+publisher:"University of North Carolina" +contributor:("Dunn, Enrique")

University of North Carolina

1. Wang, Yilin. Efficient Techniques for High Resolution Stereo.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:0ca95e06-f075-40f4-bba4-d8ec4b7fc369

► The purpose of stereo is extracting 3-dimensional (3D) information from 2-dimensional (2D) images, which is a fundamental problem in computer vision. In general, given a… (more)

▼ The purpose of stereo is extracting 3-dimensional (3D) information from 2-dimensional (2D) images, which is a fundamental problem in computer vision. In general, given a known imaging geometry the position of any 3D point observed by two or more different views can be recovered by triangulation, so 3D reconstruction task relies on figuring out the pixel’s correspondence between the reference and matching images. In general computational complexity of stereo algorithms is proportional to the image resolution (the total number of pixels) and the search space (the number of depth candidates). Hence, high resolution stereo tasks are not tractable for many existing stereo algorithms whose computational costs (including the processing time and the storage space) increase drastically with higher image resolution. The aim of this dissertation is to explore techniques aimed at improving the efficiency of high resolution stereo without any accuracy loss. The efficiency of stereo is the first focus of this dissertation. We utilize the implicit smoothness property of the local image patches and propose a general framework to reduce the search space of stereo. The accumulated matching costs (measured by the pixel similarity) are investigated to estimate the representative depths of the local patch. Then, a statistical analysis model for the search space reduction based on sequential probability ratio test is provided, and an optimal sampling scheme is proposed to find a complete and compact candidate depth set according to the structure of local regions. By integrating our optimal sampling schemes as a pre-processing stage, the performance of most existing stereo algorithms can be significantly improved. The accuracy of stereo algorithms is the second focus. We present a plane-based approach for the local geometry estimation combining with a parallel structure propagation algorithm, which outperforms most state-of-the-art stereo algorithms. To obtain precise local structures, we also address the problem of utilizing surface normals, and provide a framework to integrate color and normal information for high quality scene reconstruction. Advisors/Committee Members: Wang, Yilin, Frahm, Jan-Michael, Dunn, Enrique, Frahm, Jan-Michael, Dunn, Enrique, Fuchs, Henry, Niethammer, Marc, Mordohai, Philippos.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6^th Edition):

Wang, Y. (2016). Efficient Techniques for High Resolution Stereo. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:0ca95e06-f075-40f4-bba4-d8ec4b7fc369

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

Chicago Manual of Style (16^th Edition):

Wang, Yilin. “Efficient Techniques for High Resolution Stereo.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:0ca95e06-f075-40f4-bba4-d8ec4b7fc369.

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

MLA Handbook (7^th Edition):

Wang, Yilin. “Efficient Techniques for High Resolution Stereo.” 2016. Web. 18 Jan 2021.

Vancouver:

Wang Y. Efficient Techniques for High Resolution Stereo. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:0ca95e06-f075-40f4-bba4-d8ec4b7fc369.

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

Council of Science Editors:

Wang Y. Efficient Techniques for High Resolution Stereo. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:0ca95e06-f075-40f4-bba4-d8ec4b7fc369

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

University of North Carolina

2. Dou, Mingsong. Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras.

Degree: Computer Science, 2015, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:291f2a9e-855b-4cfb-b020-7a975eacf000

► 3D reconstruction of dynamic scenes can find many applications in areas such as virtual/augmented reality, 3D telepresence and 3D animation, while it is challenging to… (more)

