A comparative study of navigation meshes

International audience

Context Models for Understanding Images and Videos

A computer vision system that has to interact in natural language needs to understand the visual appearance of interactions between objects along with the appearance of objects themselves. Relationships between objects are frequently mentioned in queries of tasks like semantic image retrieval, image captioning, visual question answering and natural language object detection. Hence, it is essential to model context between objects for solving these tasks. In the first part of this thesis, we present a technique for detecting an object mentioned in a natural language query. Specifically, we work with referring expressions which are sentences that identify a particular object instance in an image. In many referring expressions, an object is described in relation to another object using prepositions, comparative adjectives, action verbs etc. Our proposed technique can identify both the referred object and the context object mentioned in such expressions. Context is also useful for incrementally understanding scenes and videos. In the second part of this thesis, we propose techniques for searching for objects in an image and events in a video. Our proposed incremental algorithms use the context from previously explored regions to prioritize the regions to explore next. The advantage of incremental understanding is restricting the amount of computation time and/or resources spent for various detection tasks. Our first proposed technique shows how to learn context in indoor scenes in an implicit manner and use it for searching for objects. The second technique shows how explicitly written context rules of one-on-one basketball can be used to sequentially detect events in a game.

EACS: Effective Avoidance Combination Strategy

International audience

Contribuciones sobre métodos óptimos y subóptimos de aproximaciones poligonales de curvas 2-D

Esta tesis versa sobre el an álisis de la forma de objetos 2D. En visión articial existen numerosos aspectos de los que se pueden extraer información. Uno de los más usados es la forma o el contorno de esos objetos. Esta característica visual de los objetos nos permite, mediante el procesamiento adecuado, extraer información de los objetos, analizar escenas, etc. No obstante el contorno o silueta de los objetos contiene información redundante. Este exceso de datos que no aporta nuevo conocimiento debe ser eliminado, con el objeto de agilizar el procesamiento posterior o de minimizar el tamaño de la representación de ese contorno, para su almacenamiento o transmisión. Esta reducción de datos debe realizarse sin que se produzca una pérdida de información importante para representación del contorno original. Se puede obtener una versión reducida de un contorno eliminando puntos intermedios y uniendo los puntos restantes mediante segmentos. Esta representación reducida de un contorno se conoce como aproximación poligonal. Estas aproximaciones poligonales de contornos representan, por tanto, una versión comprimida de la información original. El principal uso de las mismas es la reducción del volumen de información necesario para representar el contorno de un objeto. No obstante, en los últimos años estas aproximaciones han sido usadas para el reconocimiento de objetos. Para ello los algoritmos de aproximaci ón poligonal se han usado directamente para la extracci ón de los vectores de caracter ísticas empleados en la fase de aprendizaje. Las contribuciones realizadas por tanto en esta tesis se han centrado en diversos aspectos de las aproximaciones poligonales. En la primera contribución se han mejorado varios algoritmos de aproximaciones poligonales, mediante el uso de una fase de preprocesado que acelera estos algoritmos permitiendo incluso mejorar la calidad de las soluciones en un menor tiempo. En la segunda contribución se ha propuesto un nuevo algoritmo de aproximaciones poligonales que obtiene soluciones optimas en un menor espacio de tiempo que el resto de métodos que aparecen en la literatura. En la tercera contribución se ha propuesto un algoritmo de aproximaciones que es capaz de obtener la solución óptima en pocas iteraciones en la mayor parte de los casos. Por último, se ha propuesto una versi ón mejorada del algoritmo óptimo para obtener aproximaciones poligonales que soluciona otro problema de optimización alternativo.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Análisis predictivo para clasificar dígitos escritos a mano utilizando la base de datos MNIST

