Object Localization, Segmentation, and Classification in 3D Images

<p>We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly to reduce potential errors propagated when solving these tasks independently.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2018

Author dc:creator

• Zelener, Allan

Advisor dc:contributor.advisor

• Ioannis Stamos

Committee members dc:contributor.committeemember

• Yingli Tian
• Andrew Rosenberg
• Philippos Mordohai

• Artificial Intelligence and Robotics
• Computer Sciences
• machine learning
• neural networks
• deep learning
• computer vision
• lidar

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/2531

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-3590