▼ 3D reconstruction of dynamic scenes can find many applications in areas such as virtual/augmented reality, 3D telepresence and 3D animation, while it is challenging to achieve a complete and high quality reconstruction due to the sensor noise and occlusions in the scene. This dissertation demonstrates our efforts toward building a 3D capture system for room-sized dynamic environments. A key observation is that reconstruction insufficiency (e.g., incompleteness and noise) can be mitigated by accumulating data from multiple frames. In dynamic environments, dropouts in 3D reconstruction generally do not consistently appear in the same locations. Thus, accumulation of the captured 3D data over time can fill in the missing fragments. Reconstruction noise is reduced as well. The first piece of the system builds 3D models for room-scale static scenes with one hand-held depth sensor, where we use plane features, in addition to image salient points, for robust pairwise matching and bundle adjustment over the whole data sequence. In the second piece of the system, we designed a robust non-rigid matching algorithm that considers both dense point alignment and color similarity, so that the data sequence for a continuously deforming object captured by multiple depth sensors can be aligned together and fused into a high quality 3D model. We further extend this work for deformable object scanning with a single depth sensor. To deal with the drift problem, we designed a dense nonrigid bundle adjustment algorithm to simultaneously optimize for the final mesh and the deformation parameters of every frame. Finally, we integrate static scanning and nonrigid matching into a reconstruction system for room-sized dynamic environments, where we prescan the static parts of the scene and perform data accumulation for dynamic parts. Both rigid and nonrigid motions of objects are tracked in a unified framework, and close contacts between objects are also handled. The dissertation demonstrates significant improvements for dense reconstruction over state-of-the-art. Our plane-based scanning system for indoor environments delivers reliable reconstruction for challenging situations, such as lack of both visual and geometrical salient features. Our nonrigid alignment algorithm enables data fusion for deforming objects and thus achieves dramatically enhanced reconstruction. Our novel bundle adjustment algorithm handles dense input partial scans with nonrigid motion and outputs dense reconstruction with comparably high quality as the static scanning algorithm (e.g., KinectFusion). Finally, we demonstrate enhanced reconstruction results for room-sized dynamic environments by integrating the above techniques, which significantly advances state-of-the-art. Advisors/Committee Members: Dou, Mingsong, Fuchs, Henry, Cham, Tat-Jen, Dunn, Enrique, Frahm, Jan-Michael, Izadi, Shahram, Lastra, Anselmo.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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

APA (6^th Edition):

Dou, M. (2015). Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:291f2a9e-855b-4cfb-b020-7a975eacf000

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

Chicago Manual of Style (16^th Edition):

Dou, Mingsong. “Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:291f2a9e-855b-4cfb-b020-7a975eacf000.

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

MLA Handbook (7^th Edition):

Dou, Mingsong. “Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras.” 2015. Web. 18 Jan 2021.

Vancouver:

Dou M. Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:291f2a9e-855b-4cfb-b020-7a975eacf000.

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

Council of Science Editors:

Dou M. Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:291f2a9e-855b-4cfb-b020-7a975eacf000

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

University of North Carolina

3. Heinly, Jared. Toward Efficient and Robust Large-Scale Structure-from-Motion Systems.

Degree: Computer Science, 2015, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:49e7a86a-d94e-4d47-b5ae-0d6a015e4725

► The ever-increasing number of images that are uploaded and shared on the Internet has recently been leveraged by computer vision researchers to extract 3D information… (more)

▼ The ever-increasing number of images that are uploaded and shared on the Internet has recently been leveraged by computer vision researchers to extract 3D information about the content seen in these images. One key mechanism to extract this information is structure-from-motion, which is the process of recovering the 3D geometry (structure) of a scene via a set of images from different viewpoints (camera motion). However, when dealing with crowdsourced datasets comprised of tens or hundreds of millions of images, the magnitude and diversity of the imagery poses challenges such as robustness, scalability, completeness, and correctness for existing structure-from-motion systems. This dissertation focuses on these challenges and demonstrates practical methods to address the problems of data association and verification within structure-from-motion systems. Data association within structure-from-motion systems consists of the discovery of pairwise image overlap within the input dataset. In order to perform this discovery, previous systems assumed that information about every image in the input dataset could be stored in memory, which is prohibitive for large-scale photo collections. To address this issue, we propose a novel streaming-based framework for the discovery of related sets of images, and demonstrate our approach on a crowdsourced dataset containing 100 million images from all around the world. Results illustrate that our streaming-based approach does not compromise model completeness, but achieves unprecedented levels of efficiency and scalability. The verification of individual data associations is difficult to perform during the process of structure-from-motion, as standard methods have limited scope when determining image overlap. Therefore, it is possible for erroneous associations to form, especially when there are symmetric, repetitive, or duplicate structures which can be incorrectly associated with each other. The consequences of these errors are incorrectly placed cameras and scene geometry within the 3D reconstruction. We present two methods that can detect these local inconsistencies and successfully resolve them into a globally consistent 3D model. In our evaluation, we show that our techniques are efficient, are robust to a variety of scenes, and outperform existing approaches. Advisors/Committee Members: Heinly, Jared, Frahm, Jan-Michael, Dunn, Enrique, Berg, Alexander, Niethammer, Marc, Agarwal, Sameer.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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

