889 resultados para computer vision, facial expression recognition, swig, red5, actionscript, ruby on rails, html5


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This study aimed to: i) determine if the attention bias towards angry faces reported in eating disorders generalises to a non-clinical sample varying in eating disorder-related symptoms; ii) examine if the bias occurs during initial orientation or later strategic processing; and iii) confirm previous findings of impaired facial emotion recognition in non-clinical disordered eating. Fifty-two females viewed a series of face-pairs (happy or angry paired with neutral) whilst their attentional deployment was continuously monitored using an eye-tracker. They subsequently identified the emotion portrayed in a separate series of faces. The highest (n=18) and lowest scorers (n=17) on the Eating Disorders Inventory (EDI) were compared on the attention and facial emotion recognition tasks. Those with relatively high scores exhibited impaired facial emotion recognition, confirming previous findings in similar non-clinical samples. They also displayed biased attention away from emotional faces during later strategic processing, which is consistent with previously observed impairments in clinical samples. These differences were related to drive-for-thinness. Although we found no evidence of a bias towards angry faces, it is plausible that the observed impairments in emotion recognition and avoidance of emotional faces could disrupt social functioning and act as a risk factor for the development of eating disorders.

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BACKGROUND: Since 1972, the Australian College of Optometry has worked in partnership with Vision Australia to provide multidisciplinary low-vision care at the Kooyong Low Vision Clinic. In 1999, Wolffsohn and Cochrane reported on the demographic characteristics of patients attending Kooyong. Sixteen years on, the aim of this study is to review the demographics of the Kooyong patient cohort and prescribing patterns. METHODS: Records of all new patients (n = 155) attending the Kooyong Low Vision Clinic for optometry services between April and September 2012 were retrospectively reviewed. RESULTS: Median age was 84.3 years (range 7.7 to 98.1 years) with 59 per cent female. The majority of patients presented with late-onset degenerative pathology, 49 per cent with a primary diagnosis of age-related macular degeneration. Many (47.1 per cent) lived with their families. Mean distance visual acuity was 0.57 ± 0.47 logMAR or approximately 6/24. The median spectacle-corrected near visual acuity was N8 (range N3 to worse than N80). Fifty patients (32.3 per cent) were prescribed new spectacles, 51 (32.9 per cent) low vision aids and five (8.3 per cent) were prescribed electronic magnification devices. Almost two-thirds (63.9 per cent) were referred for occupational therapy management and 12.3 per cent for orientation and mobility services. CONCLUSIONS: The profile of patients presenting for low-vision services at Kooyong is broadly similar to that identified in 1999. Outcomes appear to be similar, aside from an expected increase in electronic devices and technological solutions; however, the nature of services is changing, as treatments for ocular diseases advance and assistive technology develops and becomes more accessible. Alongside the aging population and age-related ocular disease being the predominant cause of low vision in Australia, the health-funding landscape is becoming more restrictive. The challenge for the future will be to provide timely, high-quality care in an economically efficient model.

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This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineates the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.

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Registration of point clouds captured by depth sensors is an important task in 3D reconstruction applications based on computer vision. In many applications with strict performance requirements, the registration should be executed not only with precision, but also in the same frequency as data is acquired by the sensor. This thesis proposes theuse of the pyramidal sparse optical flow algorithm to incrementally register point clouds captured by RGB-D sensors (e.g. Microsoft Kinect) in real time. The accumulated errorinherent to the process is posteriorly minimized by utilizing a marker and pose graph optimization. Experimental results gathered by processing several RGB-D datasets validatethe system proposed by this thesis in visual odometry and simultaneous localization and mapping (SLAM) applications.

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This work presents an analysis of the behavior of some algorithms usually available in stereo correspondence literature, with full HD images (1920x1080 pixels) to establish, within the precision dilemma versus runtime applications which these methods can be better used. The images are obtained by a system composed of a stereo camera coupled to a computer via a capture board. The OpenCV library is used for computer vision operations and processing images involved. The algorithms discussed are an overall method of search for matching blocks with the Sum of the Absolute Value of the difference (Sum of Absolute Differences - SAD), a global technique based on cutting energy graph cuts, and a so-called matching technique semi -global. The criteria for analysis are processing time, the consumption of heap memory and the mean absolute error of disparity maps generated.