En el presente trabajo de fin de máster se realiza una investigación sobre las técnicas de preproceso del dataset de entrenamiento y la aplicación de un modelo de predicción que realice una clasificación de dı́gitos escritos a mano. El conjunto de dataset de train y test son proporcionado en la competencia de Kaggle: Digit Recognizer y provienen de la base de datos de dı́gitos manuscritos MNIST. Por tratarse de imágenes las técnicas de preproceso se concentran en obtener una imagen lo más nı́tida posible y la reducción de tamaño de la misma, objetivos que se logran con técnicas de umbralización por el método de Otsu, transformada de Wavelet de Haar y el análisis de sus componentes principales. Se utiliza Deep Learning como modelo predictivo por ajustarse a este tipo de datos, se emplean además librerı́as de código abierto implementadas en el lenguaje estádisto R. Por último se obtiene una predicción con las técnicas y herramientas mencio- nadas para ser evaluada en la competencia de Kaggle, midiendo y comparando los resultados obtenidos con el resto de participantes.

Guided Bus Rapid Transit

Electrical Bus Rapid Transit (eBRT) is a charging electrical public transport which brings a clean, high performance, and affordable cost alternative from the conventional traffic vehicles which work with combustion and hybrid technology. These buses charge the battery in every bus stop to arrive at the next station. But, this charging system needs an appropriate infrastructure called pantograph, and it requires a high precision bus location to maintain battery lifetime, energy saving and charging time. To overcome this issue Vicomtech and Datik has planned a project based on computer vision to help to the driver to locate the vehicle in the correct place. In this document, we present a mono camera bus driver guided fast algorithm because these vehicles embedded computers do not support high computation and precision operations. In addition to the frequent lane sign, there are more accurate geometric beacons painted on the road to bring metric information to the vision system. This method uses segmentation to binarize the image discriminating the background space. Besides it detects, tracks and counts different lane mark contours in addition to classify each special painted mark. Besides it does not need any calibration task to calculate longitudinal and cross distances because we know the lane mark sizes.

Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.

Improving surgery operations by means of cloud systems and distributed user interfaces

Surgical interventions are usually performed in an operation room; however, access to the information by the medical team members during the intervention is limited. While in conversations with the medical staff, we observed that they attach significant importance to the improvement of the information and communication direct access by queries during the process in real time. It is due to the fact that the procedure is rather slow and there is lack of interaction with the systems in the operation room. These systems can be integrated on the Cloud adding new functionalities to the existing systems the medical expedients are processed. Therefore, such a communication system needs to be built upon the information and interaction access specifically designed and developed to aid the medical specialists. Copyright 2014 ACM.

Improving user-insurance communication on accident reports

This paper presents an easy to use methodology and system for insurance companies targeting at managing traffic accidents reports process. The main objective is to facilitate and accelerate the process of creating and finalizing the necessary accident reports in cases without mortal victims involved. The diverse entities participating in the process from the moment an accident occurs until the related final actions needed are included. Nowadays, this market is limited to the consulting platforms offered by the insurance companies. Copyright 2014 ACM.

Journalisation dans un Programme de carrière en nouveaux médias : Le rôle de la réflexion par l’action

