Does the Size-Arrival Effect Occur With an Active Collision-Avoidance Task in an Immersive 3D Virtual Reality Environment?

A Real-World Size Organization of Object Responses in Occipitotemporal Cortex

This study proposes gaze-based hand interaction, which is helpful for improving the user’s immersion in the production process of virtual reality content for the mobile platform, and analyzes efficiency through an experiment using a questionnaire. First, three-dimensional interactive content is produced for use in the proposed interaction experiment while presenting an experiential environment that gives users a high sense of immersion in the mobile virtual reality environment. This is designed to induce the tension and concentration of users in line with the immersive virtual reality environment. Additionally, a hand interaction method based on gaze—which is mainly used for the entry of mobile virtual reality content—is proposed as a design method for immersive mobile virtual reality environment. The user satisfaction level of the immersive environment provided by the proposed gaze-based hand interaction is analyzed through experiments in comparison with the general method that uses gaze only. Furthermore, detailed analysis is conducted by dividing the effects of the proposed interaction method on user’s psychology into positive factors such as immersion and interest and negative factors such as virtual reality (VR) sickness and dizziness. In this process, a new direction is proposed for improving the immersion of users in the production of mobile platform virtual reality content.

The large-scale variation issue in high-resolution aerial images significantly lowers the accuracy of segmenting small objects. For a deep-learning-based semantic segmentation model, the main reason is that the deeper layers generate high-level semantics over considerably large receptive fields, thus improving the accuracy for large objects but ignoring small objects. Although the low-level features extracted by shallow layers contain small-object information, large-object information has predominant effects. When the model, using low-level features, is trained, the large objects push the small objects aside. This observation motivates us to propose a novel reverse difference mechanism (RDM). The RDM eliminates the predominant effects of large objects and highlights small objects from low-level features. Based on the RDM, a novel semantic segmentation method called the reverse difference network (RDNet) is designed. In the RDNet, a detailed stream is proposed to produce small-object semantics by enhancing the output of RDM. A contextual stream for generating high-level semantics is designed by fully accumulating contextual information to ensure the accuracy of the segmentation of large objects. Both high-level and small-object semantics are concatenated when the RDNet performs predictions. Thus, both small- and large-object information is depicted well. Two semantic segmentation benchmarks containing vital small objects are used to fully evaluate the performance of the RDNet. Compared with existing methods that exhibit good performance in segmenting small objects, the RDNet has lower computational complexity and achieves 3.9–18.9% higher accuracy in segmenting small objects.

This paper presents an immersive and highly responsive virtual 3D environment. By combining state of the art 3D display device zSpace and a high speed 499 fps hand gesture interface system a truly immersive system is achieved. Truly immersive virtual 3D environment can only be achieved with low latency interaction. Utilizing high speed camera, the system is capable of detecting two hands simultaneously. The system offers temporal congruency, spatial congruency, and intuitive interaction. The system offers temporal congruency by having a end-to-end latency of only 27.7 ms. It also offers spatial congruency with an accuracy of less than 3mm. It also provides intuitive interaction by allowing the user to use simple hand gestures to interact with the system. Small and fast movements can be detected easily in this system. Therefore, this system is suitable for settings where reflex speed response is required from the user. The possibility to operate using bare hand also offers a convenience in setting where hand hygiene is important such as in medical surgery.

User capabilities in eyes-free spatial target acquisition in immersive virtual reality environments

The aim of the current study was to investigate fine motor control ability and manual dexterity women with fibromyalgia syndrome (FMS) without symptoms in the upper extremity compared to healthy women. Subtests of the Purdue Pegboard Test (one-hand, bilateral and assembly) and of the Jebsen-Taylor hand-function test (writing, turning cards, picking up small, light and large heavy objects, simulated feeding and stacking checkers) were evaluated bilaterally in 20 women with FMS (aged 35-55years) without symptoms in the upper limb and 20 age- and hand dominance-matched healthy women. Differences between sides and groups were analysed with several analysis of variance (ANOVA). The ANOVA revealed significant differences between groups (P<0.001) and sides (P=0.007) for one-hand pin placement subtest: women with FMS showed bilateral worse scores than controls. Patients also exhibited significantly lower scores in bilateral pin placement and assembly subtests when compared to healthy controls (P<0.001). The ANOVA also revealed significant differences between groups for writing, turning over cards, picking up small objects, stacking checkers, picking up large light objects and picking up large heavy objects (all, P<0.001): women with FMS needed more time for these subtests than healthy women with both hands. No difference for simulated feeding was found between groups. Our findings revealed bilateral deficits in fine motor control ability and manual dexterity in patients with FMS without symptoms in the upper extremity. These deficits are not related to the clinical features of the symptoms supporting an underlying central mechanism of altered motor control.

Virtual environments are increasingly being used for upper limb rehabilitation in post-stroke patients. However, still there is no clear evidence that the movements performed in virtual reality are comparable to those performed in the physical world from the kinematic point-of-view. The goal of the proposed study is thus to determine if aimed reaching movements made in a 3D ecological and immersive virtual environment – displayed through a Head Mounted Display (HMD) – are comparable to movements performed in the real world. The study foresees the realization of two comparable environmental settings representing the shelf of a supermarket. Three different groups of subjects (healthy young adults, healthy elderly, and post-stroke subjects, n=15 each) are asked to reach 5 times toward 9 targets in 3 different conditions: virtual reality, physical reality, and physical reality while holding a controller. Their movements are tracked with a stereo-photogrammetric motion capture system; movement times, peak velocities, and joint angles are then extracted for analysis. This protocol will allow comparing reaching movements, and also excluding of the effects related to holding a controller. A preliminary trial reveled the feasibility of the protocol, thus the experiment will be carried out in the next months. If results will be encouraging, VR should be considered in rehabilitative treatments as a useful means to elicit patients’ motivation, but also appropriate movement synergies, thus promoting a better recovery of upper limb functions.

