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Face recognition remains a challenge today as recognition performance is strongly affected by variability such as illumination, expressions and poses. In this work we apply Convolutional Neural Networks (CNNs) on the challenging task of both 2D and 3D face recognition. We constructed two CNN models, namely CNN-1 (two convolutional layers) and CNN-2 (one convolutional layer) for testing on 2D and 3D...
This work presents a Virtual Reality training environment for upper limb amputees. Based on principles of a serious game, the training environment aims to condition the patient to use a prosthesis before it is manufactured. Studies show that the time of adjustment for use of a real prosthesis is considerably high. This often brings immense dismay to those patients who are already psychologically depressed...
The paper presents an overview of contemporary approaches to application of virtual reality systems for training of operating personnel for man-machine systems. Special emphases are made on application issues of virtual and alternate reality systems connected with probable cognitive dissonance in operation of real physical objects. The prospects of virtual reality application in professional training...
Every link of power system may exist security risks, power industry funds and technology is highly concentrated, the production equipment is very expensive, its operating parameters almost reached the extreme, physical simulation of its production process of experimental teaching costs are extremely high, resource consumption is huge, and may cause malignant environmental pollution. Teaching, training...
We propose a robust hand pose estimation method by learning hand articulations from depth features and auxiliary modality features. As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion. The proposed heat distribution descriptor is robust to identify the keypoints on the surface as it incorporates both the local...
This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of the surface structure can be directly learned for the purpose of multiview stereopsis...
In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output, which play the role of a structured dimensionality reduction on the feature vector, leading to drastic improvements...
In this paper, we present a study conducted to investigate the feasibility and effectiveness of Virtual Reality (VR) applied to daily living skills (DLS) training of individuals diagnosed with Autism Spectrum Disorder (ASD). In collaboration with a teacher at a school for children and adolescents with mental disorders, a head-mounted display based VR simulation of a supermarket was built and evaluated...
Generating a realistic image from a novel viewpoint has always been a key problem in image-based rendering and other related domains. In this paper we utilize the state-of-the-art generative adversarial networks(GAN) to synthesize novel views of a structured scene. Based on our proposed representations for traffic scene, a realistic image of a certain viewpoint can be generated via conditional GANs,...
Video scene parsing is challenging due to the following two reasons: firstly, it is non-trivial to learn meaningful video representations for producing the temporally consistent labeling map; secondly, such a learning process becomes more difficult with insufficient labeled video training data. In this work, we propose a unified framework to address the above two problems, which is to our knowledge...
We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous...
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces...
With the developing of modern training, advanced requirements of training process such as creative, economical, realistic, safety have been proposed. It is hard for tradition training method to satisfy the training requirement of new technology and equipment in the background of modern industry. Traditional training methods were facing severe challenges. In order to solve the complex technical training...
In this paper we evaluate the effects of various data augmentation techniques on the automated classification of celiac disease using endoscopic imagery in the circumstances of limited training data. The used data augmentation techniques range from standard augmentation techniques like cropping patches and flipping to augmentation techniques using the full spectrum of affine or even projective transformations...
The paper deals with the use of modelling and simulation tools for preparation and implementation of exercises of Integrated Rescue System components and crisis management bodies with the main emphasis on the use of means of constructive simulation using SIMEX simulator. A scenario of multiple traffic accident was prepared for the crisis management authorities. Activities performed by crisis management...
Percutaneous therapy is a common clinical operation in minimally invasive surgery. Yet, learning curve of this skillful manual operation is steep, which imposes negative impacts on its further advances. In this paper, we proposed a novel workflow to simulate percutaneous therapy through visuo-haptic rendering based on the clinical trials. Intraoperative puncture data, obtained by our 6DOF force recording...
Virtual accident scenes in specific environments are simulated via the virtual reality technology. In the light of emergency rescue principles, trainees are allowed to mobilize and deploy emergency rescue forces, and work out combat schemes to control the development and expansion of accidents. In addition, based on the tactical knowledge repository, the system carries out logical reasoning for combat...
Reinforcement learning is one of the best methods to train autonomous robots. Using this method, a robot can learn to make optimal decisions without detailed programming and hard coded instructions. So, this method is useful for learning complex robotic behaviors. For example, in RoboCup competitions this method will be very useful in learning different behaviors. We propose a method for training...
Haptic skills are essential for effective Cardiopulmonary Resuscitation (CPR). Existing CPR training simulators provide unrealistic chest conditions. It is assumed that the CPR performance on a real human chest is the same even when the trainees have learnt on unrealistic dummy chests. To test this assumption we have developed an immersive Virtual Reality based CPR simulator in which force feedback...
Distinguishing subtle differences in attributes is valuable, yet learning to make visual comparisons remains nontrivial. Not only is the number of possible comparisons quadratic in the number of training images, but also access to images adequately spanning the space of fine-grained visual differences is limited. We propose to overcome the sparsity of supervision problem via synthetically generated...
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