Adeline T M Paiement | Swansea University (original) (raw)

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inproceedings by Adeline T M Paiement

PerCom 2017 (Work in progress session), Mar 2017

Nutrition related health conditions can seriously decrease quality of life; a system able to moni... more Nutrition related health conditions can seriously decrease quality of life; a system able to monitor the kitchen activities and eating behaviour of patients could provide clinicians with important indicators for improving a patient's condition. To achieve this, the system has to reason about the person's actions and goals. To address this challenge, we present a behaviour recognition approach that relies on symbolic behaviour representation and probabilistic reasoning to recognise the person's actions, the type of meal being prepared and its potential impact on a patient's health. We test our approach on a cooking dataset containing unscripted kitchen activities recorded with various sensors in a real kitchen. The results show that the approach is able to recognise the sequence of executed actions and the prepared meal, to determine whether it is healthy, and to reason about the possibility of depression based on the type of meal.

British Machine Vision Conference (BMVC), Sep 2014

2015 IEEE International Conference on Communication Workshop (ICCW), 2015

Proceedings of the International Astronomical Union, 2007

ABSTRACT

Medical Image Understanding and Analysis (MIUA), 2010

articles by Adeline T M Paiement

IEEE Transactions on Image Processing, 2014

We address the two inherently related problems of segmentation and interpolation of 3D and 4D spa... more We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework. The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjectively on artificial data and MRI and CT scans and is compared against the traditional sequential approach, which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.

Monthly Notices of the Royal Astronomical Society, 2007

ABSTRACT

phdtheses by Adeline T M Paiement

PerCom 2017 (Work in progress session), Mar 2017

British Machine Vision Conference (BMVC), Sep 2014

2015 IEEE International Conference on Communication Workshop (ICCW), 2015

Proceedings of the International Astronomical Union, 2007

ABSTRACT

Medical Image Understanding and Analysis (MIUA), 2010

IEEE Transactions on Image Processing, 2014

Monthly Notices of the Royal Astronomical Society, 2007

ABSTRACT

2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016), 2016

Labelling user data is a central part of the design and evaluation of pervasive systems that aim ... more Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and trainin ...

2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016), 2016

Computer Vision – ACCV 2016 Workshops, 2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017

2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016), 2016

Designing, Developing, and Facilitating Smart Cities, 2016

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015

ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2015

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, Jan 30, 2016

We address the problem of object modeling from 3D and 3D+T data made up of images which contain d... more We address the problem of object modeling from 3D and 3D+T data made up of images which contain different parts of an object of interest, are separated by large spaces, and are misaligned with respect to each other. These images have only a limited number of intersections, hence making their registration particularly challenging. Furthermore, such data may result from various medical imaging modalities and can therefore present very diverse spatial configurations. Previous methods perform registration and object modeling (segmentation and interpolation) sequentially. However, sequential registration is ill-suited for the case of images with few intersections. We propose a new methodology which, regardless of the spatial configuration of the data, performs the three stages of registration, segmentation, and shape interpolation from spaced and misaligned images simultaneously. We integrate these three processes in a level set framework, in order to benefit from their synergistic inter...

Computer Vision and Image Understanding, 2016

2015 17th International Conference on E-health Networking, Application & Services (HealthCom), 2015

2015 IEEE International Conference on Communication Workshop (ICCW), 2015

Procedings of the British Machine Vision Conference 2015, 2015

Modeling the heart motion has important applications for diagnosis and intervention. We present a... more Modeling the heart motion has important applications for diagnosis and intervention. We present a new method for modeling the deformation of the myocardium in the cardiac cycle. Our approach is based on manifold learning to build a representation of shape variation across time. We experiment with various manifold types to identify the best manifold method, and with real patient data extracted from cine MRIs. We obtain a representation, common to all subjects, that can discriminate cardiac cycle phases and heart function types.

ArXiv, 2020

Robustness to transformation is desirable in many computer vision tasks, given that input data of... more Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance within classes. While translation invariance and equivariance is a documented phenomenon of CNNs, sensitivity to other transformations is typically encouraged through data augmentation. We investigate the modulation of complex valued convolutional weights with learned Gabor filters to enable orientation robustness. With Gabor modulation, the designed network is able to generate orientation dependent features free of interpolation with a single set of rotation-governing parameters. Moreover, by learning rotation parameters alongside traditional convolutional weights, the representation space is not constrained and may adapt to the exact input transformation. We present Learnable Convolutional Gabor Networks (LCGNs), that are parameter-efficient and offer increased model complexity while keeping backpropagation simple. We demonstrate that learned Gabor modulation...

Type II solar radio bursts have proven to be a useful tool for gaining insights into the behaviou... more Type II solar radio bursts have proven to be a useful tool for gaining insights into the behaviour of complex solar events and for forecasting and mitigating their damages on Earth. In this work, we detect and segment the occurrence of type II bursts in solar radio spectrograms, thereby facilitating the extraction of parameters needed to gain insight into solar events. We utilise prior knowledge of how type II bursts drift through frequencies over time to assist with these tasks of detection and segmentation. A new adaptive Region of Interest (ROI) is proposed, to constrain the search to regions that follow the burst curvature at a given frequency. It comes with an implicit data normalisation that reduces the variance of burst appearance in the data, hence simplifying the learning process from small datasets. We demonstrate the effectiveness of our methodology using a simple and popular HOG and logistic regression detector and basic segmentation based on voting and background subtra...

This dataset contains sequences of depth images of people walking up stairs, as well as the assoc... more This dataset contains sequences of depth images of people walking up stairs, as well as the associated skeletons obtained from the OpenNI SDK. These data have been acquired in the frame of the SPHERE IRC for the experiments on movement quality assessment in: A. Paiement, L. Tao, M. Camplani, S. Hannuna, D. Damen, M. Mirmehdi, Online quality assessment of human movement from skeleton data, in Proceedings of BMVC 2014.

We propose a CNN regression method to generate high-level, view-invariant features from RGB image... more We propose a CNN regression method to generate high-level, view-invariant features from RGB images which are suitable for human pose estimation and movement quality analysis. The inputs to our network are body joint heatmaps and limb-maps to help our network exploit geometric relationships between different body parts to estimate the features more accurately. A new multiview and multimodal human movement dataset is also introduced part of which is used to evaluate the results of the proposed method. We present comparative experimental results on pose estimation using a manifold-based pose representation built from motion-captured data. We show that the new RGB derived features provide pose estimates of similar or better accuracy than those produced from depth data, even from single views only.

Sensors

We propose a view-invariant method towards the assessment of the quality of human movements which... more We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and...