DS. Theory and applications of optimized correlation output filters. [Doctoral Dissertation]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/47326


Colorado State University

6. Teli, Mohammad Nayeem. Face detection using correlation filters.

Degree: PhD, Computer Science, 2013, Colorado State University

 Cameras are ubiquitous and available all around us. As a result, images and videos are posted online in huge numbers. These images often need to… (more)

Subjects/Keywords: MOSSE; face detection; point and shoot; correlation filters; face

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APA (6th Edition):

Teli, M. N. (2013). Face detection using correlation filters. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/80983

Chicago Manual of Style (16th Edition):

Teli, Mohammad Nayeem. “Face detection using correlation filters.” 2013. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/80983.

MLA Handbook (7th Edition):

Teli, Mohammad Nayeem. “Face detection using correlation filters.” 2013. Web. 27 Feb 2021.

Vancouver:

Teli MN. Face detection using correlation filters. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/80983.

Council of Science Editors:

Teli MN. Face detection using correlation filters. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/80983


Colorado State University

7. Kulkarni, Hrushikesh N. Performance evaluation of feature sets for carried object detection in still images.

Degree: MS(M.S.), Electrical and Computer Engineering, 2014, Colorado State University

 Human activity recognition has gathered a lot of interest. The ability to accurately detect carried objects on human beings will directly help activity recognition. This… (more)

Subjects/Keywords: carried object detection; computer vision; image processing; machine learning; performance evaluation of features; video surveillance

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APA (6th Edition):

Kulkarni, H. N. (2014). Performance evaluation of feature sets for carried object detection in still images. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/83983

Chicago Manual of Style (16th Edition):

Kulkarni, Hrushikesh N. “Performance evaluation of feature sets for carried object detection in still images.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/83983.

MLA Handbook (7th Edition):

Kulkarni, Hrushikesh N. “Performance evaluation of feature sets for carried object detection in still images.” 2014. Web. 27 Feb 2021.

Vancouver:

Kulkarni HN. Performance evaluation of feature sets for carried object detection in still images. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/83983.

Council of Science Editors:

Kulkarni HN. Performance evaluation of feature sets for carried object detection in still images. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/83983


Colorado State University

8. Kadappan, Karthik. Element rearrangement for action classification on product manifolds.

Degree: MS(M.S.), Electrical and Computer Engineering, 2013, Colorado State University

 Conventional tensor-based classification algorithms unfold tensors into matrices using the standard mode-k unfoldings and perform classification using established machine learning algorithms. These methods assume that… (more)

Subjects/Keywords: action classification; computer vision; element rearrangement; manifolds; Tabu search; tensor

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APA (6th Edition):

Kadappan, K. (2013). Element rearrangement for action classification on product manifolds. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/80250

Chicago Manual of Style (16th Edition):

Kadappan, Karthik. “Element rearrangement for action classification on product manifolds.” 2013. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/80250.

MLA Handbook (7th Edition):

Kadappan, Karthik. “Element rearrangement for action classification on product manifolds.” 2013. Web. 27 Feb 2021.

Vancouver:

Kadappan K. Element rearrangement for action classification on product manifolds. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/80250.

Council of Science Editors:

Kadappan K. Element rearrangement for action classification on product manifolds. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/80250

9. Alsaaran, Hessah. Unsupervised video segmentation using temporal coherence of motion.

Degree: PhD, Computer Science, 2015, Colorado State University

 Spatio-temporal video segmentation groups pixels with the goal of representing moving objects in scenes. It is a difficult task for many reasons: parts of an… (more)

Subjects/Keywords: temporal coherence of motion; video segmentation

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APA (6th Edition):

Alsaaran, H. (2015). Unsupervised video segmentation using temporal coherence of motion. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/170396

Chicago Manual of Style (16th Edition):

Alsaaran, Hessah. “Unsupervised video segmentation using temporal coherence of motion.” 2015. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/170396.

MLA Handbook (7th Edition):

Alsaaran, Hessah. “Unsupervised video segmentation using temporal coherence of motion.” 2015. Web. 27 Feb 2021.

Vancouver:

Alsaaran H. Unsupervised video segmentation using temporal coherence of motion. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/170396.

Council of Science Editors:

Alsaaran H. Unsupervised video segmentation using temporal coherence of motion. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/170396

10. Patil, Dhruva. Looking under the hood: visualizing what LSTMs learn.

Degree: MS(M.S.), Computer Science, 2019, Colorado State University

 Recurrent Neural Networks (RNNs) such as Long Short Term Memory (LSTM) and Gated Recurrent Units (GRUs) have been successful in many applications involving sequential data.… (more)

Subjects/Keywords: activation maximization; recurrent neural networks; action recognition; visualization; LSTM

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APA (6th Edition):

Patil, D. (2019). Looking under the hood: visualizing what LSTMs learn. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/197280

Chicago Manual of Style (16th Edition):

Patil, Dhruva. “Looking under the hood: visualizing what LSTMs learn.” 2019. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/197280.

MLA Handbook (7th Edition):

Patil, Dhruva. “Looking under the hood: visualizing what LSTMs learn.” 2019. Web. 27 Feb 2021.

Vancouver:

Patil D. Looking under the hood: visualizing what LSTMs learn. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/197280.

