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| Saliency-guided Visual Attention Modeling (SVAM) | Preprint | |
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Diver Face Recognition for Underwater HRI We present a deep-learned facial recognition method for underwater robots to identify scuba divers. Specifically, the proposed method is able to recognize divers underwater with faces heavily obscured by scuba masks and breathing apparatus. Our proposed framework is able to learn discriminative features from real-world diver faces through different data augmentation and generation techniques. Investigator: Jungseok Hong, Sadman Sakib Enan |
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Deep SESR: Simultaneous Enhancement and Super-Resolution We introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications: Deep SESR. We also present UFO-120, the first dataset to facilitate large-scale SESR learning. Investigator: Md Jahidul Islam, Peigen Luo |
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Semantic Segmentation of Underwater Imagery: Dataset and Benchmark We introduce the first large-scale dataset for semantic segmentation of underwater imagery (SUIM). We conduct a comprehensive benchmark evaluation of several state-of-the-art semantic segmentation approaches based on standard performance metrics. We also present SUIM-Net, a fully-convolutional deep residual model that balances the tradeoff between performance and computational efficiency. |
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Fast Underwater Image Enhancement: FUnIE-GAN We present a fast GAN-based model for real-time underwater image enhancement: FUnIE-GAN. We also present a large-scale dataset named EUVP to facilitate paired and unpaired learning of underwater image enhancement. Investigator: Md Jahidul Islam, Youya Xia |
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Underwater Image Super-Resolution using Deep Residual Multipliers: SRDRM We present a deep residual network-based generative model for single image super-resolution underwater (SISR), which we refer to as SRDRM. We also formulate its adversarial training pipeline, i.e., SRDM-GAN. Additionally, we present USR-248, a large-scale dataset that contains paired instances for supervised training of 2x, 4x, or 8x SISR models. Investigator: Md Jahidul Islam, Sadman Sakib Enan, Peigen Luo |
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Balancing Robustness and Efficiency in Deep Diver Detection Design and development of a class of autonomous diver-following algorithms that are: a) invariant to color (of divers’ body/wearables), b) invariant to divers’ relative motion and orientation, c) robust to noise and image distortions, and d) reasonably efficient for real-time deployment. Investigator: Md Jahidul Islam, Michael Fulton |
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Visual Diver Recognition for Underwater Human-Robot Collaboration This paper presents an approach for autonomous underwater robots to visually detect and identify divers. The proposed approach enables an autonomous underwater robot to detect multiple divers in a visual scene and distinguish between them. Investigator: Youya Xia
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Robot Communication Via Motion Developing new modalities of interaction for 6DOF robots using motion as the primary robot-to-human communication vector, to be used in challenging environments. Investigator: Michael Fulton |
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Investigating and applying methods for object detection to the detection of marine debris, particularly plastic trash, with the eventual purpose of removal and monitoring. Investigator: Michael Fulton, Jungseok Hong |
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Investigating robust methods for finding lane markers under severely degraded visibility, for assisted and autonomous driving. Investigator: Jiawei Mo |
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Mixed-Domain Periodic Motion (MDPM) Tracker Robust algorithm for an autonomous underwater robot to visually detect and track its companion human diver swimming in arbitrary directions. Investigator: Md Jahidul Islam |
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A real-time programming and parameter reconfiguration method for autonomous underwater robots using a set of hand-gestures Investigator: Md Jahidul Islam, Marc Ho |
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Adversarial Image_Colorization Using Generative Adversarial Networks for automatic image colorization. |
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Using deep learning for restoring underwater images. Investigator: Cameron Fabbri, Md Jahidul Islam |
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smartTalk: A Learning-based Framework for Natural Human-Robot Dialog Investigator: Cameron Fabbri |
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