While web proxy caching is a widely deployed technique, the performance of a proxy cache is limited by the local storage. Some studies have addressed this limitation by using the residual resources of clients via a p2p method and have achieved a very high hit rate. However, these approaches treat web objects as homogeneous objects and there is no consideration of various web characteristics. Consequently, the byte hit rate of the system is limited, external bandwidth is wasted, and perceived user latency is increased. The present paper suggests an efficient p2p based web caching technique that manages objects with different policies so as to exploit the characteristics of web objects, such as size and temporal locality. Small objects are stored alone whereas large objects are stored by dividing them into numerous small blocks, which are distributed in clients. On a proxy cache, header blocks of large objects take the place of objects themselves and smaller objects are cached. This technique increases the hit rate. Unlike a web cache, which evicts large objects as soon as possible in the case where clients fulfill the role of backup storage, large objects are given higher priority than small objects in the proposed approach. This maximizes the effect of hits for large objects and thereby increases the byte hit rate. Furthermore, we construct simple latency models for various p2p based web caching systems and analyze the effects of the proposed policies on these systems. We then examine the performances of the efficient policies via a trace driven simulation. The results demonstrate that the proposed techniques effectively enhance web cache performance, including hit rate, byte hit rate, and response time.

The traditional rubber hand illusion is a psychological experiment where participants are under the illusion that a rubber hand is part of their own body. This paper examines the use of real, virtual and augmented reality environments for identifying the elements that influence body ownership in healthy participants. Compared to the classical experiment where a plastic rubber hand was used, a realistic 3D representation was chosen to create the same illusion this time in both immersive virtual reality and augmented reality. Experiments were performed on 30 volunteers undergoing testing session composed of three stages. Participants were asked to complete two different questionnaires, one measuring their cognitive workload and another one regarding their experience with the rubber hand illusion. In addition, EEG signals of the individuals were recorded, resulting in 90 electroencephalogram datasets. Results indicate correlations between ownership statements with beta and gamma electroencephalogram bands in premotor cortex activity. Link between higher gamma production in ventral premotor area during the illusion was established in previous studies.

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

Interpreting ocean remote sensing images is still a challenge is worth studying because they can carry valuable information for various important applications. Due to the absence of labeled datasets, unsupervised object-based image analysis (OBIA) methods provide an effective solution to understand remote sensing images with its advantage of grouping local similar pixels into a homogeneous area. However, ocean remote sensing images usually have the characteristics of large size, large background, and coexisting of large and small objects, which result in previous OBIA methods easily falling into the difficulty of accurately segmenting the large and small objects at the same time, and the dilemma of time-consuming computation. To solve this problem, a novel multi-scale and multi-subgraph (MSMSG)-based image segmentation method is presented in this paper. First, a coarse-to-fine superpixel generation method is designed to generate optimal superpixels, which can not only solve the problem of coexisting large objects and small objects but also the problem of manually setting the initial segmentation number. Second, the proposed background removal strategy helps to eliminate the trouble of large background areas in ocean remote sensing images. Third, a multi-subgraph is constructed with the help of background removal. Finally, the multi-scale and multi-subgraph merging strategy is addressed to group all similar superpixels into a same cluster, which not only reduces the useless computation of nonadjacent superpixels but also avoids segmentation errors with the same scale. Experiments conducted on three different datasets show that the proposed segmentation method is high-performance and high-efficiency.

Purpose Immersive Virtual Reality (VR) systems allow for highly repetitive tasks to be performed within a virtual environment that increases practice in home environments. VR can increase access to rehabilitation by reducing access barriers. However, rehabilitation outcomes between immersive VR systems and conventional physical rehabilitation are not well understood. The purpose of this case study was to assess the use of a custom clinically based VR simulation for testing gross hand dexterity with an individual with chronic stroke. Materials and methods The participant performed the box and blocks test (BBT) in an immersive VR environment and a physical environment. Three trials of the BBT were performed with their less-affected and affected hands each in both environments while measuring cortical activity using fNIRS. Rests were given between trials and environment conditions. Results Our results show that there was no statistical difference in the number of blocks moved between the physical and VR BBT for both the affected and less-affected hands. Furthermore, our results also indicate no statistically significant difference between the physical BBT and VR BBT conditions on contralateral motor cortex activation, suggesting that cortical involvement is comparable between physical and VR conditions. Conclusions These results suggest that an immersive VR system may be able to elicit functional and motor cortex activations that are comparable to the conventional physical BBT. Importantly, these findings highlights the potential benefits of VR therapy as a remote therapy intervention and/or to increase the effectiveness and practicality of current in-person rehabilitation programs. Implications for rehabilitation These findings highlight the potential benefits of immersive virtual reality as a remote therapy intervention. Immersive virtual reality use has potential benefits to increase the effectiveness and practicality of current in-person rehabilitation programs.

Paradoxical perception of object identity in visual motion

Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that, underwater images are not clear due to several factors such as: low light, the presence of small particles, different levels of refraction of light etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNetisadeeplearning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features which helps to work with above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation and added downsampling and upsampling to improve detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. The experiment on the Brackish and Trash-ICRA19 datasets shows that our proposed method enhances the mean average precision for both datasets by 10% and 9%, respectively, compared to the original YOLOv3 and other existing work. This enhancement demonstrates that our proposed model is more appropriate for identifying submerged items and is also capable of recognizing closely clustered, small, very large objects on the ocean floor.

Does how I look at what you're doing depend on what I'm doing?