Council of Science Editors:

Patil D. Looking under the hood: visualizing what LSTMs learn. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/197280


Colorado State University

11. Stevens, John. Analysis of grating cell features for texture discrimination, An.

Degree: MS(M.S.), Computer Science, 2010, Colorado State University

 The design of artificial vision systems has been influenced by knowledge of the early stages of processing in the human vision system. The discovery of… (more)

Subjects/Keywords: Image analysis; Imaging systems; Computer vision

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APA (6th Edition):

Stevens, J. (2010). Analysis of grating cell features for texture discrimination, An. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/41470

Chicago Manual of Style (16th Edition):

Stevens, John. “Analysis of grating cell features for texture discrimination, An.” 2010. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/41470.

MLA Handbook (7th Edition):

Stevens, John. “Analysis of grating cell features for texture discrimination, An.” 2010. Web. 27 Feb 2021.

Vancouver:

Stevens J. Analysis of grating cell features for texture discrimination, An. [Internet] [Masters thesis]. Colorado State University; 2010. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/41470.

Council of Science Editors:

Stevens J. Analysis of grating cell features for texture discrimination, An. [Masters Thesis]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/41470


Colorado State University

12. Yamaguchi, Takanobu. Cloud-top entrainment analyzed with a Lagrangian parcel tracking model in large-eddy simulations.

Degree: PhD, Atmospheric Science, 2010, Colorado State University

 Despite decades of research, cloud-top entrainment has not been described with firm evidence. This leads to insufficient understanding of the physics of marine stratocumulus clouds.… (more)

Subjects/Keywords: stratocumulus cloud; large eddy simulation; Lagrangian parcel tracking; entrainment; cloud-top entrainment instability; buoyancy reversal; Atmospheric turbulence; Cloud physics; Boundary layer (Meteorology); Turbulence

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APA (6th Edition):

Yamaguchi, T. (2010). Cloud-top entrainment analyzed with a Lagrangian parcel tracking model in large-eddy simulations. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/44769

Chicago Manual of Style (16th Edition):

Yamaguchi, Takanobu. “Cloud-top entrainment analyzed with a Lagrangian parcel tracking model in large-eddy simulations.” 2010. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/44769.

MLA Handbook (7th Edition):

Yamaguchi, Takanobu. “Cloud-top entrainment analyzed with a Lagrangian parcel tracking model in large-eddy simulations.” 2010. Web. 27 Feb 2021.

Vancouver:

Yamaguchi T. Cloud-top entrainment analyzed with a Lagrangian parcel tracking model in large-eddy simulations. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/44769.

Council of Science Editors:

Yamaguchi T. Cloud-top entrainment analyzed with a Lagrangian parcel tracking model in large-eddy simulations. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/44769


Colorado State University

13. Lui, Yui Man. Geometric methods on special manifolds for visual recognition.

Degree: PhD, Computer Science, 2010, Colorado State University

 Many computer vision methods assume that the underlying geometry of images is Euclidean. This assumption is generally not valid. Therefore, this dissertation introduces new nonlinear… (more)

Subjects/Keywords: action classification; visual recognition; special manifolds; geometric methods; face recognition; Human face recognition (Computer science); Grassmann manifolds; Stiefel manifolds

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APA (6th Edition):

Lui, Y. M. (2010). Geometric methods on special manifolds for visual recognition. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/39042

Chicago Manual of Style (16th Edition):

Lui, Yui Man. “Geometric methods on special manifolds for visual recognition.” 2010. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/39042.

MLA Handbook (7th Edition):

Lui, Yui Man. “Geometric methods on special manifolds for visual recognition.” 2010. Web. 27 Feb 2021.

Vancouver:

Lui YM. Geometric methods on special manifolds for visual recognition. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/39042.

Council of Science Editors:

Lui YM. Geometric methods on special manifolds for visual recognition. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/39042


Colorado State University

14. Crawford-Hines, Stewart. Machine learned boundary definitions for an expert's tracing assistant in image processing.

Degree: PhD, Computer Science, 2003, Colorado State University

 Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the… (more)

Subjects/Keywords: visible human imagery; boundary definitions; expert's tracing assistant; ETA; intelligent scissors; IS; active contour models; ACM; boundary-learning system; Image processing; Pattern recognition systems

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APA (6th Edition):

Crawford-Hines, S. (2003). Machine learned boundary definitions for an expert's tracing assistant in image processing. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/28552

Chicago Manual of Style (16th Edition):

Crawford-Hines, Stewart. “Machine learned boundary definitions for an expert's tracing assistant in image processing.” 2003. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/28552.

MLA Handbook (7th Edition):

Crawford-Hines, Stewart. “Machine learned boundary definitions for an expert's tracing assistant in image processing.” 2003. Web. 27 Feb 2021.

Vancouver:

Crawford-Hines S. Machine learned boundary definitions for an expert's tracing assistant in image processing. [Internet] [Doctoral dissertation]. Colorado State University; 2003. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/28552.

Council of Science Editors:

Crawford-Hines S. Machine learned boundary definitions for an expert's tracing assistant in image processing. [Doctoral Dissertation]. Colorado State University; 2003. Available from: http://hdl.handle.net/10217/28552

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