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Publications

  1. C. Andreatta; M. Lecca; S. Messelodi,
    Memory-based Object Recognition in digital Images,
    Berlin,
    AKA Akademische Verlagsgesellschaft,
    2005
    , pp. 33-
    40
    , (19th International Fall Workshop- Vision Modeling and Visualization,
    Erlangen, Germany,
    11/16/2005 - 11/18/2005)
  2. P. Chippendale,
    Real-Time Skin Labelling in Active Camera Images,
    2005
    , (Workshop on Multimodal Interaction and Related Machine Learning Algorithms - MLMI 2005,
    Edinburgh, UK,
    da 07/11/2005 a 07/13/2005)
  3. O. Lanz; R. Manduchi,
    Hybrid Joint-Separable Multibody Tracking,
    vol.1,
    2005
    , pp. 413-
    420
    , (IEEE Conference on Computer Vision and Pattern Recognition - CVPR,
    San Diego (CA),
    from 06/20/2005 to 06/26/2005)
  4. O. Lanz,
    Probabilistic Multi-Person Tracking for Ambient Intelligence,
    Monitoring human activities with visual sensors is still a challenge, especially when multiple targets are involved. Occlusions, if not properly handled, are a major source of failure. Indoor environments with complex topology require the use of sensor networks whose effective management is by itself a difficult problem. Situated in the context of Ambient Intelligence, this thesis is concerned with both algorithmic and architectural aspects of distributed monitoring systems. The algorithmic approach pursued is that of non–parametric Bayesian filtering, a probabilistic state estimation framework whose multi target formulation allows physically–based modeling of the occlusion process within an appearance based, background independent observation model. While unaffordable in its plain, joint formulation, a novel, efficient, probabilistically sound solution is proposed and its robustness experimentally verified. Its MonteCarlo implementation provides a suitable framework for tackling dynamic resource allocation within a distributed modular architecture. Representation size of probabilistic estimates is adapted to conveyed uncertainty, while active environment sampling aims at minimizing uncertainty. Independent sensor agencies are synergically reconfigured by a supervisor process with global environment knowledge. The proposed system is then adaptive to its own performance, environment and sensing and computing infrastructure, providing a scalable solution to the problem of visual monitoring of crowded, topologically complex environments,
    2005
  5. O. Lanz,
    A Joint-Separable Filter for Multitarget Tracking,
    Tracking multiple targets with visual sensors is a challenging problem, especially when efficiency is an issue. Occlusions, if not properly handled, are a major source of failure. Solutions supporting principled occlusion reasoning have been proposed but are yet unpractical for online applications. This article presents a new solution which effectively manages the trade-off between reliable modeling and computational efficiency. The Hybrid Joint-Separable (HJS) filter is derived from a joint Bayesian formulation of the problem, and shown to be efficient while optimal in terms of efficient belief representation. Computational efficiency is achieved by employing a forward MRF approximation to joint dynamics and an incremental algorithm for posterior update with an appearance likelihood that implements a physically-based model of the occlusion process. Its real time MonteCarlo implementation achieves accurate tracking during partial occlusions, while in case of complete occlusion tracking hypotheses are bound to estimated occlusion volumes. Experiments show that the proposed algorithm is very robust and able to resolve even long term complete occlusions between targets with identical appearance,
    2005
  6. O. Lanz,
    Dynamic Resource Allocation for Probabilistic Tracking in Complex Scenes,
    Monitoring human activities in large environments is a challenging problem. Such scenarios impose the use of sensor networks and decentralized processing to achieve scalability in terms of spatial environment and task complexity. This article addresses both algorithmic and architectural aspects of distributed tracking systems. The algorithmic approach pursued is that of sequential Bayesian filtering, whose standard formulation is rewritten in a form suitable for distribution within a three-layered modular architecture. Its non-parametric MonteCarlo implementation provides a suitable framework for tackling dynamic resource allocation: representation size of probabilistic estimates is adapted to conveyed uncertainty, while active environment sampling aims at minimizing uncertainty. Independent tracking agencies with competence on a subset of targets are instantiated and continuously reconfigured by a supervisor process with global environment knowledge. The proposed system is then adaptive to its sensing and computing infrastructure, own performance and environment, providing a scalable solution to the problem of visual monitoring of populated, topologically complex environments,
    2005
  7. Paolo Lombardi,
    Image-Based Road Segmentation and Obstacle Detection in the DIPLODOC Project,
    This report describes the activity carried out as contract researcher at ITC-irst, Sensory System Division, from February 2004 to January 2005 in the scope of the DIPLODOC project (DIstributed Processing of Local Data for On-line Car services). DIPLODOC is a three years project funded by the Provincia Autonoma di Trento that started on April, 2002. Three research partners are involved: ITC-irst, CRF (Centro Ricerche Fiat), and the University of Trento,
    2005
  8. C. Andreatta,
    Visual Mouse II: museum paintings recognition,
    This paper presents a prototype of a visual recognition system for a handheld interactive museum guide. Contextualized information about museum drawings may be obtained by the user, without any knowledge about how the system works by simply pointing a palmtop camera towards the piece and taking a shot. The system was tested and performance was found to be satisfactory in challenging environment conditions,
    2005
  9. M. Lecca,
    MEMORI - Version 1.0,
    This report contains a detailed description of the latest version of MEMORI, a system for the automatic detection and recognition of the objects of a database within digital color images. A comparison with the previous version is also presented,
    2005
  10. M. Lecca,
    A new method for the automatic estimation of the heuristic rule parameters for MEMORI 1.0,
    2005

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