APA (6^th Edition):

Heinly, J. (2015). Toward Efficient and Robust Large-Scale Structure-from-Motion Systems. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:49e7a86a-d94e-4d47-b5ae-0d6a015e4725

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

Chicago Manual of Style (16^th Edition):

Heinly, Jared. “Toward Efficient and Robust Large-Scale Structure-from-Motion Systems.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:49e7a86a-d94e-4d47-b5ae-0d6a015e4725.

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

MLA Handbook (7^th Edition):

Heinly, Jared. “Toward Efficient and Robust Large-Scale Structure-from-Motion Systems.” 2015. Web. 18 Jan 2021.

Vancouver:

Heinly J. Toward Efficient and Robust Large-Scale Structure-from-Motion Systems. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:49e7a86a-d94e-4d47-b5ae-0d6a015e4725.

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

Council of Science Editors:

Heinly J. Toward Efficient and Robust Large-Scale Structure-from-Motion Systems. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:49e7a86a-d94e-4d47-b5ae-0d6a015e4725

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

University of North Carolina

4. Ji, Dinghuang. Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements.

Degree: Computer Science, 2018, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d

► Our world is filled with living beings and other dynamic elements. It is important to record dynamic things and events for the sake of education,… (more)

▼ Our world is filled with living beings and other dynamic elements. It is important to record dynamic things and events for the sake of education, archeology, and culture inheritance. From vintage to modern times, people have recorded dynamic scene elements in different ways, from sequences of cave paintings to frames of motion pictures. This thesis focuses on two key computer vision techniques by which dynamic element representation moves beyond video capture: towards 3D reconstruction and view synthesis. Although previous methods on these two aspects have been adopted to model and represent static scene elements, dynamic scene elements present unique and difficult challenges for the tasks. This thesis focuses on three types of dynamic scene elements, namely 1) dynamic texture with static shape, 2) dynamic shapes with static texture, and 3) dynamic illumination of static scenes. Two research aspects will be explored to represent and visualize them: dynamic 3D reconstruction and dynamic view synthesis. Dynamic 3D reconstruction aims to recover the 3D geometry of dynamic objects and, by modeling the objects’ movements, bring 3D reconstructions to life. Dynamic view synthesis, on the other hand, summarizes or predicts the dynamic appearance change of dynamic objects – for example, the daytime-to-nighttime illumination of a building or the future movements of a rigid body. We first target the problem of reconstructing dynamic textures of objects that have (approximately) fixed 3D shape but time-varying appearance. Examples of such objects include waterfalls, fountains, and electronic billboards. Since the appearance of dynamic-textured objects can be random and complicated, estimating the 3D geometry of these objects from 2D images/video requires novel tools beyond the appearance-based point correspondence methods of traditional 3D computer vision. To perform this 3D reconstruction, we introduce a method that simultaneously 1) segments dynamically textured scene objects in the input images and 2) reconstructs the 3D geometry of the entire scene, assuming a static 3D shape for the dynamically textured objects. Compared to dynamic textures, the appearance change of dynamic shapes is due to physically defined motions like rigid body movements. In these cases, assumptions can be made about the object’s motion constraints in order to identify corresponding points on the object at different timepoints. For example, two points on a rigid object have constant distance between them in the 3D space, no matter how the object moves. Based on this assumption of local rigidity, we propose a robust method to correctly identify point correspondences of two images viewing the same moving object from different viewpoints and at different times. Dense 3D geometry could be obtained from the computed point correspondences. We apply this method on unsynchronized video streams, and observe that the number of inlier correspondences found by this method can be used as indicator for frame alignment among the different streams. … Advisors/Committee Members: Ji, Dinghuang, Frahm, Jan-Michael, Dunn, Enrique, Berg, Tamara, Niethammer, Marc, Savarese, Silvio.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6^th Edition):

Ji, D. (2018). Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d

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

Chicago Manual of Style (16^th Edition):

Ji, Dinghuang. “Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements.” 2018. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d.

