5 results on '"Ricciardi, Stefano"'
Search Results
2. A method for user-customized compensation of metamorphopsia through video see-through enabled head mounted display.
- Author
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Cimmino, Lucia, Pero, Chiara, Ricciardi, Stefano, and Wan, Shaohua
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HEAD-mounted displays , *STREAMING video & television , *CAMCORDERS , *VISION disorders , *VIDEO processing , *AUGMENTED reality - Abstract
• We propose an approach to compensate the visual defects caused by metamorphopsia • Our approach enables interactive measurement of distortion in user's visual field • We compensate the warped visual field through a real-time processing of video streams • We conducted an experiment on 17 patients affected by metamorphopsia • The results show the proposed system is able to reduce visual field distortion Advances in Augmented Reality technologies and, particularly, the availability of video see-through enabled head mounted displays (HMD), are allowing to devise new strategies to help individuals with visual impairments in daily life. In this work, an approach is proposed to compensate a serious visual impairment, known as metamorphopsia, a vision disorder characterized by deformed images. The goal is to provide patients with a digitally restored visual field, through real-time processing of video see-through streams captured from the HMD. To this regard, we present two contributions, respectively, an interactive discrete modeling of patient's eye-specific vision distortion and a compensation of the latter by means of corresponding real-time counter-distortion of incoming frames. Our approach, indeed, maps each of the video streams acquired by the stereoscopic video see-through cameras aboard the headset on a 2D polygonal mesh which is then counter-warped by moving its vertices based on the previously built distortion model and then displayed, restored, on the HMD's screen. First user evaluations report promising results along with usability issues related to HMD technology. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
3. Gradient boosting regression for faster Partitioned Iterated Function Systems‐based head pose estimation.
- Author
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Barra, Paola, Distasi, Riccardo, Pero, Chiara, Ricciardi, Stefano, and Tucci, Maurizio
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PARTITION functions , *BOOSTING algorithms , *COMPUTER vision , *VIDEO surveillance , *APPLICATION software , *REGRESSION analysis , *BIOMETRY - Abstract
Head pose estimation (HPE) notoriously represents a crucial task for many computer vision applications in robotics, biometry and video surveillance. While, in general, HPE can be performed on both still images and frames extracted from live video or captured footage, its functional approach and the related processing pipeline may have a significant impact on suitability to different application contexts. This implies that, for any real‐time application in which HPE is required, this information, namely the angular value of yaw, pitch and roll axes, should be provided in real‐time as well. Since, so far, the primary aim in HPE research has been on improving estimation accuracy, there are only a few works reporting the computing time of the proposed HPE method and even less explicitly addressing it. The present work stems from a previous Partitioned Iterated Function Systems‐based approach providing state‐of‐the‐art accuracy with high computing cost, and improve it by means of two regression models, namely Gradient Boosting Regressor and Extreme Gradient Boosting Regressor, achieving much faster response and an even lower mean absolute error on the yaw and roll axis, as shown by experiments conducted on the BIWI and AFLW2000 datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Visual question answering: Which investigated applications?
- Author
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Barra, Silvio, Bisogni, Carmen, De Marsico, Maria, and Ricciardi, Stefano
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NATURAL languages , *COMPUTER vision , *CULTURAL education - Abstract
• The paper presents concrete applications of Visual Question Answering • Domains where VQA has been experimented are presented together with the exploited dataset • The paper suggests some challenging techniques that can be especially suited for specific domains • Some final considerations sketch future work in domain-related VQA Visual Question Answering (VQA) is an extremely stimulating and challenging research area where Computer Vision (CV) and Natural Language Processig (NLP) have recently met. In image captioning and video summarization, the semantic information is completely contained in still images or video dynamics, and it has only to be mined and expressed in a human-consistent way. Differently from this, in VQA semantic information in the same media must be compared with the semantics implied by a question expressed in natural language, doubling the artificial intelligence-related effort. Some recent surveys about VQA approaches have focused on methods underlying either the image-related processing or the verbal-related one, or on the way to consistently fuse the conveyed information. Possible applications are only suggested, and, in fact, most cited works rely on general-purpose datasets that are used to assess the building blocks of a VQA system. This paper rather considers the proposals that focus on real-world applications, possibly using as benchmarks suitable data bound to the application domain. The paper also reports about some recent challenges in VQA research. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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5. Postural control assessment via Microsoft Azure Kinect DK: An evaluation study.
- Author
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Antico, Mauro, Balletti, Nicoletta, Laudato, Gennaro, Lazich, Aldo, Notarantonio, Marco, Oliveto, Rocco, Ricciardi, Stefano, Scalabrino, Simone, and Simeone, Jonathan
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KINECT (Motion sensor) , *MICROSOFT Azure (Computing platform) , *STANDARD deviations , *HOME rehabilitation - Abstract
• Azure Kinect is applicable in home rehabilitation contexts. • Azure Kinect shows tracking capabilities comparable to multi-cameras systems. • Azure Kinect is highly accurate in tracking upper body movements. • Azure Kinect is more accurate than its predecessor. Equipments generally used for entertainment, such as Microsoft Kinect, have been widely used for postural control as well. Such systems—compared to professional motion tracking systems—allow to obtain non-invasive and low-cost tracking. This makes them particularly suitable for the implementation of home rehabilitation systems. Microsoft has recently released a new version of Kinect, namely Azure Kinect DK, that is meant for developers, not consumers, and it has been specifically designed to implement professional applications. The hardware of this new version of the Kinect has been substantially improved as compared with previous versions. However, the accuracy of the Azure Kinect DK has not been evaluated yet in the context of the assessment of postural control as done for its predecessors. We present a study to compare the motion traces of the Azure Kinect DK with those of a Vicon 3D system, typically considered the gold standard for high-accuracy motion tracking. The study involved 26 subjects performing specific functional reach and functional balance exercises. The results clearly indicates that the Azure Kinect DK provides a very accurate tracking of the main joints of the body for all the recording taken during the lateral reach movement. The Root Mean Square Error (RMSE) between the two tracking systems obtained is approximately 0.2 for the lateral and forward exercises while for the balance exercise it is around 0.47 considering the average of the results among all the joints. The angular Mean Absolute Error is approximately in the range 5–15 degrees for all the upper joints and independently on the exercise. The lower body joints show a higher angular error between the two systems. Not surprisingly, it was found that results are much better in correspondence of slow movements. The results achieved that the Azure Kinect DK has an incredibly high potential to be used in applications of home rehabilitation, where the assessment of postural control is a fundamental and crucial activity. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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