Search Results for author: Mohammad Ali Armin

Found 15 papers, 5 papers with code

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.

Learning To Segment Dominant Object Motion From Watching Videos

no code implementations28 Nov 2021 Sahir Shrestha, Mohammad Ali Armin, Hongdong Li, Nick Barnes

Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train.

Optical Flow Estimation Semantic Segmentation +2

Declarative nets that are equilibrium models

no code implementations ICLR 2022 Russell Tsuchida, Suk Yee Yong, Mohammad Ali Armin, Lars Petersson, Cheng Soon Ong

We show that using a kernelised generalised linear model (kGLM) as an inner problem in a DDN yields a large class of commonly used DEQ architectures with a closed-form expression for the hidden layer parameters in terms of the kernel.

Blind Image Decomposition

1 code implementation25 Aug 2021 Junlin Han, Weihao Li, Pengfei Fang, Chunyi Sun, Jie Hong, Mohammad Ali Armin, Lars Petersson, Hongdong Li

We present and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown.

Rain Removal

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.


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

Dual Contrastive Learning for Unsupervised Image-to-Image Translation

3 code implementations15 Apr 2021 Junlin Han, Mehrdad Shoeiby, Lars Petersson, Mohammad Ali Armin

Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data.

Contrastive Learning Translation +1

Mosaic Super-resolution via Sequential Feature Pyramid Networks

no code implementations15 Apr 2020 Mehrdad Shoeiby, Mohammad Ali Armin, Sadegh Aliakbarian, Saeed Anwar, Lars Petersson

Additionally, to the best of our knowledge, our method is the first specialized method to super-resolve mosaic images, whether it be multi-spectral or Bayer.

Autonomous Driving Super-Resolution

Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network

no code implementations5 Sep 2019 Mehrdad Shoeiby, Lars Petersson, Mohammad Ali Armin, Sadegh Aliakbarian, Antonio Robles-Kelly

This paper introduces a novel method to simultaneously super-resolve and colour-predict images acquired by snapshot mosaic sensors.


Cannot find the paper you are looking for? You can Submit a new open access paper.