Browsing by Author "Fabbri, Cameron"

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    Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks
    (2018-08) Fabbri, Cameron
    Autonomous robots rely on a variety of sensors – acoustic, inertial, and visual – for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality. However, many environments contain natural sources of visual noise such as snow, rain, dust, and other forms of distortion. This work focuses on the underwater environment, in which visual noise is a prominent component. Factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. Autonomous Underwater Vehicles (AUVs) that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision driven tasks. This thesis proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.
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    On Applications of GANs and Their Latent Representations
    (2018-07-09) Fabbri, Cameron; Sattar, Junaed
    This report describes various applications of Generative Adversarial Networks (GANs) for image generation, image-to-image translation, and vehicle control. With this, we also investigate the role played by the computed latent space, and show various ways of exploiting this space for controlled image generation and exploration. We show one pure generative method which we call AstroGAN that is able to generate realistic images of galaxies from a set of galaxy morphologies. Two image-to-image translation methods are also displayed: StereoGAN, which is able to generate a pair of stereo images given a single image; Underwater GAN, which is able to restore distorted imagery exhibited in underwater environments. Lastly, we show a generative model for generating actions in a simulated self-driving car environment.