Léo Pini Magalhães

Coordination of collaborative activities: a framework for the definition of tasks interdependencies

The coordination of interdependencies between tasks in collaborative environments is a very important and difficult endeavour. The separation between tasks and interdependencies allows for the use of different coordination policies in the same collaborative environment by changing only the coordination mechanisms that control the interdependencies. This paper presents a framework for the definition of interdependencies that frequently occur in collaborative activities. By means of a clear characterization of interdependencies, it is possible to identify coordination mechanisms to manage them, opening the way toward a powerful coordination tool capable of encompassing a wide range of collaborative applications. An implementation of the coordination model of a collaborative virtual environment based on the proposed framework is given as example.

Active learning paradigms for CBIR systems based on optimum-path forest classification

This paper discusses methods for content-based image retrieval (CBIR) systems based on relevance feedback according to two active learning paradigms, named greedy and planned. In greedy methods, the system aims to return the most relevant images for a query at each iteration. In planned methods, the most informative images are returned during a few iterations and the most relevant ones are only presented afterward. In the past, we proposed a greedy approach based on optimum-path forest classification (OPF) and demonstrated its gain in effectiveness with respect to a planned method based on support-vector machines and another greedy approach based on multi-point query. In this work, we introduce a planned approach based on the OPF classifier and demonstrate its gain in effectiveness over all methods above using more image databases. In our tests, the most informative images are better obtained from images that are classified as relevant, which differs from the original definition. The results also indicate that both OPF-based methods require less user involvement (efficiency) to satisfy the users expectation (effectiveness), and provide interactive response times.

Coordination components for collaborative virtual environments

This paper deals with the behavior of virtual environments from the collaboration point-of-view, in which actors (human or virtual beings) interact and collaborate by means of interdependent tasks. In this sense, actors may realize tasks that are dependent on tasks performed by other actors, while the interdependencies between tasks (through resource management and temporal relations) delineate the overall behavior of a virtual environment. Our main goal is to propose an approach for the coordination of those behaviors. Initially a generic study of possible interdependencies between collaborative tasks is presented, followed by the formal modeling (using Petri Nets) of coordination mechanisms for those dependencies. In order to implement such mechanisms, an architecture of reusable and pluggable coordination components is also introduced. These components are used in an implementation of a multi-user videogame. The presented approach is a concrete step to create virtual societies of actors that collaborate to reach common goals without the risk of getting involved in conflicting or repetitive tasks. r 2001 Elsevier Science Ltd. All rights reserved.

Technical Section: Facial animation based on context-dependent visemes

This paper presents a novel approach for the generation of realistic speech synchronized 3D facial animation that copes with anticipatory and perseveratory coarticulation. The methodology is based on the measurement of 3D trajectories of fiduciary points marked on the face of a real speaker during the speech production of CVCV non-sense words. The trajectories are measured from standard video sequences using stereo vision photogrammetric techniques. The first stationary point of each trajectory associated with a phonetic segment is selected as its articulatory target. By clustering according to geometric similarity all articulatory targets of a same segment in different phonetic contexts, a set of phonetic context-dependent visemes accounting for coarticulation is identified. These visemes are then used to drive a set of geometric transformation/deformation models that reproduce the rotation and translation of the temporomandibular joint on the 3D virtual face, as well as the behavior of the lips, such as protrusion, and opening width and height of the natural articulation. This approach is being used to generate 3D speech synchronized animation from both natural and synthetic speech generated by a text-to-speech synthesizer.

Virtual Reality in Brazil 2011: Simulating crowds based on a space colonization algorithm

This paper presents a method for crowd simulation based on a biologically motivated space colonization algorithm. This algorithm was originally introduced to model leaf venation patterns and the branching architecture of trees. It operates by simulating the competition for space between growing veins or branches. Adapted to crowd modeling, the space colonization algorithm focuses on the competition for space among moving agents. Several behaviors observed in real crowds, including collision avoidance, relationship of crowd density and speed of agents, and the formation of lanes in which people follow each other, are emergent properties of the algorithm. The proposed crowd modeling method is free-of-collision, simple to implement, robust, computationally efficient, and suited to the interactive control of simulated crowds.

AN INTERACTIVE MODEL FOR STEERING BEHAVIORS OF GROUPS OF CHARACTERS

This article presents an approach for generating steering behaviors of groups of characters based on the space colonization algorithm that has been used in the past for generating leaf venation patterns and tree structures. In this article, the underlying idea of the space colonization algorithm is adapted to control the motion of virtual characters, providing robust and realistic group behaviors by adjusting just a few parameters. The main contributions of this work are the robustness, flexibility, and simplicity of the proposed approach to control groups of characters in an interactive way, providing path planning and a series of group behaviors, such as group formation, alignment among others. We also introduce a possible extension of this model to provide collision avoidance among agents, mainly focused on crowd simulation. In addition, an interactive tool is provided to allow an easy manner for controlling the motion of virtual characters.

Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning

In content-based image retrieval (CBIR) using feedback-based learning, the user marks the relevance of returned images and the system learns how to return more relevant images in a next iteration. In this learning process, image comparison may be based on distinct distance spaces due to multiple visual content representations. This work improves the retrieval process by incorporating multiple distance spaces in a recent method based on optimum-path forest (OPF) classification. For a given training set with relevant and irrelevant images, an optimization algorithm finds the best distance function to compare images as a combination of their distances according to different representations. Two optimization techniques are evaluated: a multi-scale parameter search (MSPS), never used before for CBIR, and a genetic programming (GP) algorithm. The combined distance function is used to project an OPF classifier and to rank images classified as relevant for the next iteration. The ranking process takes into account relevant and irrelevant representatives, previously found by the OPF classifier. Experiments show the advantages in effectiveness of the proposed approach with both optimization techniques over the same approach with single distance space and over another state-of-the-art method based on multiple distance spaces.

Using fuzzy Petri nets to coordinate collaborative activities

This paper presents a fuzzy Petri net based approach suitable for the modeling of flexible coordination mechanisms to deal with temporal interdependencies between collaborative tasks. Such approach is based on an extension of the generalized fuzzy Petri net model, including the notion of time for the execution and synchronization of these tasks. A scenario of study is described, indicating the suitability of the proposal.

Building interactive animations using VRML and Java

The paper exploits the combination of VRML (Virtual Reality Modeling Language) and Java for the construction of highly interactive animations, whose behaviour is defined in real time by a users actions. The animations are modeled in VRML, which allows the definition of a Java program to process and generate events that determine the behaviour of scene elements. An application for the generation of Java graphical interfaces was developed, aiming to establish the communication between the user and the VRML environment, sending parameters to the program that controls the animation.

Interactive classification of remote sensing images by using optimum-path forest and genetic programming

The use of remote sensing images as a source of information in agribusiness applications is very common. In those applications, it is fundamental to know how the space occupation is. However, identification and recognition of crop regions in remote sensing images are not trivial tasks yet. Although there are automatic methods proposed to that, users very often prefer to identify regions manually. That happens because these methods are usually developed to solve specific problems, or, when they are of general purpose, they do not yield satisfying results. This work presents a new interactive approach based on relevance feedback to recognize regions of remote sensing. Relevance feedback is a technique used in content-based image retrieval (CBIR) tasks. Its objective is to aggregate user preferences to the search process. The proposed solution combines the Optimum-Path Forest (OPF) classifier with composite descriptors obtained by a Genetic Programming (GP) framework. The new approach has presented good results with respect to the identification of pasture and coffee crops, overcoming the results obtained by a recently proposed method and the traditional Maximimun Likelihood algorithm.