The purpose of this case study is to report on the use of learning journals as a strategy to encourage critical reflection in the field of graphic design. Very little empirical research has been published regarding the use of critical reflection in learning journals in this field. Furthermore, nothing has been documented at the college level. To that end, the goal of this research endeavor was to investigate whether second-year students in the NewMedia and Publication Design Program at a small Anglophone CEGEP in Québec, enrolled in a Page Layout and Design course, learn more deeply by reflecting in action during design projects or reflecting on action after completing design projects. Secondarily, indications of a possible change in self-efficacy were examined. Two hypotheses were posited: 1) reflection-on-action journaling will promote a deeper approach to learning than reflection-in-action journaling, and 2) the level of self-efficacy in graphic design improves as students are encouraged to think reflectively. Using both qualitative and quantitative methods, a mixed methods approach was used to collect and analyze the data. Content analysis of journal entries and interview responses was the primary method used to address the first hypothesis. Students were required to journal twice for each of three projects, once during the project and again one week after the project had been submitted. In addition, data regarding the students' perception of journaling was obtained through administering a survey and conducting interviews. For the second hypothesis, quantitative methods were used through the use of two surveys, one administered early in the Fall 2011 semester and the second administered early in the Winter 2012 semester. Supplementary data regarding self-efficacy was obtained in the form of content analysis of journal entries and interviews. Coded journal entries firmly supported the hypothesis that reflection-on-action journaling promotes deep learning. Using a taxonomy developed by Kember et al. (1999) wherein "critical reflection" is considered the highest level of reflection, it was found that only 5% of the coded responses in the reflection-in-action journals were deemed of the highest level, whereas 39% were considered critical reflection in the reflection-on-action journals. The findings from the interviews suggest that students had some initial concerns about the value of journaling, but these concerns were later dismissed as students learned that journaling was a valuable tool that helped them reflect and learn. All participants indicated that journaling changed their learning processes as they thought much more about what they were doing while they were doing it. They were taking the learning they had acquired and thinking about how they would apply it to new projects; this is critical reflection. The survey findings did not support the conclusive results of the comparison of journal instruments, where an increase of 35% in critical reflection was noted in the reflection-on-action journals. In Chapter 5, reasons for this incongruence are explored. Furthermore, based on the journals, surveys, and interviews, there is not enough evidence at this time to support the hypothesis that self-efficacy improves when students are encouraged to think reflectively. It could be hypothesized, however, that one's self-efficacy does not change in such a short period of time. In conclusion, the findings established in this case study make a practical contribution to the literature concerning the promotion of deep learning in the field of graphic design, as this researcher's hypothesis was supported that reflection-on-action journaling promoted deeper learning than reflection-in-action journaling. When examining the increases in critical reflection from reflection-in-action to the reflection-on-action journals, it was found that all students but one showed an increase in critical reflection in reflection-on-action journals. It is therefore recommended that production-oriented program instructors consider integrating reflection-on-action journaling into their courses where projects are given.

Dynamic Match Analysis

Recent technological development has enabled research- ers to gather data from different performance scenarios while considering players positioning and action events within a specific time frame. This technology varies from global positioning systems to radio frequency devices and computer vision tracking, to name the most common, and aims to collect players’ time motion data and enable the dynamical analysis of performance. Team sports—and in particular, invasion games—present a complex dynamic by nature based on the interaction between 2 opposing sides trying to outperform 1 another. During match and training situations, players’ actions are coupled to their performance context at different interaction levels. As expected, ball, teammates’, and opponents’ positioning play an important role in this interaction process. But other factors, such as final score, teams’ development level, and players’ expertise, seem to affect the match dynamics. In this symposium, we will focus on how different constraints affect invasion games dynamics during both match and training situations. This relation will be established while underpinning the importance of these effects to game teaching and performance optimization. Regarding the match, different performance indicators based on spatial-temporal relations between players and teams will be presented to reveal the interaction processes that form the crucial component of game analysis. Considering the training, this symposium will address the relationship of small-sided games with full- sized matches and will present how players’ dynamical interaction affects different performance indicators.

Automotive Radars:towards smarter vehicles

Radars are expected to become the main sensors in various civilian applications, especially for autonomous driving. Their success is mainly due to the availability of low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. This thesis focuses on the study and the development of different deterministic and learning based techniques for colocated multiple-input multiple-output (MIMO) radars. In particular, after providing an overview on the architecture of these devices, the problem of detecting and estimating multiple targets in stepped frequency continuous wave (SFCW) MIMO radar systems is investigated and different deterministic techniques solving it are illustrated. Moreover, novel solutions, based on an approximate maximum likelihood approach, are developed. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from low power wideband radar devices. The results demonstrate that the developed algorithms achieve reasonable accuracies, but at the price of different computational efforts. Another important technical problem investigated in this thesis concerns the exploitation of machine learning and deep learning techniques in the field of colocated MIMO radars. In this thesis, after providing a comprehensive overview of the machine learning and deep learning techniques currently being considered for use in MIMO radar systems, their performance in two different applications is assessed on the basis of synthetically generated and experimental datasets acquired through a commercial frequency modulated continuous wave (FMCW) MIMO radar. Finally, the application of colocated MIMO radars to autonomous driving in smart agriculture is illustrated.

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly larger than seen during training.