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You searched for +publisher:"Cornell University" +contributor:("Bala, Kavita"). Showing records 1 – 12 of 12 total matches.

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Cornell University

1. Wu, Eric. Adding Model Uncertainty to Depth Prediction.

Degree: M.S., Computer Science, Computer Science, 2019, Cornell University

 Disparity and depth estimation of images is a fundamental problem for computer vision. Recent work has shown that convolutional neural networks are effective at both… (more)

Subjects/Keywords: Computer science

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APA (6th Edition):

Wu, E. (2019). Adding Model Uncertainty to Depth Prediction. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/67749

Chicago Manual of Style (16th Edition):

Wu, Eric. “Adding Model Uncertainty to Depth Prediction.” 2019. Masters Thesis, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/67749.

MLA Handbook (7th Edition):

Wu, Eric. “Adding Model Uncertainty to Depth Prediction.” 2019. Web. 05 Dec 2020.

Vancouver:

Wu E. Adding Model Uncertainty to Depth Prediction. [Internet] [Masters thesis]. Cornell University; 2019. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/67749.

Council of Science Editors:

Wu E. Adding Model Uncertainty to Depth Prediction. [Masters Thesis]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/67749


Cornell University

2. Gardner, Jacob. Discovering and Exploiting Structure for Gaussian Processes.

Degree: PhD, Computer Science, 2018, Cornell University

 Gaussian processes have emerged as a powerful tool for modeling complex and noisy functions. They have found wide applicability in personalized medicine, time series analysis,… (more)

Subjects/Keywords: Artificial intelligence

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APA (6th Edition):

Gardner, J. (2018). Discovering and Exploiting Structure for Gaussian Processes. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59460

Chicago Manual of Style (16th Edition):

Gardner, Jacob. “Discovering and Exploiting Structure for Gaussian Processes.” 2018. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/59460.

MLA Handbook (7th Edition):

Gardner, Jacob. “Discovering and Exploiting Structure for Gaussian Processes.” 2018. Web. 05 Dec 2020.

Vancouver:

Gardner J. Discovering and Exploiting Structure for Gaussian Processes. [Internet] [Doctoral dissertation]. Cornell University; 2018. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/59460.

Council of Science Editors:

Gardner J. Discovering and Exploiting Structure for Gaussian Processes. [Doctoral Dissertation]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59460


Cornell University

3. Wehrwein, Scott. Light and Motion: Modeling and Visualizing How Scenes Change Over Time.

Degree: PhD, Computer Science, 2018, Cornell University

 The visible world changes in many ways as time passes. Image and video data capturing these changes is plentiful, but analyzing such data presents many… (more)

Subjects/Keywords: Computer science

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APA (6th Edition):

Wehrwein, S. (2018). Light and Motion: Modeling and Visualizing How Scenes Change Over Time. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59739

Chicago Manual of Style (16th Edition):

Wehrwein, Scott. “Light and Motion: Modeling and Visualizing How Scenes Change Over Time.” 2018. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/59739.

MLA Handbook (7th Edition):

Wehrwein, Scott. “Light and Motion: Modeling and Visualizing How Scenes Change Over Time.” 2018. Web. 05 Dec 2020.

Vancouver:

Wehrwein S. Light and Motion: Modeling and Visualizing How Scenes Change Over Time. [Internet] [Doctoral dissertation]. Cornell University; 2018. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/59739.

Council of Science Editors:

Wehrwein S. Light and Motion: Modeling and Visualizing How Scenes Change Over Time. [Doctoral Dissertation]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59739


Cornell University

4. Wilson, Kyle. Robustly Modeling The World From Photos.

Degree: PhD, Applied Mathematics, 2016, Cornell University

 A camera is a device for compressing rich information about the visual appearance of the three-dimensional world into a two-dimensional image. This process is inherently… (more)

Subjects/Keywords: Computer Vision; Structure from Motion; 3D Reconstruction

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APA (6th Edition):

Wilson, K. (2016). Robustly Modeling The World From Photos. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/44330

Chicago Manual of Style (16th Edition):

Wilson, Kyle. “Robustly Modeling The World From Photos.” 2016. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/44330.

MLA Handbook (7th Edition):

Wilson, Kyle. “Robustly Modeling The World From Photos.” 2016. Web. 05 Dec 2020.

Vancouver:

Wilson K. Robustly Modeling The World From Photos. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/44330.

Council of Science Editors:

Wilson K. Robustly Modeling The World From Photos. [Doctoral Dissertation]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44330


Cornell University

5. Khungurn, Pramook. Modeling and Rendering Appearance of Hair and Textile Fibers.

Degree: PhD, Computer Science, 2017, Cornell University

 Fibers are ubiquitous in our visual world. Hair is an important part of our appearance, and we wear and use clothes made from various types… (more)

Subjects/Keywords: Computer science; Computer Graphics; appearance modeling; light transport; physical simulation; reflectance; rendering

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APA (6th Edition):

Khungurn, P. (2017). Modeling and Rendering Appearance of Hair and Textile Fibers. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/51596

Chicago Manual of Style (16th Edition):

Khungurn, Pramook. “Modeling and Rendering Appearance of Hair and Textile Fibers.” 2017. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/51596.

