Qualitative grading of aortic regurgitation: a pilot study comparing CMR 4D flow and echocardiography.

Over the past 10 years there has been intense research in the development of volumetric visualization of intracardiac flow by cardiac magnetic resonance (CMR).This volumetric time resolved technique called CMR 4D flow imaging has several advantages over standard CMR. It offers anatomical, functional and flow information in a single free-breathing, ten-minute acquisition. However, the data obtained is large and its processing requires dedicated software. We evaluated a cloud-based application package that combines volumetric data correction and visualization of CMR 4D flow data, and assessed its accuracy for the detection and grading of aortic valve regurgitation using transthoracic echocardiography as reference. Between June 2014 and January 2015, patients planned for clinical CMR were consecutively approached to undergo the supplementary CMR 4D flow acquisition. Fifty four patients(median age 39 years, 32 males) were included. Detection and grading of the aortic valve regurgitation using CMR4D flow imaging were evaluated against transthoracic echocardiography. The agreement between 4D flow CMR and transthoracic echocardiography for grading of aortic valve regurgitation was good (j = 0.73). To identify relevant,more than mild aortic valve regurgitation, CMR 4D flow imaging had a sensitivity of 100 % and specificity of 98 %. Aortic regurgitation can be well visualized, in a similar manner as transthoracic echocardiography, when using CMR 4D flow imaging

Virtual Reality to Simulate Visual Tasks for Robotic Systems

Virtual reality (VR) can be used as a tool to analyze the interactions between the visual system of a robotic agent and the environment, with the aim of designing the algorithms to solve the visual tasks necessary to properly behave into the 3D world. The novelty of our approach lies in the use of the VR as a tool to simulate the behavior of vision systems. The visual system of a robot (e.g., an autonomous vehicle, an active vision system, or a driving assistance system) and its interplay with the environment can be modeled through the geometrical relationships between the virtual stereo cameras and the virtual 3D world. Differently from conventional applications, where VR is used for the perceptual rendering of the visual information to a human observer, in the proposed approach, a virtual world is rendered to simulate the actual projections on the cameras of a robotic system. In this way, machine vision algorithms can be quantitatively validated by using the ground truth data provided by the knowledge of both the structure of the environment and the vision system

On the Effectiveness of Video Recolouring as an Uplink-model Video Coding Technique

For decades, conventional video compression formats have advanced via incremental improvements with each subsequent standard achieving better rate-distortion (RD) efficiency at the cost of increased encoder complexity compared to its predecessors. Design efforts have been driven by common multi-media use cases such as video-on-demand, teleconferencing, and video streaming, where the most important requirements are low bandwidth and low video playback latency. Meeting these requirements involves the use of computa- tionally expensive block-matching algorithms which produce excellent compression rates and quick decoding times. However, emerging use cases such as Wireless Video Sensor Networks, remote surveillance, and mobile video present new technical challenges in video compression. In these scenarios, the video capture and encoding devices are often power-constrained and have limited computational resources available, while the decoder devices have abundant resources and access to a dedicated power source. To address these use cases, codecs must be power-aware and offer a reasonable trade-off between video quality, bitrate, and encoder complexity. Balancing these constraints requires a complete rethinking of video compression technology. The uplink video-coding model represents a new paradigm to address these low-power use cases, providing the ability to redistribute computational complexity by offloading the motion estimation and compensation steps from encoder to decoder. Distributed Video Coding (DVC) follows this uplink model of video codec design, and maintains high quality video reconstruction through innovative channel coding techniques. The field of DVC is still early in its development, with many open problems waiting to be solved, and no defined video compression or distribution standards. Due to the experimental nature of the field, most DVC codec to date have focused on encoding and decoding the Luma plane only, which produce grayscale reconstructed videos. In this thesis, a technique called “video recolouring” is examined as an alternative to DVC. Video recolour- ing exploits the temporal redundancies between colour planes, reducing video bitrate by removing Chroma information from specific frames and then recolouring them at the decoder. A novel video recolouring algorithm called Motion-Compensated Recolouring (MCR) is proposed, which uses block motion estimation and bi-directional weighted motion-compensation to reconstruct Chroma planes at the decoder. MCR is used to enhance a conventional base-layer codec, and shown to reduce bitrate by up to 16% with only a slight decrease in objective quality. MCR also outperforms other video recolouring algorithms in terms of objective video quality, demonstrating up to 2 dB PSNR improvement in some cases

