Search Results for author: Md Jahidul Islam

Found 13 papers, 9 papers with code

UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots

1 code implementation26 Sep 2022 Boxiao Yu, Jiayi Wu, Md Jahidul Islam

Subsequently, we extend this into a domain projection loss that guides the end-to-end learning of UDepth on over 9K RGB-D training samples.

Depth Prediction Monocular Depth Estimation

A Generative Approach for Detection-driven Underwater Image Enhancement

no code implementations10 Dec 2020 Chelsey Edge, Md Jahidul Islam, Christopher Morse, Junaed Sattar

In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain.

Generative Adversarial Network Image Enhancement

SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots

1 code implementation12 Nov 2020 Md Jahidul Islam, Ruobing Wang, Junaed Sattar

This paper presents a holistic approach to saliency-guided visual attention modeling (SVAM) for use by autonomous underwater robots.

object-detection Object Detection +2

IMU-Assisted Learning of Single-View Rolling Shutter Correction

1 code implementation5 Nov 2020 Jiawei Mo, Md Jahidul Islam, Junaed Sattar

In this paper, we propose a deep neural network to predict depth and row-wise pose from a single image for rolling shutter correction.

Pose Prediction Rolling Shutter Correction

Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

4 code implementations4 Feb 2020 Md Jahidul Islam, Peigen Luo, Junaed Sattar

In this paper, 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.

Image Enhancement Saliency Prediction +1

Underwater Image Super-Resolution using Deep Residual Multipliers

3 code implementations20 Sep 2019 Md Jahidul Islam, Sadman Sakib Enan, Peigen Luo, Junaed Sattar

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots.

Image Super-Resolution Scene Understanding

Fast Underwater Image Enhancement for Improved Visual Perception

3 code implementations23 Mar 2019 Md Jahidul Islam, Youya Xia, Junaed Sattar

In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement.

Ranked #4 on Underwater Image Restoration on LSUI (using extra training data)

Generative Adversarial Network Image Enhancement +5

Person Following by Autonomous Robots: A Categorical Overview

no code implementations22 Mar 2018 Md Jahidul Islam, Jungseok Hong, Junaed Sattar

Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of a person-following robot.


Enhancing Underwater Imagery using Generative Adversarial Networks

3 code implementations11 Jan 2018 Cameron Fabbri, Md Jahidul Islam, Junaed Sattar

Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making.

Ranked #3 on Underwater Image Restoration on LSUI (using extra training data)

Decision Making Underwater Image Restoration

Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration

no code implementations26 Sep 2017 Md Jahidul Islam, Marc Ho, Junaed Sattar

This paper presents a real-time programming and parameter reconfiguration method for autonomous underwater robots in human-robot collaborative tasks.

Hand Gesture Recognition Hand-Gesture Recognition

Underwater Multi-Robot Convoying using Visual Tracking by Detection

1 code implementation25 Sep 2017 Florian Shkurti, Wei-Di Chang, Peter Henderson, Md Jahidul Islam, Juan Camilo Gamboa Higuera, Jimmy Li, Travis Manderson, Anqi Xu, Gregory Dudek, Junaed Sattar

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.

object-detection Object Detection +1

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