34 citations · 62 across the 17 of their papers we have counts for
16 papers · 1 filter
Event-driven Robust Fitting on Neuromorphic Hardware
Tam Ngoc-Bang Nguyen, Anh-Dzung Doan, Zhipeng Cai +1
Robust fitting of geometric models is a fundamental task in many computer vision pipelines. Numerous innovations have been produced on the topic, from improving the efficiency and…
Simultaneous Diffusion Sampling for Conditional LiDAR Generation
Ryan Faulkner, Luke Haub, Simon Ratcliffe +3
By enabling capturing of 3D point clouds that reflect the geometry of the immediate environment, LiDAR has emerged as a primary sensor for autonomous systems. If a LiDAR scan is to…
TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation
Anh-Dzung Doan, Vu Minh Hieu Phan, Surabhi Gupta +3
Infrared imaging offers resilience against changing lighting conditions by capturing object temperatures. Yet, in few scenarios, its lack of visual details compared to daytime visi…
Weakly Supervised Test-Time Domain Adaptation for Object Detection
Anh-Dzung Doan, Bach Long Nguyen, Terry Lim +6
Prior to deployment, an object detector is trained on a dataset compiled from a previous data collection campaign. However, the environment in which the object detector is deployed…
Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection
Andrew Du, Anh-Dzung Doan, Yee Wei Law +1
The advent of satellite-borne machine learning hardware accelerators has enabled the on-board processing of payload data using machine learning techniques such as convolutional neu…
Assessing Domain Gap for Continual Domain Adaptation in Object Detection
Anh-Dzung Doan, Bach Long Nguyen, Surabhi Gupta +3
To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather,…
A Hybrid Quantum-Classical Algorithm for Robust Fitting
Anh-Dzung Doan, Michele Sasdelli, David Suter +1
Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do n…
Learning to Predict Repeatability of Interest Points
Anh-Dzung Doan, Daniyar Turmukhambetov, Yasir Latif +2
Many robotics applications require interest points that are highly repeatable under varying viewpoints and lighting conditions. However, this requirement is very challenging as the…
HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin +1
Visual place recognition needs to be robust against appearance variability due to natural and man-made causes. Training data collection should thus be an ongoing process to allow c…
In defense of OSVOS
Yu Liu, Yutong Dai, Anh-Dzung Doan +2
As a milestone for video object segmentation, one-shot video object segmentation (OSVOS) has achieved a large margin compared to the conventional optical-flow based methods regardi…
Scalable Place Recognition Under Appearance Change for Autonomous Driving
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin +3
A major challenge in place recognition for autonomous driving is to be robust against appearance changes due to short-term (e.g., weather, lighting) and long-term (seasons, vegetat…
Visual Localization Under Appearance Change: Filtering Approaches
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin +4
A major focus of current research on place recognition is visual localization for autonomous driving. In this scenario, as cameras will be operating continuously, it is realistic t…
G2D: from GTA to Data
Anh-Dzung Doan, Abdul Mohsi Jawaid, Thanh-Toan Do +1
This document describes G2D, a software that enables capturing videos from Grand Theft Auto V (GTA V), a popular role playing game set in an expansive virtual city. The target user…
On-device Scalable Image-based Localization via Prioritized Cascade Search and Fast One-Many RANSAC
Ngoc-Trung Tran, Dang-Khoa Le Tan, Anh-Dzung Doan +4
We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence…
Compact Hash Code Learning with Binary Deep Neural Network
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +2
Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challen…
Binary Hashing with Semidefinite Relaxation and Augmented Lagrangian
Thanh-Toan Do, Anh-Dzung Doan, Duc-Thanh Nguyen +1
This paper proposes two approaches for inferencing binary codes in two-step (supervised, unsupervised) hashing. We first introduce an unified formulation for both supervised and un…