Valdinei Freire da Silva

Mutual occlusion between real and virtual elements in Augmented Reality based on fiducial markers

Augmented Reality (AR) systems which use optical tracking with fiducial marker for registration have had an important role in popularizing this technology, since only a personal computer with a conventional webcam is required. However, in most these applications, the virtual elements are shown only in the foreground a real element does not occlude a virtual one. The method presented enables AR environments based on fiducial markers to support mutual occlusion between a real element and many virtual ones, according to the elements position (depth) in the environment.

Subjective Video Quality Assessment in Segmentation for Augmented Reality Applications

Video segmentation to extract a person in foreground has been a common task in many Augmented Reality (AR) applications. In natural environments which the background color and the light environment are not constant the segmentation method must be able to extract the element of the interest in these conditions. However, methods for segmentation in natural environment are more error prone than the traditional ones which are based on a constant color elimination. Thus, in order to use them in a large number of applications it is necessary to know how the segmentation errors are perceived by the users in order to focus on development of algorithms which avoid the more perceptible ones. In this work a video quality subjective assessment method was applied to obtain the AR users opinions about videos with different misclassified pixels rates. The results showed that segmentation errors are perceived by AR applications users. However, the video quality was not related with the number of the misclassified pixels. In addiction, it was noted that when the errors concentrated in the element of the interest increase the score of the associated video decreases.

Shortest stochastic path with risk sensitive evaluation

In an environment of uncertainty where decisions must be taken, how to make a decision considering the risk? The shortest stochastic path (SSP) problem models the problem of reaching a goal with the least cost. However under uncertainty, a best decision may: minimize expected cost, minimize variance, minimize worst case, maximize best case, etc. Markov Decision Processes (MDPs) defines optimal decision in the shortest stochastic path problem as the decision that minimizes expected cost, therefore MDPs does not care about the risk. An extension of MDP which has few works in Artificial Intelligence literature is Risk Sensitive MDP. RSMDPs considers the risk and integrates expected cost, variance, worst case and best case in a simple way. We show theoretically the differences and similarities between MDPs and RSMDPs for modeling the SSP problem, in special the relationship between the discount factor γ and risk prone attitudes under the SSP with constant cost. We also exemplify each model in a simple artificial scenario.

Finding Memoryless Probabilistic Relational Policies for Inter-task Reuse

Relational representations let sequential decision problems be described through objects and relations, resulting in more compact, expressive, and domain-independent representations that make it possible to find and generalize solutions much faster than propositional representations. In this paper we propose a modified policy iteration algorithm (AbsProb-PI) for the infinite-horizon discounted-reward criterion; the algorithm finds a memoryless probabilistic relational abstract policy that abstracts well the solution from source problems so that it can be applied in new, similar problems. Experiments in robotic navigation validate our proposals and show that we can find effective and efficient abstract policies, outperforming solutions by inductive approaches in the literature.

Bilayer segmentation augmented with future evidence

This paper presents an algorithm that augments a previous model known in the literature for the automatic segmentation of monocular videos into foreground and background layers. The original model fuses visual cues such as color, contrast, motion and spatial priors within a Conditional Random Field. Our augmented model makes use of bidirectional motion priors by exploiting future evidence. Although our augmented model processes more data, it does so with the same time performance of the original model. We evaluate the augmented model within ground truth data and the results show that the augmented model produces better segmentation.

Objective assessment of video segmentation quality for augmented reality

Assessment of video segmentation quality is a problem seldom investigated by the scientific community. Nevertheless, recent studies presented some objective metrics to evaluate algorithms. Such metrics consider different ways in which segmentation errors occur (perceptual factors) and its parameters are adjusted according to the application for which the segmented frames are intended. We demonstrate empirically that the performance of existing metrics changes according to the segmentation algorithm; we applied such metrics to evaluate bilayer segmentation algorithms for compose scenes in Augmented Reality environments. We also contribute with a new objective metric to adjust the parameters of two bilayer segmentation algorithms found in the literature.

Stochastic abstract policies for knowledge transfer in robotic navigation tasks

Most work in navigation approaches for mobile robots does not take into account existing solutions to similar problems when learning a policy to solve a new problem, and consequently solves the current navigation problem from scratch. In this article we investigate a knowledge transfer technique that enables the use of a previously know policy from one or more related source tasks in a new task. Here we represent the knowledge learned as a stochastic abstract policy, which can be induced from a training set given by a set of navigation examples of state-action sequences executed successfully by a robot to achieve a specific goal in a given environment. We propose both a probabilistic and a nondeterministic abstract policy, in order to preserve the occurrence of all actions identified in the inductive process. Experiments carried out attest to the effectiveness and efficiency of our proposal.

Reusing Risk-Aware Stochastic Abstract Policies in Robotic Navigation Learning

In this paper we improve learning performance of a risk-aware robot facing navigation tasks by employing transfer learning; that is, we use information from a previously solved task to accelerate learning in a new task. To do so, we transfer risk-aware memoryless stochastic abstract policies into a new task. We show how to incorporate risk-awareness into robotic navigation tasks, in particular when tasks are modeled as stochastic shortest path problems. We then show how to use a modified policy iteration algorithm, called AbsProb-PI, to obtain risk-neutral and risk-prone memoryless stochastic abstract policies. Finally, we propose a method that combines abstract policies, and show how to use the combined policy in a new navigation task. Experiments validate our proposals and show that one can find effective abstract policies that can improve robot behavior in navigation problems.

Ad Network Optimization: Evaluating Linear Relaxations

This paper presents a theoretical and empirical analysis of linear programming relaxations to ad network optimization. The underlying problem is to select a sequence of ads to send to websites, while an optimal policy can be produced using a Markov Decision Process, in practice one must resort to relaxations to bypass the curse of dimensionality. We focus on a state-of-art relaxation scheme based on linear programming. We build a Markov Decision Process that captures the worst-case behavior of such a linear programming relaxation, and derive theoretical guarantees concerning linear relaxations. We then report on extensive empirical evaluation of linear relaxations, our results suggest that for large problems (similar to ones found in practice), the loss of performance introduced by linear relaxations is rather small.

Forward and backward feature selection in gradient-based MDP algorithms

In problems modeled as Markov Decision Processes (MDP), knowledge transfer is related to the notion of generalization and state abstraction. Abstraction can be obtained through factored representation by describing states with a set of features. Thus, the definition of the best action to be taken in a state can be easily transferred to similar states, i.e., states with similar features. In this paper we compare forward and backward greedy feature selection to find an appropriate compact set of features for such abstraction, thus facilitating the transfer of knowledge to new problems. We also present heuristic versions of both approaches and compare all of the approaches within a discrete simulated navigation problem.