MLA Handbook (7th Edition):

Khungurn, Pramook. “Modeling and Rendering Appearance of Hair and Textile Fibers.” 2017. Web. 05 Dec 2020.

Vancouver:

Khungurn P. Modeling and Rendering Appearance of Hair and Textile Fibers. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/51596.

Council of Science Editors:

Khungurn P. Modeling and Rendering Appearance of Hair and Textile Fibers. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/51596


Cornell University

6. Boyadzhiev, Ivaylo. Computational Lighting Design And Image Filtering For Material Enhancement.

Degree: PhD, Computer Science, 2015, Cornell University

 Photography provides a powerful tool for depicting the world around us by capturing the intricate relationship between light and materials. Indeed, much of the art… (more)

Subjects/Keywords: computational lighting design; multi-lights white balance; image-based material editing

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APA (6th Edition):

Boyadzhiev, I. (2015). Computational Lighting Design And Image Filtering For Material Enhancement. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/41097

Chicago Manual of Style (16th Edition):

Boyadzhiev, Ivaylo. “Computational Lighting Design And Image Filtering For Material Enhancement.” 2015. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/41097.

MLA Handbook (7th Edition):

Boyadzhiev, Ivaylo. “Computational Lighting Design And Image Filtering For Material Enhancement.” 2015. Web. 05 Dec 2020.

Vancouver:

Boyadzhiev I. Computational Lighting Design And Image Filtering For Material Enhancement. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/41097.

Council of Science Editors:

Boyadzhiev I. Computational Lighting Design And Image Filtering For Material Enhancement. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/41097

7. Getman, Rachel Rose. Machine Learning (ML) For Tracking the Geo-Temporality of a Trend: Documenting the Frequency of the Baseball-Trucker Hat on Social Media and the Runway.

Degree: MA, Fiber Science and Apparel Design, 2019, Cornell University

 The study applied fine-grained Machine Learning (ML) to document the frequency of baseball-trucker hats on social media with images populated from the Matzen et al.… (more)

Subjects/Keywords: trend visualization; Artificial intelligence; Fashion; athleisure; baseball hat; deep-learning; normcore; machine learning

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APA (6th Edition):

Getman, R. R. (2019). Machine Learning (ML) For Tracking the Geo-Temporality of a Trend: Documenting the Frequency of the Baseball-Trucker Hat on Social Media and the Runway. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/67455

Chicago Manual of Style (16th Edition):

Getman, Rachel Rose. “Machine Learning (ML) For Tracking the Geo-Temporality of a Trend: Documenting the Frequency of the Baseball-Trucker Hat on Social Media and the Runway.” 2019. Masters Thesis, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/67455.

MLA Handbook (7th Edition):

Getman, Rachel Rose. “Machine Learning (ML) For Tracking the Geo-Temporality of a Trend: Documenting the Frequency of the Baseball-Trucker Hat on Social Media and the Runway.” 2019. Web. 05 Dec 2020.

Vancouver:

Getman RR. Machine Learning (ML) For Tracking the Geo-Temporality of a Trend: Documenting the Frequency of the Baseball-Trucker Hat on Social Media and the Runway. [Internet] [Masters thesis]. Cornell University; 2019. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/67455.

Council of Science Editors:

Getman RR. Machine Learning (ML) For Tracking the Geo-Temporality of a Trend: Documenting the Frequency of the Baseball-Trucker Hat on Social Media and the Runway. [Masters Thesis]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/67455

8. Hauagge, Daniel Cabrini. Vision Under Changing Scene Appearance: Describing The World Through Light And Symmetries.

Degree: PhD, Computer Science, 2014, Cornell University

 Change is an inexorable aspect of the world that surrounds us. Night gives way to day as the Earth rotates around its axis, weather changes,… (more)

Subjects/Keywords: Ambient occlusion; Intrinsic images; Local symmetry

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APA (6th Edition):

Hauagge, D. C. (2014). Vision Under Changing Scene Appearance: Describing The World Through Light And Symmetries. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/38788

Chicago Manual of Style (16th Edition):

Hauagge, Daniel Cabrini. “Vision Under Changing Scene Appearance: Describing The World Through Light And Symmetries.” 2014. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/38788.

MLA Handbook (7th Edition):

Hauagge, Daniel Cabrini. “Vision Under Changing Scene Appearance: Describing The World Through Light And Symmetries.” 2014. Web. 05 Dec 2020.

