The effectiveness of robot autonomy is governed by the ability to make decisions based on online sensor measurements and a prior belief of the environment. Uncertainty in the environment introduces challenges to robotic decision making. This thesis address two key robot decision making problems: exploration and navigation. The robotic exploration...
This dissertation addresses the problem of semantic labeling of image pixels. In the course of our work, we considered different types of semantic labels, including object classes (e.g., car, person), 3D depth values (in the range 0 to 80 meters), and affordance classes (e.g., walkable, sittable). Semantic pixel labeling is...
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...
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...
This thesis is about visual relationship detection. This is an important task in computer vision. The goal is to detect all visual relationships in a given image between objects. This thesis presents a new approach to this problem. Our approach does not use an object detector as a common pre-processing...
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...
Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
The ability to create reproducible cryptographically secure keys from temporal environments (e.g., images) has the potential to be a contributor to effective cryptographic mechanisms. Due to the noisy nature of these environments, achieving this goal in a user friendly fashion is a very challenging task, especially since there exists a...
In this dissertation, we propose Ideal Thumbnail-Preserving Encryption (Ideal TPE), as a special case of format-preserving encryption, to balance image privacy and usability concerns in a cloud environment. We first introduce a concrete construction for Ideal TPE, that provably leaks nothing about the plaintext (unencrypted) image beyond its thumbnail. We...
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...