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

MLA Handbook (7^th Edition):

Ji, Dinghuang. “Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements.” 2018. Web. 18 Jan 2021.

Vancouver:

Ji D. Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d.

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

Council of Science Editors:

Ji D. Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d

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

University of North Carolina

5. Kim, Hyo Jin. Learning Adaptive Representations for Image Retrieval and Recognition.

Degree: Computer Science, 2018, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:fe578de7-bc9f-461b-815a-83dcfd923878

► Content-based image retrieval is a core problem in computer vision. It has a wide range of application such as object and place recognition, digital library… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6^th Edition):

Kim, H. J. (2018). Learning Adaptive Representations for Image Retrieval and Recognition. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:fe578de7-bc9f-461b-815a-83dcfd923878

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

Chicago Manual of Style (16^th Edition):

Kim, Hyo Jin. “Learning Adaptive Representations for Image Retrieval and Recognition.” 2018. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:fe578de7-bc9f-461b-815a-83dcfd923878.

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

MLA Handbook (7^th Edition):

Kim, Hyo Jin. “Learning Adaptive Representations for Image Retrieval and Recognition.” 2018. Web. 18 Jan 2021.

Vancouver:

Kim HJ. Learning Adaptive Representations for Image Retrieval and Recognition. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:fe578de7-bc9f-461b-815a-83dcfd923878.

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

Council of Science Editors:

Kim HJ. Learning Adaptive Representations for Image Retrieval and Recognition. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:fe578de7-bc9f-461b-815a-83dcfd923878

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

University of North Carolina

6. Zheng, Enliang. TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d

► The goal of image-based 3D reconstruction is to construct a spatial understanding of the world from a collection of images. For applications that seek to… (more)

▼ The goal of image-based 3D reconstruction is to construct a spatial understanding of the world from a collection of images. For applications that seek to model generic real-world scenes, it is important that the reconstruction methods used are able to characterize both static scene elements (e.g. trees and buildings) as well as dynamic objects (e.g. cars and pedestrians). However, due to many inherent ambiguities in the reconstruction problem, recovering this 3D information with accuracy, robustness, and efficiency is a considerable challenge. To advance the research frontier for image-based 3D modeling, this dissertation focuses on three challenging problems in static scene and dynamic object reconstruction. We first target the problem of static scene depthmap estimation from crowd-sourced datasets (i.e. photos collected from the Internet). While achieving high-quality depthmaps using images taken under a controlled environment is already a difficult task, heterogeneous crowd-sourced data presents a unique set of challenges for multi-view depth estimation, including varying illumination and occasional occlusions. We propose a depthmap estimation method that demonstrates high accuracy, robustness, and scalability on a large number of photos collected from the Internet. Compared to static scene reconstruction, the problem of dynamic object reconstruction from monocular images is fundamentally ambiguous when not imposing any additional assumptions. This is because having only a single observation of an object is insufficient for valid 3D triangulation, which typically requires concurrent observations of the object from multiple viewpoints. Assuming that dynamic objects of the same class (e.g. all the pedestrians walking on a sidewalk) move in a common path in the real world, we develop a method that estimates the 3D positions of the dynamic objects from unstructured monocular images. Experiments on both synthetic and real datasets illustrate the solvability of the problem and the effectiveness of our approach. Finally, we address the problem of dynamic object reconstruction from a set of unsynchronized videos capturing the same dynamic event. This problem is of great interest because, due to the increased availability of portable capture devices, captures using multiple unsynchronized videos are common in the real world. To resolve the challenges that arises from non-concurrent captures and unknown temporal overlap among video streams, we propose a self-expressive dictionary learning framework, where the dictionary entries are defined as the collection of temporally varying structures. Experiments demonstrate the effectiveness of this approach to the previously unsolved problem. Advisors/Committee Members: Zheng, Enliang, Frahm, Jan-Michael, Dunn, Enrique, Berg, Tamara, Jojic, Vladimir, Sheikh, Yaser.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6^th Edition):

Zheng, E. (2016). TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d

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

Chicago Manual of Style (16^th Edition):

Zheng, Enliang. “TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d.

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

MLA Handbook (7^th Edition):

Zheng, Enliang. “TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS.” 2016. Web. 18 Jan 2021.

Vancouver:

Zheng E. TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d.

