Skip to main content

Low to High Dimensional Modality Reconstruction Using Aggregated Fields of View

Abstract Autonomous systems that are out in the real world today deal with a slew of different data modalities to perform effectively in tasks ranging from robot navigation in complex maneuverable robots to identity verification in simpler static systems. The performance of the system heavily banks on the continuous supply of data from all modalities. These systems can face drastically increased risk with the loss of one or multiple modalities due to an adverse scenario like that of hardware malfunction, inimical environmental conditions, etc. This thesis investigates modality hallucination and its efficacy in mitigating the risks posed to the autonomous system. Modality hallucination is proposed as one effective way to ensure consistent modality av... (more)
Created Date 2019
Contributor Gunasekar, Kausic (Author) / Yang, Yezhou (Advisor) / Qiu, Qiang (Committee member) / Amor, Heni Ben (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / Robotics / depth to RGB / Modality Hallucination / Multi-modal systems
Type Masters Thesis
Extent 73 pages
Language English
Note Masters Thesis Computer Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

  Full Text
40.1 MB application/pdf
Download Count: 0

Description Dissertation/Thesis