Search Results for author: David Ahmedt-Aristizabal

Found 12 papers, 0 papers with code

Continuous Human Action Recognition for Human-Machine Interaction: A Review

no code implementations26 Feb 2022 Harshala Gammulle, David Ahmedt-Aristizabal, Simon Denman, Lachlan Tychsen-Smith, Lars Petersson, Clinton Fookes

With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams.

Action Recognition Action Segmentation +2

Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study

no code implementations22 Feb 2022 Thisun Dayarathna, Thamidu Muthukumarana, Yasiru Rathnayaka, Simon Denman, Chathura de Silva, Akila Pemasiri, David Ahmedt-Aristizabal

In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings.

Pose Estimation Privacy Preserving

The CSIRO Crown-of-Thorn Starfish Detection Dataset

no code implementations29 Nov 2021 Jiajun Liu, Brano Kusy, Ross Marchant, Brendan Do, Torsten Merz, Joey Crosswell, Andy Steven, Nic Heaney, Karl Von Richter, Lachlan Tychsen-Smith, David Ahmedt-Aristizabal, Mohammad Ali Armin, Geoffrey Carlin, Russ Babcock, Peyman Moghadam, Daniel Smith, Tim Davis, Kemal El Moujahid, Martin Wicke, Megha Malpani

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.

A Survey on Graph-Based Deep Learning for Computational Histopathology

no code implementations1 Jul 2021 David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson

With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches.

graph construction Image Retrieval +3

Towards Interpretable Attention Networks for Cervical Cancer Analysis

no code implementations27 May 2021 Ruiqi Wang, Mohammad Ali Armin, Simon Denman, Lars Petersson, David Ahmedt-Aristizabal

Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells.

Classification

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

no code implementations27 May 2021 David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson

It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.

Medical Diagnosis

Neural Memory Networks for Seizure Type Classification

no code implementations10 Dec 2019 David Ahmedt-Aristizabal, Tharindu Fernando, Simon Denman, Lars Petersson, Matthew J. Aburn, Clinton Fookes

Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.

Classification EEG +2

Neural Memory Plasticity for Anomaly Detection

no code implementations12 Oct 2019 Tharindu Fernando, Simon Denman, David Ahmedt-Aristizabal, Sridha Sridharan, Kristin Laurens, Patrick Johnston, Clinton Fookes

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling.

Anomaly Detection EEG +5

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