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Wiki Page: Accurate AR Marker Location Title: C++ Library for accurate marker location based on subsequent pnp refinements
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Tutor: MatteoMatteucci |
Wiki Page: Aperiodic visual stimulation in a VEP-based BCI Title: Aperiodic visual stimulation in a VEP-based BCI
Tutor: MatteoMatteucci |
Wiki Page: Automatic Differentiation Techniques for Real Time Kalman Filtering Title: Evaluation of Automatic Differentiation Techniques for Gauss-Newton based Simultaneous Localization and Mapping Material
Expected outcome: New modules implementations based on automatic differentiation A comparison between the old stuff and new approach Required skills or skills to be acquired:
Tutor: MatteoMatteucci |
Wiki Page: Automatic generation of domain ontologies Title: Automatic generation of domain ontologies |
Wiki Page: Behavior recognition from visual data Title: Behavior recognition from visual data
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Tutor: MatteoMatteucci, AndreaBonarini |
Wiki Page: Combinatorial optimization based on stochastic relaxation Title: Combinatorial optimization based on stochastic relaxation The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers (4). The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum. Picture taken from http://www.ra.cs.uni-tuebingen.de/ Bibliography
Tutor: MatteoMatteucci, LuigiMalago |
Wiki Page: Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization Title: Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers (4). The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Computer vision provides a large number of optimization problems, such as new-view synthesis, image segmentation, panorama stitching and texture restoration, among the others, (6). One common approach in this context is based on the use of binary Markov Random Fields and on the formalization of the optimization problem as the minimum of an energy function expressed as a square-free polynomial, (5). We are interested in the proposal, comparison and evaluation of different Estimation of Distribution Algorithms for solving real world problems that appear in computer vision. Pictures taken from http://www.genetic-programming.org and (6) Bibliography
Tutor: MatteoMatteucci, LuigiMalago |
Wiki Page: Comparison of State of the Art Visual Odometry Systems Title: A Comparison of State of the Art Visual Odometry Systems (Monocular and Stereo) Material
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Tutor: MatteoMatteucci |
Wiki Page: Creation of new EEG training by introduction of noise Title: Creation of new EEG training by introduction of noise
Tutor: MatteoMatteucci |
Wiki Page: Driving an autonomous wheelchair with a P300-based BCI Title: Driving an autonomous wheelchair with a P300-based BCI |
Wiki Page: Environment Monitoring Title: Environment Monitoring
Tutor: MatteoMatteucci, DavideMigliore |
Wiki Page: Exploratory data analysis by genetic feature extraction Title: Exploratory data analysis by genetic feature extraction
Tutor: MatteoMatteucci |
Wiki Page: Extended Kalman Filtering on Manifolds Title: Extended Kalman Filtering on Manifolds
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Tutor: MatteoMatteucci, SimoneCeriani, DavideCucci |
Wiki Page: Information geometry and machine learning Title: Information geometry and machine learning Information Geometry has been recently applied in different fields, both to provide a geometrical interpretation of existing algorithms, and more recently, in some contexts, to propose new techniques to generalize or improve existing approaches. Once the student is familiar with the theory of Information Geometry, the aim of the project is to apply these notions to existing machine learning algorithms. Possible ideas are the study of a particular model from the point of view of Information Geometry, for example as Hidden Markov Models, Dynamic Bayesian Networks, or Gaussian Processes, to understand if Information Geometry can give useful insights with such models. Other possible direction of research include the use of notions and ideas from Information Geometry, such as the mixed parametrization based on natural and expectation parameters (3) and/or families of divergence functions (2), in order to study model selection from a geometric perspective. For example by exploiting projections and other geometric quantities with "statistical meaning" in a statistical manifold in order to chose/build the model to use for inference purposes. Since the project has a theoretical flavor, mathematical inclined students are encouraged to apply. The project requires some extra effort in order to build and consolidate some background in math, partially in differential geometry, and especially in probability and statistics. Bibliography
Tutor: MatteoMatteucci, LuigiMalago |
Wiki Page: LARS and LASSO in non Euclidean Spaces Title: LARS and LASSO in non Euclidean Spaces One of the common hypothesis in regression analysis is that the noise introduced in order to model the linear relationship between regressors and dependent variable has a Gaussian distribution. A generalization of this hypothesis leads to a more general framework, where the geometry of the regression task is no more Euclidean. In this context different estimation criteria, such as maximum likelihood estimation and other canonical divergence functions do not coincide anymore. The target of the project is to compare the different solutions associated to different criteria, for example in terms of robustness, and propose generalization of LASSO and LARS in non Euclidean contexts. The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the project has also a theoretical flavor, mathematical inclined students are encouraged to apply. The project may require some extra effort in order to build and consolidate some background in math, especially in probability and statistics. Picture taken from (2) Bibliography
Tutor: MatteoMatteucci, LuigiMalago |
Wiki Page: MoonSLAM Reengineering Title: Reengineering of a flexible framework for simultaneous localization and mapping Material
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Tutor: MatteoMatteucci |
Wiki Page: Multimodal GUI for driving an autonomous wheelchair Title: Multimodal GUI for driving an autonomous wheelchair
Tutor: MatteoMatteucci, SimoneCeriani, DavideMigliore |
Wiki Page: Odometric system for robots based on laser mice Title: Odometric system for robots based on laser mice The aim of the project is first to improve the current design of the PIC-based board, and realize a new working prototype, and then to implement and evaluate different algorithms able to estimate more precisely the x,y and theta odometric data from the mice readings. Experience with PIC-based systems and some experience with electronics circuits is a plus. Students are supposed to redesign the electronic board, improve the firmware of the PIC, and work on the algorithm that estimates the robot position on the PC. It would be also interesting to evaluate the possibility to embed the optimization and estimation algorithms in the firmware of the PIC in order to produce a stand-alone device. Ask the tutors of the project for extra material, such as data-sheets and other documentation. |
Wiki Page: P300 BCI Title: P300 BCI for ALS patient |
Wiki Page: Poit cloud SLAM with Microsoft Kinect Title: Point cloud SLAM with Microsoft Kinect Material:
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Tutor: MatteoMatteucci |