Summary Every year on June 8th, the globe observes World Brain Tumor Day to raise awareness and educate people about brain cancer, encompassing both noncancerous (benign) and cancerous (malignant) growths. Research in the field of brain cancer plays a vital role in supporting medical professionals. In this context, augmented reality (AR) technology has emerged as a valuable tool, enabling surgeons to visualize underlying structures and offering a cost‐effective and time‐efficient alternative. Our study focuses on the efficient segmentation of brain tumor classes using Magnetic Resonance Imaging (MRI) and incorporates a three‐stage approach: preprocessing, segmentation, and 3D reconstruction & AR display. In the preprocessing stage, a Gaussian filter is applied to mitigate intensity heterogeneity. Segmentation and detection are achieved using active geometric contour models, complemented by morphological operations. To establish 3D brain tumor reconstruction, a genuine scene is virtually integrated using 3D Slicer software. The proposed methodology was validated using a genuine patient dataset comprising 496 MRI scans obtained from the local Bab El Oued university hospital center. The results demonstrate the effectiveness of our approach in achieving accurate 3D brain tumor reconstruction, efficient tumor extraction, and augmented reality visualization. The obtained segmentation results showcased an impressive accuracy of 98.61%, outperforming existing state‐of‐the‐art methods and affirming the efficacy of our proposed strategy.

Abstract In the last decade, there has been an extraordinary acceleration in immersive technologies, including virtual reality (VR) and augmented reality (AR). The VR interfaces have widely explored various 3D interaction‐based approaches. 3D interaction is one of the main features of any VR system. However, the problems while selecting and manipulating distant and occluded 3D virtual objects in VR are still unresolved and suffer from a lack of precision and accuracy. To interact with VR/AR systems, various devices such as VR headsets or gloves are used. These devices are not very reliable yet; in fact they are inconvenient and invasive to a person's comfort. Currently, the natural user interfaces (NUIs) are increasingly introduced in human computer interaction (HCI) systems as gestures recognition, which prove to be very useful in the improvement of the user engagement and presence sensing. More particularly, they provide more user‐friendly and nonintrusive 3D interaction methods and techniques. In this article, the Zoom‐fwd, which is an efficient 3D interaction technique is presented. The proposed technique uses gesture recognition for different 3D interaction tasks like selection and manipulation. This new approach allows an efficient interaction with distant and occluded objects, while providing a precise selection, even when the environment is crowded. The Zoom‐fwd technique is a software solution for a number of hardware and software problems. A user study is conducted to determine whether the proposed technique is more suitable when performing interaction tasks. The results show that the Zoom‐fwd technique provides effective interaction with distant and occluded objects, by improving the user task completion performance. In addition, this indicates that selection precision has been enhanced significantly with the Zoom‐fwd technique.

We propose a lightweight smart haptic bracelet-based stimulation system for VR applications. This wireless system equipped with vibrotactile tactors and peltier actuator, generates different haptic/thermal levels for different materials being touched in a VR environment.

For the last decade, a rising need for emotion recognition has been noticed in several domains, such as virtual reality, human-computer interaction, video games and health monitoring, etc. Effectively, emotion recognition via facial expressions attracts increasing attention. Based on geometrical facial features, this paper proposes a new facial emotion recognition method. We collected a novel dataset of 17 subjects facial performance of six emotional states (anger, fear, happiness, surprise, sadness, and neutral) using Kinect (vi) and Kinect (v2) and RGB HD camera. New positional features including a combination of angle and distance features are used to train the classifier. The K nearest neighbors (k-NN) is used as the main classification technique. To assess our proposed method performance, we use the leave-one-out subject cross-validation. A comparison between RGB and RGB-D data is provided. The obtained results show the superior performance of the RGB-D features provided by Kinect (v2). We observed in our experiment that the 2D images are not robust enough for facial emotion recognition due to the sensitivity of the RGB camera to the surrounding conditions.

In 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.

In this paper, we propose an efficient, and fast binary descriptor, called MOBIL (MOments based BInary differences for Local description), which compares not just the intensity, but also sub-regions geometric proprieties by employing moments. This approach offers high distinctiveness against affine transformations and appearance changes. The experimental evaluation shows that MOBIL achieves a quite good performance in term of low computation complexity and high recognition rate compared to state-of-the-art real-time local descriptors.

In this paper a set of concepts to design mixed reality systems is presented. The principle of interaction space is proposed and then integrated in a development process, especially, to take into account the mixed reality specifications in the information system design. Models are elaborated to highlight relationships between business and interaction spaces. The development of the proposed method is presented on a case study.

Considers the problem of image restoration degraded by a shift-invariant blur function and corrupted by white Gaussian noise. We propose a modified Hopfield neural network-based image restoration. Two algorithms with two updating modes using the modified Hopfield neural network are presented: (1) sequential updates, and (2) n-simultaneous updates. In the sequential algorithm, only one element of the state is updated at time (t+1), while the rest are left unchanged. In the n-simultaneous algorithm, all elements of the state are updated simultaneously. Lastly, we present some image restoration results which attest to the efficiency of our method.