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

Council of Science Editors:

Zheng E. TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d

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

University of North Carolina

7. Xu, Yi. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

► The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection… (more)

▼ The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach… Advisors/Committee Members: Xu, Yi, Frahm, Jan-Michael, Monrose, Fabian, Dunn, Enrique, Berg, Tamara, Crandall, David.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6^th Edition):

Xu, Y. (2016). Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

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

Chicago Manual of Style (16^th Edition):

Xu, Yi. “Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41.

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

MLA Handbook (7^th Edition):

Xu, Yi. “Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.” 2016. Web. 18 Jan 2021.

Vancouver:

Xu Y. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41.

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

Council of Science Editors:

Xu Y. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

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

University of North Carolina

8. Wang, Ke. Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery.

Degree: Computer Science, 2018, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:a342d2a8-51a5-44c4-b4b5-8d9a1ac68700

► Recent years have witnessed the rapid growth of commercial satellite imagery. Compared with other imaging products, such as aerial or streetview imagery, modern satellite images… (more)

▼ Recent years have witnessed the rapid growth of commercial satellite imagery. Compared with other imaging products, such as aerial or streetview imagery, modern satellite images are captured at high resolution and with multiple spectral bands, thus provide unique viewing angles, global coverage, and frequent updates of the Earth surfaces. With automated processing and intelligent analysis algorithms, satellite images can enable global-scale 3D modeling applications. This dissertation explores computer vision algorithms to reconstruct 3D models from satellite images at different levels: geometric, semantic, and parametric reconstructions. However, reconstructing satellite imagery is particularly challenging for the following reasons: 1) Satellite images typically contain an enormous amount of raw pixels. Efficient algorithms are needed to minimize the substantial computational burden. 2) The ground sampling distances of satellite images are comparatively low. Visual entities, such as buildings, appear visually small and cluttered, thus posing difficulties for 3D modeling. 3) Satellite images usually have complex camera models and inaccurate vendor-provided camera calibrations. Rational polynomial coefficients (RPC) camera models, although widely used, need to be appropriately handled to ensure high-quality reconstructions. To obtain geometric reconstructions efficiently, we propose an edge-aware interpolation-based algorithm to obtain 3D point clouds from satellite image pairs. Initial 2D pixel matches are first established and triangulated to compensate the RPC calibration errors. Noisy dense correspondences can then be estimated by interpolating the inlier matches in an edge-aware manner. After refining the correspondence map with a fast bilateral solver, we can obtain dense 3D point clouds via triangulation. Pixel-wise semantic classification results for satellite images are usually noisy due to the negligence of spatial neighborhood information. Thus, we propose to aggregate multiple corresponding observations of the same 3D point to obtain high-quality semantic models. Instead of just leveraging geometric reconstructions to provide such correspondences, we formulate geometric modeling and semantic reasoning in a joint Markov Random Field (MRF) model. Our experiments show that both tasks can benefit from the joint inference. Finally, we propose a novel deep learning based approach to perform single-view parametric reconstructions from satellite imagery. By parametrizing buildings as 3D cuboids, our method simultaneously localizes building instances visible in the image and estimates their corresponding cuboid models. Aerial LiDAR and vectorized GIS maps are utilized as supervision. Our network upsamples CNN features to detect small but cluttered building instances. In addition, we estimate building contours through a separate fully convolutional network to avoid overlapping building cuboids. Advisors/Committee Members: Wang, Ke, Frahm, Jan-Michael, Dunn, Enrique, Berg, Alexander, Manocha, Dinesh, Niethammer, Marc, University of North Carolina at Chapel Hill.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6^th Edition):

Wang, K. (2018). Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:a342d2a8-51a5-44c4-b4b5-8d9a1ac68700

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

Chicago Manual of Style (16^th Edition):

Wang, Ke. “Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery.” 2018. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:a342d2a8-51a5-44c4-b4b5-8d9a1ac68700.

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

MLA Handbook (7^th Edition):

Wang, Ke. “Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery.” 2018. Web. 18 Jan 2021.

Vancouver:

Wang K. Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:a342d2a8-51a5-44c4-b4b5-8d9a1ac68700.

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

Council of Science Editors:

Wang K. Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:a342d2a8-51a5-44c4-b4b5-8d9a1ac68700

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

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