Virtual reality is a new technology that simulates a three-dimensional virtual world on a com- puter and enables the generation of visual, audio, and haptic feedback for the full immersion of users. Users can interact...Virtual reality is a new technology that simulates a three-dimensional virtual world on a com- puter and enables the generation of visual, audio, and haptic feedback for the full immersion of users. Users can interact with and observe objects in three-dimensional visual space without limitation. At present, virtual reality training has been widely used in rehabilitation therapy for balance dysfunction. This paper summarizes related articles and other articles suggesting that virtual reality training can improve balance dysfunction in patients after neurological diseases. When patients perform virtual reality training, the prefrontal, parietal cortical areas and other motor cortical networks are activated. These activations may be involved in the reconstruction of neurons in the cerebral cortex. Growing evidence from clinical studies reveals that virtual reality training improves the neurological function of patients with spinal cord injury, cerebral palsy and other neurological impairments. These findings suggest that virtual reality training can acti- vate the cerebral cortex and improve the spatial orientation capacity of patients, thus facilitating the cortex to control balance and increase motion function.展开更多
In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in term...In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.展开更多
基金supported by the National Natural Science Foundation of China,No.30973165 and 81372108Guangdong Province College Students Innovative Research Projects in 2013
文摘Virtual reality is a new technology that simulates a three-dimensional virtual world on a com- puter and enables the generation of visual, audio, and haptic feedback for the full immersion of users. Users can interact with and observe objects in three-dimensional visual space without limitation. At present, virtual reality training has been widely used in rehabilitation therapy for balance dysfunction. This paper summarizes related articles and other articles suggesting that virtual reality training can improve balance dysfunction in patients after neurological diseases. When patients perform virtual reality training, the prefrontal, parietal cortical areas and other motor cortical networks are activated. These activations may be involved in the reconstruction of neurons in the cerebral cortex. Growing evidence from clinical studies reveals that virtual reality training improves the neurological function of patients with spinal cord injury, cerebral palsy and other neurological impairments. These findings suggest that virtual reality training can acti- vate the cerebral cortex and improve the spatial orientation capacity of patients, thus facilitating the cortex to control balance and increase motion function.
基金supported by the National Natural Science Foundation of China(61533019,71232006)
文摘In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
