Low-cost VR Collaborative System equipped with Haptic Feedback

International audienceIn this paper, we present a low-cost virtual reality (VR) collaborative system equipped with a haptic feedback sensation system. This system is composed of a Kinect sensor for bodies and gestures detection, a microcontroller and vibrators to simulate outside interactions, and smartphone powered cardboard, all of this are put into a network implemented with Unity 3D game engine. CCS CONCEPTS • Interaction paradigms → Virtual reality; Collaborative interaction; • Hardware → Sensors and actuators; Wireless devices; KEYWORDS collaborative virtual reality, haptic feedback system

An Interactive VR System for Anatomy Training

In recent decades, virtual reality (VR) becomes a potential solution to enhance clinical medical (functional reeducation, training, etc.), especially with the growth evolution of technologies form both visualization (e.g., HoloLens, VR in Case, etc.) and 3D gestural interaction (Ray Casting, Free Hand, etc.) point of views. The 3D visualization of the human anatomy could be a serious asset for students in medicine. This new technology could provide a clear and realistic representation of the internal organs of the human body, without having to resort to surgery. 3D organs based-course supports visualization could be a useful tool for students, especially in their first graduate studies, to enhance their perception on human’s internal composition. This system is composed of two modules, 3D human’s anatomy visualization module and interaction module for organs manipulation. Finally, the system will be tested and evaluated with several subjects

MVC-3D: Adaptive Design Pattern for Virtual and Augmented Reality Systems

International audienceIn this paper, we present MVC-3D design pattern to develop virtual and augmented (or mixed) reality interfaces that use new types of sensors, modalities and implement specific algorithms and simulation models. The proposed pattern represents the extension of classic MVC pattern by enriching the View component (interactive View) and adding a specific component (Library). The results obtained on the development of augmented reality interfaces showed that the complexity of M, iV and C components is reduced. The complexity increases only on the Library component (L). This helps the programmers to well structure their models even if the interface complexity increases. The proposed design pattern is also used in a design process called MVC-3D in the loop that enables a seamless evolution from initial prototype to the final system

Augmented reality for underwater activities with the use of the DOLPHYN

International audienceThe objective of this work is to introduce Augmented and Mixed Reality technologies in aquatic leisure activities. We have proposed a new device which is autonomous, mobile and easily transportable by one person. It can also be easily installed, equipped with GPS and wireless systems, and has positive buoyancy. The device will be used at water surface as well as underwater using a tuba. Moreover, the device is equipped with one (can be upgraded for more) video camera pointing downwards. Augmented Reality contents combining actual underwater images with 3D animated images will be one of the preferred ways to use the device

COVI3D: Automatic COVID-19 CT Image-Based Classification and Visualization Platform Utilizing Virtual and Augmented Reality Technologies

Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information

MOBIL: A Moments based Local Binary Descriptor

International audienceIn this paper, we propose an efficient, and fast binary descriptor,called MOBIL (MOments based BInary differences for Localdescription), which compares not just the intensity, but also subregionsgeometric proprieties by employing moments. Thisapproach offers high distinctiveness against affine transformationsand appearance changes. The experimental evaluation shows thatMOBIL achieves a quite good performance in term of lowcomputation complexity and high recognition rate compared tostate-of-the-art real-time local descriptors

Distinctions between Choroidal Neovascularization and Age Macular Degeneration in Ocular Disease Predictions via Multi-Size Kernels ξcho-Weighted Median Patterns

Age-related macular degeneration is a visual disorder caused by abnormalities in a part of the eye’s retina and is a leading source of blindness. The correct detection, precise location, classification, and diagnosis of choroidal neovascularization (CNV) may be challenging if the lesion is small or if Optical Coherence Tomography (OCT) images are degraded by projection and motion. This paper aims to develop an automated quantification and classification system for CNV in neovascular age-related macular degeneration using OCT angiography images. OCT angiography is a non-invasive imaging tool that visualizes retinal and choroidal physiological and pathological vascularization. The presented system is based on new retinal layers in the OCT image-specific macular diseases feature extractor, including Multi-Size Kernels ξcho-Weighted Median Patterns (MSKξMP). Computer simulations show that the proposed method: (i) outperforms current state-of-the-art methods, including deep learning techniques; and (ii) achieves an overall accuracy of 99% using ten-fold cross-validation on the Duke University dataset and over 96% on the noisy Noor Eye Hospital dataset. In addition, MSKξMP performs well in binary eye disease classifications and is more accurate than recent works in image texture descriptors