Index Catalog // ScholarsArchive@OSU

Robust and Efficient Classification of Videos in the Wild

Creator:: Mahasseni, Behrooz
Abstract:: Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
Resource Type:: Dissertation

Object detection in biological images using a search-based framework

Creator:: Lam, Michael
Abstract:: This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
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

Analysis of bio-based composites for image segmentation with the aid of games

Creator:: Inouye, Jennifer A.
Abstract:: A fundamental problem in computer vision is to partition an image into meaningful segments. While image segmentation is required by many applications, the thesis focuses on segmentation of computed tomography (CT) images for analysis and quality control of composite materials. The key research contribution of this thesis is a novel...
Resource Type:: Masters Thesis

From shape-based object recognition and discovery to 3D scene interpretation

Creator:: Payet, Nadia
Abstract:: This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in images, as well as 3D scene reconstruction and scene interpretation. The key ideas behind our approaches include using shape as a basic object feature, and...
Resource Type:: Dissertation

From multitarget tracking to event recognition in videos

Creator:: Brendel, William
Abstract:: This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from a host of sources, including motion blur, occlusions, and dynamic cluttered backgrounds. We show that these challenges can be successfully addressed by using a multiscale,...
Resource Type:: Dissertation

Hierarchical graphical models for activity recognition in videos

Creator:: Amer, Mohamed R.
Abstract:: This dissertation addresses the problem of recognizing human activities in videos. Our focus is on activities with stochastic structure, where the activities are characterized by variable space-time arrangements of actions, and conducted by a variable number of actors. These activities occur frequently in sports and surveillance videos. They may appear...
Resource Type:: Dissertation

Recognizing human group activities in video through mining optimal features

Creator:: Amer, Mohamed R.
Abstract:: Given a video, we would like to recognize group activities, localize video parts where these activities occur, and detect actors involved in them. To this and, we propose a novel, mid-level feature, called control point, for representing group activities. The control points are aimed at summarizing visual cues, lifting from...
Resource Type:: Masters Thesis

A Deep Action Segmentation and Its Explanation with a Dictionary of Meaningful Attention Maps

Creator:: Trigkakis, Dimitrios
Abstract:: This thesis addresses the problem of temporal action segmentation in videos, where the goal is to label every video frame with the appropriate action class present. We focus on the domain of NFL football videos, where action classes represent common football play types. For action segmentation, we use a temporal...
Resource Type:: Masters Thesis

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

ScholarsArchive@OSU

Robust and Efficient Classification of Videos in the Wild

Object detection in biological images using a search-based framework

Interactive player tracking for videos in American football

Analysis of bio-based composites for image segmentation with the aid of games

From shape-based object recognition and discovery to 3D scene interpretation

From multitarget tracking to event recognition in videos

Hierarchical graphical models for activity recognition in videos

Recognizing human group activities in video through mining optimal features

A Deep Action Segmentation and Its Explanation with a Dictionary of Meaningful Attention Maps

Fine-Grained Object Recognition Under Limited Training Data

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