Cristina Nader Vasconcelos

Bipartite Graph Matching Computation on GPU

The Bipartite Graph Matching Problem is a well studied topic in Graph Theory. Such matching relates pairs of nodes from two distinct sets by selecting a subset of the graph edges connecting them. Each edge selected has no common node as its end points to any other edge within the subset. When the considered graph has huge sets of nodes and edges the sequential approaches are impractical, specially for applications demanding fast results. In this paper we investigate how to compute such matching on Graphics Processing Units (GPUs) motivated by its increasing processing power made available with decreasing costs. We present a new data-parallel approach for computing bipartite graph matching that is efficiently computed on todays graphics hardware and apply it to solve the correspondence between 3D samples taken over a time interval.

Semiotic Inspection of a Game for Children with Down Syndrome

There are not many initiatives in the area of gamedevelopment for children with special needs, specially childrenwith Down syndrome. The major purpose of our research isto promote cognitive development of disabled children in thecontext of inclusive education. In order to do so, we addressaspects of interaction, communication and game design instimulating selected cognitive abilities. By using a Human-Computer Interaction method based on the Inspection ofEvaluation, it was possible to study and understand userinteraction with the interface and thus examine the positiveaspects as well as the communicability problems found withthe JECRIPE game - a game developed specially for childrenwith Down syndrome in pre-scholar age.

Lloyd's Algorithm on GPU

The Centroidal Voronoi Diagram (CVD) is a very versatile structure, well studied in Computational Geometry. It is used as the basis for a number of applications. This paper presents a deterministic algorithm, entirely computed using graphics hardware resources, based on Lloyds Method for computing CVDs. While the computation of the ordinary Voronoi diagram on GPU is a well explored topic, its extension to CVDs presents some challenges that the present study intends to overcome.

JECRIPE: stimulating cognitive abilities of children with Down Syndrome in pre-scholar age using a game approach

Digital games are usually developed to provide fun for people of all ages. Although games have been mostly used for entertainment purposes, they have great potential as an intervention tool in health care. Digital games can be applied in health care helping users to learn or to experience something in a fun way. However, there are important issues to be considered to achieve this goal, specially in the development of applications for people with special needs. In this work, we are concerned specifically with Down syndrome needs, and we present an unhackneyed game for children with Down syndrome between 3 to 7 years old. Children in pre-scholar age need to be stimulated considering different cognitive areas. The stimulation of such cognitive areas can provide good results in the development over the years. Given the current demand and absence of games that fulfill Down syndrome special needs, we developed JECRIPE. JECRIPE is a digital game that stimulates the specific cognitive abilities: imitation, perception, fine motor skills, hand-eye coordination and receptive and expressive verbal language. This paper describes how these cognitive areas are used to stimulate children with Down syndrome in pre-scholar age and some technical issues in the development of the game.

Minimizing cyber sickness in head mounted display systems: Design guidelines and applications

We are experiencing an upcoming trend of using head mounted display systems in games and serious games, which is likely to become an established practice in the near future. While these systems provide highly immersive experiences, many users have been reporting discomfort symptoms, such as nausea, sickness, and headaches, among others. When using VR for health applications, this is more critical, since the discomfort may interfere a lot in treatments. In this work we discuss possible causes of these issues, and present possible solutions as design guidelines that may mitigate them. In this context, we go deeper within a dynamic focus solution to reduce discomfort in immersive virtual environments, when using first-person navigation. This solution applies an heuristic model of visual attention that works in real time. This work also discusses a case study (as a first-person spatial shooter demo) that applies this solution and the proposed design guidelines.

Observed Interaction in Games for Down Syndrome Children

This work proposes a method for evaluating the childrens behavioral interactions with a game, more specifically for evaluating playful applications for kids with cognitive disabilities. Our method introduces an evaluation criteria over childrens behavioral interaction and game design analysis, adapted from a list of breakdown indication types of the Detailed Video Analysis (DEVAN) that was originally designed for regular applications. We present a case study of the proposed evaluation method with a detailed analysis of the game called JECRIPE, originally developed for stimulating cognitive abilities of children with Down syndrome in preschool age. The proposed method adopts qualitative and quantitative criteria to review the initial developmental factors that have driven JECRIPEs design versus the real behavior observed in a group of children playing the game. As results of this case study, we demonstrate the reliability of the evaluation method and the capacity of this method in discovering usability and fun problems in order to be considered and addressed in future game releases.

Evaluating and Customizing User Interaction in an Adaptive Game Controller

When playing a game, the user expects an easy and intuitive interaction. While current game console controllers are physical pre-defined hardware components with a default number, size and position of buttons. Unfortunately, different games require different buttons and demand different interaction methods. Despite that, the play style of each player differs according to personal characteristics (like hand size) or past gaming experiences. To achieve an optimal controller configuration for each player, this work proposes a virtual controller based on a common touchscreen device, such as smartphone or tablet, that will be used as a joystick to control a game on a computer or console, collecting user input data and applying machine learning techniques to adapt the position and size of its virtual buttons, minimizing errors and providing an enjoyable experience. With the prototype controller, tests were performed with a set of users and the collected data showed considerable improvements in the precision and game performance of the players.

Real-Time Video Processing for Multi-Object Chromatic Tracking.

This paper presents MOCT, a multi-object chromatic tracking technique for real-time natural video processing. Its main step is the MOCT localization algorithm, that performs local data evaluations in order to apply a multiple output parallel reduction operator to the image. The reduction operator is used to localize the positions of the object centroids, to compute the number of pixels occupied by an object and its bounding boxes, and to update object trajectories in image space. The operator is analyzed using three different computation layouts and tested over several reduction factors.

PEEK - An LSTM Recurrent Network for Motion Classification from Sparse Data.

Games and other applications are exploring many different modes of interaction in order to create intuitive interfaces, such as touch screens, motion controllers, recognition of gesture or body movements among many others. In that direction, human motion is being captured by different sensors, such as accelerometers, gyroscopes, heat sensors and cameras. However, there is still room for investigation the analysis of motion data captured from low-cost sensors. This article explores the extent to which a full body motion classification can be achieved by observing only sparse data captured by two separate inherent wereable measurement unit (IMU) sensors. For that, we developed a novel Recurrent Neural Network topology based on Long Short-Term Memory cells (LSTMs) that are able to classify motions sequences of different sizes. Using cross-validation tests, our model achieves an overall accuracy of 96% which is quite significant considering that the raw data used was obtained using only 2 simple and accessible IMU sensors capturing arms movements. We also built and made public a motion database constructed by capturing sparse data from 11 actors performing five different actions. For comparison with existent methods, other deep learning approaches for sequence evaluation (more specifically, based on convolutional neural networks), were adapted to our problem and evaluated.

Polygonal Mesh Extraction from Digital Voxel Art

This work presents a method to extract polygonal surfaces from volumetric models created by artists, proposing a way of using voxel modeling tools to build B-Rep models. The volumetric data created by voxel editors usually contain topological features that do not describe solid structures. Hence, the main objective of this work is to solve the problem of extracting triangle meshes from volumes that contain these topological features. In order to extract surfaces successfully, a methodology was conceived to resample any volumetric model, in a way that it is possible to reconstruct a three-dimensional manifold that can be polygonized without generating a surface with gaps or topological problems. The meshes generated by this technique have good properties, satisfying some of the main criteria used to measure the quality of meshes, such as aspect ratio, smoothness and skewness.