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In this thesis, we introduce DeReEs-4v, an algorithm for unsupervised and automatic registration of two video frames captured depth-sensing cameras. DeReEs-4V receives two RGBD video streams from two depth-sensing cameras arbitrary located in an indoor space that share a minimum amount of 25% overlap between their captured scenes. The motivation of this research is to employ multiple depth-sensing cameras to enlarge the field of view and acquire a more complete and accurate 3D information of the environment. A typical way to combine multiple views from different cameras is through manual calibration. However, this process is time-consuming and may require some technical knowledge. Moreover, calibration has to be repeated when the location or position of the cameras change. In this research, we demonstrate how DeReEs-4V registration can be used to find the transformation of the view of one camera with respect to the other at interactive rates. Our algorithm automatically finds the 3D transformation to match the views from two cameras, requires no human interference, and is robust to camera movements while capturing. To validate this approach, a thorough examination of the system performance under different scenarios is presented. The system presented here supports any application that might benefit from the wider field-of-view provided by the combined scene from both cameras, including applications in 3D telepresence, gaming, people tracking, videoconferencing and computer vision.

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Lo scopo della tesi è creare un’architettura in FPGA in grado di ricavare informazioni 3D da una coppia di sensori stereo. La pipeline è stata realizzata utilizzando il System-on-Chip Zynq, che permette una stretta interazione tra la parte hardware realizzata in FPGA e la CPU. Dopo uno studio preliminare degli strumenti hardware e software, è stata realizzata l’architettura base per la scrittura e la lettura di immagini nella memoria DDR dello Zynq. In seguito l’attenzione si è spostata sull’implementazione di algoritmi stereo (rettificazione e stereo matching) su FPGA e nella realizzazione di una pipeline in grado di ricavare accurate mappe di disparità in tempo reale acquisendo le immagini da una camera stereo.

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Questa tesi si occupa dell’estensione di un framework software finalizzato all'individuazione e al tracciamento di persone in una scena ripresa da telecamera stereoscopica. In primo luogo è rimossa la necessità di una calibrazione manuale offline del sistema sfruttando algoritmi che consentono di individuare, a partire da un fotogramma acquisito dalla camera, il piano su cui i soggetti tracciati si muovono. Inoltre, è introdotto un modulo software basato su deep learning con lo scopo di migliorare la precisione del tracciamento. Questo componente, che è in grado di individuare le teste presenti in un fotogramma, consente ridurre i dati analizzati al solo intorno della posizione effettiva di una persona, escludendo oggetti che l’algoritmo di tracciamento sarebbe portato a individuare come persone.

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Acquiring 3D shape from images is a classic problem in Computer Vision occupying researchers for at least 20 years. Only recently however have these ideas matured enough to provide highly accurate results. We present a complete algorithm to reconstruct 3D objects from images using the stereo correspondence cue. The technique can be described as a pipeline of four basic building blocks: camera calibration, image segmentation, photo-consistency estimation from images, and surface extraction from photo-consistency. In this Chapter we will put more emphasis on the latter two: namely how to extract geometric information from a set of photographs without explicit camera visibility, and how to combine different geometry estimates in an optimal way. © 2010 Springer-Verlag Berlin Heidelberg.

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Photometric Stereo is a powerful image based 3D reconstruction technique that has recently been used to obtain very high quality reconstructions. However, in its classic form, Photometric Stereo suffers from two main limitations: Firstly, one needs to obtain images of the 3D scene under multiple different illuminations. As a result the 3D scene needs to remain static during illumination changes, which prohibits the reconstruction of deforming objects. Secondly, the images obtained must be from a single viewpoint. This leads to depth-map based 2.5 reconstructions, instead of full 3D surfaces. The aim of this Chapter is to show how these limitations can be alleviated, leading to the derivation of two practical 3D acquisition systems: The first one, based on the powerful Coloured Light Photometric Stereo method can be used to reconstruct moving objects such as cloth or human faces. The second, permits the complete 3D reconstruction of challenging objects such as porcelain vases. In addition to algorithmic details, the Chapter pays attention to practical issues such as setup calibration, detection and correction of self and cast shadows. We provide several evaluation experiments as well as reconstruction results. © 2010 Springer-Verlag Berlin Heidelberg.

