Learning the dynamics of articulated tracked vehicles

In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV

High-resolution SAR images for fire susceptibility estimation in urban forestry

We present an adaptive system for the automatic assessment of both physical and anthropic fire impact factors on periurban forestries. The aim is to provide an integrated methodology exploiting a complex data structure built upon a multi resolution grid gathering historical land exploitation and meteorological data, records of human habits together with suitably segmented and interpreted high resolution X-SAR images, and several other information sources. The contribution of the model and its novelty rely mainly on the definition of a learning schema lifting different factors and aspects of fire causes, including physical, social and behavioural ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way

Bayesian non-parametric inference for manifold based MoCap representation

We propose a novel approach to human action recognition, with motion capture data (MoCap), based on grouping sub-body parts. By representing configurations of actions as manifolds, joint positions are mapped on a subspace via principal geodesic analysis. The reduced space is still highly informative and allows for classification based on a non-parametric Bayesian approach, generating behaviors for each sub-body part. Having partitioned the set of joints, poses relative to a sub-body part are exchangeable, given a specified prior and can elicit, in principle, infinite behaviors. The generation of these behaviors is specified by a Dirichlet process mixture. We show with several experiments that the recognition gives very promising results, outperforming methods requiring temporal alignment

Point Cloud Structural Parts Extraction based on Segmentation Energy Minimization

In this work we consider 3D point sets, which in a typical setting represent unorganized point clouds. Segmentation of these point sets requires first to single out structural components of the unknown surface discretely approximated by the point cloud. Structural components, in turn, are surface patches approximating unknown parts of elementary geometric structures, such as planes, ellipsoids, spheres and so on. The approach used is based on level set methods computing the moving front of the surface and tracing the interfaces between different parts of it. Level set methods are widely recognized to be one of the most efficient methods to segment both 2D images and 3D medical images. Level set methods for 3D segmentation have recently received an increasing interest. We contribute by proposing a novel approach for raw point sets. Based on the motion and distance functions of the level set we introduce four energy minimization models, which are used for segmentation, by considering an equal number of distance functions specified by geometric features. Finally we evaluate the proposed algorithm on point sets simulating unorganized point clouds

Rigid tool affordance matching points of regard

In this abstract we briefly introduce the analysis of simple rigid object affordance by experimentally establishing the relation between the point of regard of subjects before grasping an object and the finger tip points of contact once the object is grasped. The analysis show that there is a strong relation between these data, in so justifying the hypothesis that people figures out how objects are afforded according to their functionality

Component-wise modeling of articulated objects

We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time

The well-designed logical robot: Learning and experience from observations to the Situation Calculus

The well-designed logical robot paradigmatically represents, in the words of McCarthy, the abilities that a robot-child should have to reveal the structure of reality within a "language of thought". In this paper we partially support McCarthy's hypothesis by showing that early perception can trigger an inference process leading to the "language of thought". We show this by defining a systematic transformation of structures of different formal languages sharing the same signature kernel for actions and states. Starting from early vision, visual features are encoded by descriptors mapping the space of features into the space of actions. The densities estimated in this space form the observation layer of a hidden states model labelling the identified actions as observations and the states as action preconditions and effects. The learned parameters are used to specify the probability space of a first-order probability model. Finally we show how to transform the probability model into a model of the Situation Calculus in which the learning phase has been reified into axioms for preconditions and effects of actions and, of course, these axioms are expressed in the language of thought. This shows, albeit partially, that there is an underlying structure of perception that can be brought into a logical language. © 2010 Elsevier B.V. All rights reserved

About implicit and explicit shape representation

We present a composite analysis of shapes based on form and features. We discuss how form and features are two facets of object representation and how similarity measures are used to understand the relation between two objects' images. We present a novel approach to approximate a shape that can still make use of Procrustes distance, leading to a relaxed notion of similarity measure. We introduce also a study on the similarity measures for non-parametric kernel densities. Finally we briefly discuss how these distance measures can be combined and represented into a Bayesian network, to learn the parameters of the defined similarity function. © 2006 Springer-Verlag Berlin/Heidelberg