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Flavio Prieto

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

ICRA Conference 2009 Conference Paper

Mouth gesture and voice command based robot command interface

  • Juan-Bernardo Gómez
  • Alexánder Ceballos
  • Flavio Prieto
  • Tanneguy Redarce

In this paper we present a voice command and mouth gesture based robot command interface which is capable of controlling three degrees of freedom. The gesture set was designed in order to avoid head rotation and translation, and thus relying solely in mouth movements. Mouth segmentation is performed by using the normalized a* component, as in J. Gomez, et al. , (October 2008). The gesture detection process is carried out by a Gaussian mixture model (GMM) based classifier. After that, a state machine stabilizes the system response by restricting the number of possible movements depending on the initial state. Voice commands are modeled using a hidden Markov model (HMM) isolated word recognition scheme. The interface was designed taking into account the specific pose restrictions found in the DaVinci assisted surgery command console.

ICRA Conference 2002 Conference Paper

Automated Inspection System using Range Data

  • Flavio Prieto
  • Pierre Boulanger
  • Richard Lepage
  • Tanneguy Redarce

We propose an automated inspection system of manufactured parts using a cloud of 3D measured points of a part provided by a range sensor, and its CAD model. Inspection consists in verifying the accuracy of a part related to a given set of tolerances. It is thus necessary that the 3D measurements be accurate. In the 3D capture of a part, several sources of error can alter the measured values. So, we have to find and model the most influential parameters affecting the accuracy of the range sensor in the digitalization process. This model is used to produce a sensing plan to acquire accurately the geometry of a part. By using the noise model, we introduce a dispersion value for each 3D point acquired. This value of dispersion is shown as a weight factor in the inspection results.

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