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Nowadays, people have to face many challenges In order to become familiar with all the functionalities of electronical devices. The training process becomes thus essential, especially when we are referring to consumer electronics created for people with disabilities. Our paper presents the importance of training visually impaired people for an advanced sensory substitution device. By using the advantages...
First aid saves lives and reanimation is an important part of it. We developed a virtual reality (VR) application, VReanimate, that teaches about this aspect of first aid in a controlled digital environment. In this paper we present its non-verbal approach to guiding as many people as possible through the VR experience. In the first part of this paper, we describe the conceptual and implementational...
Although shadows in images have a constructive role providing a natural view of features of the scene, they also have a destructive role in image processing by hiding significant information. Improving the quality of 3D textured models for serious games and augmented reality applications via shadow detection and removal remains challenging due to the complexity of an image scene. This paper proposes...
In this paper I review gaze-based interaction, distinguishing eye movement analysis from synthesis in virtual reality and games for serious applications. My focus is on four forms of gaze-based interaction: diagnostic, active, passive, and expressive. In discussing each, I briefly review seminal results and recent advancements, highlighting outstanding research problems.
This paper presents a serious game designed for children suffering from profound intellectual and multiple disabilities (PIMD) also know as multihandicap, for their evaluation and cognitive training. The specificities of these children must be taken into account for the choice of both the game feedbacks and interfaces.
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison...
In this article, we develop two visual impression models: recognition model and generalization model to simulate the cognition process of human visual systems. We show how the visual impression learned with a deep neural network can be efficiently transferred to other visual recognition tasks. By reusing the hidden layers trained in an unsupervised way, we show that we can largely reduce the number...
We present in this paper a novel approach for training a topological deep neural network with visual impression. We show that by combing denoising auto-encoder model and contractive auto-encoder with Hessian regularization model, we can achieve a deterministic auto-encoder aiming for robustness to small variations of the input. We exploit the tangent propagation algorithm to show how our algorithm...
Despite of the ultra-wideband (UWB) system's robustness against multipath in cluttered environments, a number of challenges remain before UWB localization can be implemented. In particular, non-line-of-sight (NLOS) propagation is especially critical for high-resolution localization systems because non-negligibly positive biases will be introduced in distance measurements, thus degrading the localization...
Scene recognition is an important and challenging problem in the field of computer vision owing to the variations in the same class and the similarities between different classes. This paper presents a novel approach that learns a reasonable dictionary from convolutional features to effectively describe the distinctive and shared properties in scene images. Substantial convolution operations in Deep...
To improve the accuracy of surface defect detection, an approach of defect inspection based on visual saliency map and Support Vector Machine(SVM) is proposed. Monochrome fabric defect images are taken as examples in this paper. By analyzing the visual saliency maps of these images, the global associated value and the background associated value are extracted as the two features. After being normalized,...
Evaluating aesthetic value of digital photographs is a challenging task, mainly due to numerous factors that need to be taken into account and subjective manner of this process. In this paper, we propose to approach this problem using deep convolutional neural networks. Using a dataset of over 1.7 million photos collected from Flickr, we train and evaluate a deep learning model whose goal is to classify...
Area V5 or Middle Temporal (MT) area of the primate brain is said to be involved in visual motion perception. Physiological studies indicate that the neurons in MT respond selectively to the direction of moving stimuli. However in response to the complex stimuli containing multiple oriented components, a set of MT neurons are selective to the direction of the component motion whereas the other set...
This study aimed to design and develop a cost effective, portable tool as a software application with target games to identify the preferred retinal locus (PRL) in patients with macular diseases and train them using the novel Eccentric Viewing Training Module (EVTM) to further improve their visual abilities such as recognizing faces, reading speed. We have designed a novel software for identifying...
This paper presents a new discriminative learning framework to associate the relationship between the objects and the words in an image and perform template matching scheme for complex association patterns. The problem is first formulated as a bipartite graph matching problem. Thereafter, structural support vector machine (SVM) is employed to obtain the optimal compatibility function to encode the...
In recent years, there has been a growing interest in the modelling of crime commission processes, in particular crime scripting, in physical and cyber spaces. This article aims to demonstrate the limits of unstructured scripting approaches, and advocates the development of more systematic techniques. For this, we examined he differences and similarities between various scripts. Twenty-one participants...
This article presents the objectives of the project, Training of Trainers in Robotics for Schools in Vulnerable Areas of Costa Rica as well as the main activities, and the results that have been obtained in its first phase of execution. This is a joint project of the School of Informatics of the National University of Costa Rica (UNA), the Costa Rican Institute on Drugs (ICD) and the Ministry of Public...
Detecting potential aerial threats like drones with computer vision is at the paramount of interest for the protection of critical locations. This type of a system should prevent efficiently the false alarms caused by non-malign objects such as birds, which intrude the image plane. In this paper, we propose an improved version of a previously presented Speeded-up Robust Feature Transform (SURF) based...
Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical...
This paper investigates the potential of combining deep learning and neuroevolution to create a bot for a simple first person shooter (FPS) game capable of aiming and shooting based on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature representations, while the evolving network takes those features as inputs...
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