Index Catalog // ScholarsArchive@OSU

Deep Convolutional Network on Point Clouds for 3D Scene Understanding

Creator:: Wu, Wenxuan
Abstract:: As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
Resource Type:: Dissertation
Full Text:: AN ABSTRACT OF THE DISSERTATION OF Wenxuan Wu for the

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:: Khorram, Saeed
Abstract:: In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Resource Type:: Masters Thesis

Interactive player tracking for videos in American football

Creator:: Bawaskar, Amit
Abstract:: This thesis presents an interactive software tool for tracking a moving object in a video. In particular, we focus on the problem of tracking a player in American football videos. Object tracking is one of the fundamental problems in computer vision. It is one of the most important components in...
Resource Type:: Masters Thesis

Efficient Incremental Panorama Reconstruction from Multiple Videos

Creator:: Feng, Zhongyuan
Abstract:: Constructing a panorama from a set of videos is a long-standing problem in computer vision. A panorama represents an enhanced still-image representation of an entire scene captured in a set of videos, where each video shows only a part of the scene. Importantly, a panorama shows only the scene background,...
Resource Type:: Capstone Project

Fine-Grained Object Recognition Under Limited Training Data

Creator:: Lam, Michael Q.
Abstract:: This dissertation addresses object recognition in challenging settings, where distinct object classes are visually very similar (e.g., species of birds and insects) and/or access to training examples of object classes is limited (e.g., due to the associated high costs of data annotation). In this dissertation, we present a variety of...
Resource Type:: Dissertation

Semantic embodied navigation : developing agents that navigate from language and vision

Creator:: Krantz, Jacob
Abstract:: Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...
Resource Type:: Dissertation

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

Creator:: Nguyen, Khoi D.
Abstract:: This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
Resource Type:: Dissertation

Characterizing and Addressing Dataset Shift with Deep Learning

Creator:: Olson, Matthew L.
Abstract:: This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
Resource Type:: Dissertation

Network Coding, Random Matrices, and Their Applications to Communication Systems

Creator:: Nguyen-Huu, Duong
Abstract:: In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
Resource Type:: Dissertation

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

Creator:: Khorram, Saeed
Abstract:: The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
Resource Type:: Dissertation

ScholarsArchive@OSU

Deep Convolutional Network on Point Clouds for 3D Scene Understanding

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Interactive player tracking for videos in American football

Efficient Incremental Panorama Reconstruction from Multiple Videos

Fine-Grained Object Recognition Under Limited Training Data

Semantic embodied navigation : developing agents that navigate from language and vision

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

Characterizing and Addressing Dataset Shift with Deep Learning

Network Coding, Random Matrices, and Their Applications to Communication Systems

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

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