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer

Three dimensional tracking of exploratory behavior of barnacle cyprids using stereoscopy

Surface exploration is a key step in the colonization of surfaces by sessile marine biofoulers. As many biofouling organisms can delay settlement until a suitable surface is encountered, colonization can comprise surface exploration and intermittent swimming. As such, the process is best followed in three dimensions. Here we present a low-cost transportable stereoscopic system consisting of two consumer camcorders. We apply this novel apparatus to behavioral analysis of barnacle larvae (? 800 lm length) during surface exploration and extract and analyze the three-dimensional patterns of movement. The resolution of the system and the accuracy of position determination are characterized. As a first practical result, three-dimensional swimming trajectories of the cypris larva of the barnacle Semibalanus balanoides are recorded in the vicinity of a glass surface and close to PEG2000-OH and C11NMe3 +Cl- terminated self-assembled monolayers. Although less frequently used in biofouling experiments due to its short reproductive season, the selected model species [Marechal and Hellio (2011), Int Biodeterior Biodegrad, 65(1):92-101] has been used following a number of recent investigations on the settlement behavior on chemically different surfaces [Aldred et al. (2011), ACS Appl Mater Interfaces, 3(6):2085-2091]. Experiments were scheduled to match the availability of cyprids off the north east coast of England so that natural material could be used. In order to demonstrate the biological applicability of the system, analysis of parameters such as swimming direction, swimming velocity and swimming angle are performed.DFG/Ro 2524/2-2DFG/Ro 2497/7-2ONR/N00014-08-1-1116ONR/N00014-12-1-0498EC/FP7/2007-2013/23799

State of the art 3D technologies and MVV end to end system design

L’oggetto del presente lavoro di tesi è costituito dall’analisi e dalla recensione di tutte le tecnologie 3D: esistenti e in via di sviluppo per ambienti domestici; tenendo come punto di riferimento le tecnologie multiview video (MVV). Tutte le sezioni della catena dalla fase di cattura a quella di riproduzione sono analizzate. Lo scopo è di progettare una possibile architettura satellitare per un futuro sistema MVV televisivo, nell’ambito di due possibili scenari, broadcast o interattivo. L’analisi coprirà considerazioni tecniche, ma anche limitazioni commerciali

Coherent multi-dimensional segmentation of multiview images using a variational framework and applications to image based rendering

Image Based Rendering (IBR) and in particular light field rendering has attracted a lot of attention for interpolating new viewpoints from a set of multiview images. New images of a scene are interpolated directly from nearby available ones, thus enabling a photorealistic rendering. Sampling theory for light fields has shown that exact geometric information in the scene is often unnecessary for rendering new views. Indeed, the band of the function is approximately limited and new views can be rendered using classical interpolation methods. However, IBR using undersampled light fields suffers from aliasing effects and is difficult particularly when the scene has large depth variations and occlusions. In order to deal with these cases, we study two approaches: New sampling schemes have recently emerged that are able to perfectly reconstruct certain classes of parametric signals that are not bandlimited but characterized by a finite number of parameters. In this context, we derive novel sampling schemes for piecewise sinusoidal and polynomial signals. In particular, we show that a piecewise sinusoidal signal with arbitrarily high frequencies can be exactly recovered given certain conditions. These results are applied to parametric multiview data that are not bandlimited. We also focus on the problem of extracting regions (or layers) in multiview images that can be individually rendered free of aliasing. The problem is posed in a multidimensional variational framework using region competition. In extension to previous methods, layers are considered as multi-dimensional hypervolumes. Therefore the segmentation is done jointly over all the images and coherence is imposed throughout the data. However, instead of propagating active hypersurfaces, we derive a semi-parametric methodology that takes into account the constraints imposed by the camera setup and the occlusion ordering. The resulting framework is a global multi-dimensional region competition that is consistent in all the images and efficiently handles occlusions. We show the validity of the approach with captured light fields. Other special effects such as augmented reality and disocclusion of hidden objects are also demonstrated