Vancouver:

Hauagge DC. Vision Under Changing Scene Appearance: Describing The World Through Light And Symmetries. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/38788.

Council of Science Editors:

Hauagge DC. Vision Under Changing Scene Appearance: Describing The World Through Light And Symmetries. [Doctoral Dissertation]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/38788

9. Savva, Nicolas. A Practical Framework for Measuring and Modeling the Appearance of Strongly Anisotropic Materials.

Degree: M.S., Computer Science, Computer Science, 2017, Cornell University

 We present a practical sparse measurement technique and a novel parameter fitting approach for the appearance of strongly anisotropic materials, with application to finished wood.… (more)

Subjects/Keywords: Computer science; Appearance Model; BRDF; Computer Graphics; Material Acquisition; SV-BRDF

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APA (6th Edition):

Savva, N. (2017). A Practical Framework for Measuring and Modeling the Appearance of Strongly Anisotropic Materials. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/47723

Chicago Manual of Style (16th Edition):

Savva, Nicolas. “A Practical Framework for Measuring and Modeling the Appearance of Strongly Anisotropic Materials.” 2017. Masters Thesis, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/47723.

MLA Handbook (7th Edition):

Savva, Nicolas. “A Practical Framework for Measuring and Modeling the Appearance of Strongly Anisotropic Materials.” 2017. Web. 05 Dec 2020.

Vancouver:

Savva N. A Practical Framework for Measuring and Modeling the Appearance of Strongly Anisotropic Materials. [Internet] [Masters thesis]. Cornell University; 2017. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/47723.

Council of Science Editors:

Savva N. A Practical Framework for Measuring and Modeling the Appearance of Strongly Anisotropic Materials. [Masters Thesis]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/47723

10. Upchurch, Paul. Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks.

Degree: PhD, Computer Science, 2018, Cornell University

 Fully automatic processing of images is a key challenge for the 21st century. Our processing needs lie beyond just organizing photos by date and location.… (more)

Subjects/Keywords: computer vision; machine learning; Computer science

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APA (6th Edition):

Upchurch, P. (2018). Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59808

Chicago Manual of Style (16th Edition):

Upchurch, Paul. “Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks.” 2018. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/59808.

MLA Handbook (7th Edition):

Upchurch, Paul. “Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks.” 2018. Web. 05 Dec 2020.

Vancouver:

Upchurch P. Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks. [Internet] [Doctoral dissertation]. Cornell University; 2018. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/59808.

Council of Science Editors:

Upchurch P. Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks. [Doctoral Dissertation]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59808

11. Zhao, Shuang. Modeling And Rendering Fabrics At Micron-Resolution.

Degree: PhD, Computer Science, 2014, Cornell University

 Fabrics are essential to our everyday lives. Recently advances in physically based rendering techniques and computing power have made it possible to accurately model and… (more)

Subjects/Keywords: Computer graphics; Material appearance modeling; Physically based rendering

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APA (6th Edition):

Zhao, S. (2014). Modeling And Rendering Fabrics At Micron-Resolution. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/39013

Chicago Manual of Style (16th Edition):

Zhao, Shuang. “Modeling And Rendering Fabrics At Micron-Resolution.” 2014. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/39013.

MLA Handbook (7th Edition):

Zhao, Shuang. “Modeling And Rendering Fabrics At Micron-Resolution.” 2014. Web. 05 Dec 2020.

Vancouver:

Zhao S. Modeling And Rendering Fabrics At Micron-Resolution. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/39013.

Council of Science Editors:

Zhao S. Modeling And Rendering Fabrics At Micron-Resolution. [Doctoral Dissertation]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/39013


Cornell University

12. Bell, Sean Cameron. Understanding Visual Appearance On The Web Using Large-Scale Crowdsourcing And Deep Learning.

Degree: PhD, Computer Science, 2016, Cornell University

Subjects/Keywords: Crowdsourcing; Deep learning; Material recognition

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APA (6th Edition):

Bell, S. C. (2016). Understanding Visual Appearance On The Web Using Large-Scale Crowdsourcing And Deep Learning. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/45344

Chicago Manual of Style (16th Edition):

Bell, Sean Cameron. “Understanding Visual Appearance On The Web Using Large-Scale Crowdsourcing And Deep Learning.” 2016. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/45344.

MLA Handbook (7th Edition):

Bell, Sean Cameron. “Understanding Visual Appearance On The Web Using Large-Scale Crowdsourcing And Deep Learning.” 2016. Web. 05 Dec 2020.

Vancouver:

Bell SC. Understanding Visual Appearance On The Web Using Large-Scale Crowdsourcing And Deep Learning. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/45344.

Council of Science Editors:

Bell SC. Understanding Visual Appearance On The Web Using Large-Scale Crowdsourcing And Deep Learning. [Doctoral Dissertation]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/45344

.