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Aims 1 To identify the 3D soft tissue volumetric and linear landmark changes following treatment with the Twin-Block Appliance TBA. 2 To estimate the TBA treatment outcome on the soft tissue facial profile volumetric and linear landmark changes from the Postured Wax Bite (PWB). 3 To identify if there is any association between certain soft tissue landmark variables and successful treatment outcome of the TBA as measured by the reduction in overjet. 4 To assess the effects of TBA treatment on facial expressions. Materials and Methods Forty-seven Caucasian subjects with Class II division 1 were recruited. 3D images captured of each subject, pre-treatment (T1), with the PWB (T2) and at the end of treatment (T3). Soft tissue volumetric and linear changes as well as the correlation between facial parameters and successful treatment were calculated. Results The mean soft tissue volumetric change from T1 to T3 was 22.24 ± 16.73 cm³. Soft tissue profile linear changes from T1-T3 for lower facial landmarks were 4-5 mm. From T1-T3, the mean soft tissue volumetric change of the total sample was 60% of the change produced by the PWB (T1 to T2). Correlations were weak for all 3D facial parameters and successful overjet reduction. Facial expression changes were only significant for the lower landmarks. Conclusions 1 TBA treatment, in growing subjects, increased the lower facial soft tissue volume and caused forward movement of the lower soft tissue facial profile landmarks.2 The PWB can be used to estimate the treatment outcome of the TBA on soft tissue profile changes.3 No association was found between soft tissue landmark variables and successful overjet reduction.4 TBA treatment had no effect on the upper facial landmarks for each facial expression but it changed the lower facial expressions significantly except for maximal smile in males.

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Le traitement des émotions joue un rôle essentiel dans les relations interpersonnelles. Des déficits dans la reconnaissance des émotions évoquées par les expressions faciales et vocales ont été démontrés à la suite d’un traumatisme craniocérébral (TCC). Toutefois, la majorité des études n’ont pas différencié les participants selon le niveau de gravité du TCC et n’ont pas évalué certains préalables essentiels au traitement émotionnel, tels que la capacité à percevoir les caractéristiques faciales et vocales, et par le fait même, la capacité à y porter attention. Aucune étude ne s’est intéressée au traitement des émotions évoquées par les expressions musicales, alors que la musique est utilisée comme méthode d’intervention afin de répondre à des besoins de prise en charge comportementale, cognitive ou affective chez des personnes présentant des atteintes neurologiques. Ainsi, on ignore si les effets positifs de l’intervention musicale sont basés sur la préservation de la reconnaissance de certaines catégories d’émotions évoquées par les expressions musicales à la suite d’un TCC. La première étude de cette thèse a évalué la reconnaissance des émotions de base (joie, tristesse, peur) évoquées par les expressions faciales, vocales et musicales chez quarante et un adultes (10 TCC modéré-sévère, 9 TCC léger complexe, 11 TCC léger simple et 11 témoins), à partir de tâches expérimentales et de tâches perceptuelles contrôles. Les résultats suggèrent un déficit de la reconnaissance de la peur évoquée par les expressions faciales à la suite d’un TCC modéré-sévère et d’un TCC léger complexe, comparativement aux personnes avec un TCC léger simple et sans TCC. Le déficit n’est pas expliqué par un trouble perceptuel sous-jacent. Les résultats montrent de plus une préservation de la reconnaissance des émotions évoquées par les expressions vocales et musicales à la suite d’un TCC, indépendamment du niveau de gravité. Enfin, malgré une dissociation observée entre les performances aux tâches de reconnaissance des émotions évoquées par les modalités visuelle et auditive, aucune corrélation n’a été trouvée entre les expressions vocales et musicales. La deuxième étude a mesuré les ondes cérébrales précoces (N1, N170) et plus tardives (N2) de vingt-cinq adultes (10 TCC léger simple, 1 TCC léger complexe, 3 TCC modéré-sévère et 11 témoins), pendant la présentation d’expressions faciales évoquant la peur, la neutralité et la joie. Les résultats suggèrent des altérations dans le traitement attentionnel précoce à la suite d’un TCC, qui amenuisent le traitement ultérieur de la peur évoquée par les expressions faciales. En somme, les conclusions de cette thèse affinent notre compréhension du traitement des émotions évoquées par les expressions faciales, vocales et musicales à la suite d’un TCC selon le niveau de gravité. Les résultats permettent également de mieux saisir les origines des déficits du traitement des émotions évoquées par les expressions faciales à la suite d’un TCC, lesquels semblent secondaires à des altérations attentionnelles précoces. Cette thèse pourrait contribuer au développement éventuel d’interventions axées sur les émotions à la suite d’un TCC.

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This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

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Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

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Advertising investment and audience figures indicate that television continues to lead as a mass advertising medium. However, its effectiveness is questioned due to problems such as zapping, saturation and audience fragmentation. This has favoured the development of non-conventional advertising formats. This study provides empirical evidence for the theoretical development. This investigation analyzes the recall generated by four non-conventional advertising formats in a real environment: short programme (branded content), television sponsorship, internal and external telepromotion versus the more conventional spot. The methodology employed has integrated secondary data with primary data from computer assisted telephone interviewing (CATI) were performed ad-hoc on a sample of 2000 individuals, aged 16 to 65, representative of the total television audience. Our findings show that non-conventional advertising formats are more effective at a cognitive level, as they generate higher levels of both unaided and aided recall, in all analyzed formats when compared to the spot.