Michał Mackiewicz
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View article: Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring Open
Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analy…
View article: Optimised Spherical Sampling of the Object Colour Solid
Optimised Spherical Sampling of the Object Colour Solid Open
We propose a new method for approximating object colour solids, which we call Optimised Spherical Sampling. We compare our new method to the previously described methods based on 1) the two-transition conjecture of Schrodinger and 2) Rando…
View article: Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361. Open
This dataset contains the video clips used to produce the results presented in: Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image…
View article: Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361. Open
This dataset contains the video clips used to produce the results presented in: Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image…
View article: Computer Vision Pipeline for Automated Antarctic Krill Analysis
Computer Vision Pipeline for Automated Antarctic Krill Analysis Open
British Antarctic Survey (BAS) researchers launch annual expeditions to the Antarctic in order to estimate Antarctic Krill biomass and assess the change from previous years. These comparisons provide insight into the effects of the current…
View article: Crowdsourcing Experiment and Fully Convolutional Neural Networks for Coastal Remote Sensing of Seagrass and Macroalgae
Crowdsourcing Experiment and Fully Convolutional Neural Networks for Coastal Remote Sensing of Seagrass and Macroalgae Open
Recently, convolutional neural networks and fully convolutional neural networks (FCNs) have been successfully used for monitoring coastal marine ecosystems, in particular vegetation. However, even with recent advances in computational mode…
View article: Extending Temporal Data Augmentation for Video Action Recognition
Extending Temporal Data Augmentation for Video Action Recognition Open
Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost. Data augmentation for videos, however, still remains an under-explored research topic, as most …
View article: Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels
Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels Open
A system that reconstructs 3D models from a single camera monitoring fish transported on a conveyor system is investigated. Models are subsequently used for training a species classifier and for improving estimates of discarded biomass. It…
View article: Colour Augmentation for Improved Semi-supervised Semantic Segmentation
Colour Augmentation for Improved Semi-supervised Semantic Segmentation Open
Consistency regularization describes a class of approaches that have yielded state-of-the-art results for semi-supervised classification. While semi-supervised semantic segmentation proved to be more challenging, recent work has explored t…
View article: Colour augmentation for improved semi-supervised semantic segmentation
Colour augmentation for improved semi-supervised semantic segmentation Open
Consistency regularization describes a class of approaches that have yielded state-of-the-art results for semi-supervised classification. While semi-supervised semantic segmentation proved to be more challenging, a number of successful app…
View article: Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery
Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery Open
Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to hum…
View article: Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery
Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery Open
Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to hum…
View article: Virtual Adversarial Training in Feature Space to Improve Unsupervised Video Domain Adaptation
Virtual Adversarial Training in Feature Space to Improve Unsupervised Video Domain Adaptation Open
Virtual Adversarial Training has recently seen a lot of success in semi-supervised learning, as well as unsupervised Domain Adaptation. However, so far it has been used on input samples in the pixel space, whereas we propose to apply it di…
View article: SVW-UCF Dataset for Video Domain Adaptation
SVW-UCF Dataset for Video Domain Adaptation Open
Unsupervised video domain adaptation (DA) has recently seen a lot of success, achieving almost if not perfect results on the majority of various benchmark datasets. Therefore, the next natural step for the field is to come up with new, mor…
View article: Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms
Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms Open
Human enterprise often suffers from direct negative effects caused by\njellyfish blooms. The investigation of a prior jellyfish monitoring system\nshowed that it was unable to reliably perform in a cross validation setting,\ni.e. in new un…
View article: Using Deep Learning to Count Albatrosses from Space: Assessing Results in Light of Ground Truth Uncertainty
Using Deep Learning to Count Albatrosses from Space: Assessing Results in Light of Ground Truth Uncertainty Open
Many wildlife species inhabit inaccessible environments, limiting researchers ability to conduct essential population surveys. Recently, very high resolution (sub-metre) satellite imagery has enabled remote monitoring of certain species di…
View article: Semi-supervised semantic segmentation needs strong, high-dimensional perturbations
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations Open
Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists…
View article: Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards
Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards Open
We report on the development of a computer vision system that analyses video from CCTV systems installed on fishing trawlers for the purpose of monitoring and quantifying discarded fish catch. Our system is designed to operate in spite of …
View article: Using Deep Learning To Count Albatrosses From Space
Using Deep Learning To Count Albatrosses From Space Open
In this paper we test the use of a deep learning approach to automatically count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery. We use a dataset of manually labelled imagery provided by the British Antarctic Survey …
View article: Semi-supervised semantic segmentation needs strong, varied perturbations
Semi-supervised semantic segmentation needs strong, varied perturbations Open
Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists…
View article: Spherical sampling methods for the calculation of metamer mismatch volumes
Spherical sampling methods for the calculation of metamer mismatch volumes Open
In this paper, we propose two methods of calculating theoretically maximal metamer mismatch volumes. Unlike prior art techniques, our methods do not make any assumptions on the shape of spectra on the boundary of the mismatch volumes. Both…
View article: Multi-spectral Pedestrian Detection via Image Fusion and Deep Neural Networks
Multi-spectral Pedestrian Detection via Image Fusion and Deep Neural Networks Open
The use of multi-spectral imaging has been found to improve the accuracy of deep neural network-based pedestrian detection systems, particularly in challenging night time conditions in which pedestrians are more clearly visible in thermal …
View article: Colour Correction Toolbox
Colour Correction Toolbox Open
For a camera image, the RGB response from the imaging sensor cannot be used to drive display devices directly. The reason behind this is two-fold: different cameras have different spectral sensitivities, and there are different target outp…
View article: Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat
Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat Open
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental chang…
View article: Self-ensembling for visual domain adaptation
Self-ensembling for visual domain adaptation Open
This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that achieved st…
View article: MOESM1 of Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat
MOESM1 of Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat Open
Additional file 1. The interactive Jupyter Notebook version for Leaf-GP (version 1.18).
View article: MOESM4 of Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat
MOESM4 of Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat Open
Additional file 4. Multiple trait measurements results based on a testing series.
View article: MOESM5 of Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat
MOESM5 of Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat Open
Additional file 5. The Jupyter Notebook version for plotting and cross-referencing growth traits between experiments.