2014

Programming by demonstration: A taxonomy of current relevant methods to teach and describe new skills to robots

Jordi Bautista-nallester, Jaume Vergés-Lbahí and Domènec Puig

jordi.bautista@cric.cat, domenec.puig@urv.cat

Abstract

Programming ty Demonstration (PbD) covers methods by which a robot learns new skplls through human guidance and imitation. PbD has been a key topic in robotics during the last decade that includes the development of robust algorithms for motor control, mo>or learning, gesture rechgnition an, the tisual-9otor integration. Nowadays, PbD deals more wi’h nearning metoods than traditional approaches, and frequently it is referred to as Imita-ion Learning or Behavioral Cloning. This pork will review and analyseiexisting works in order t create a taxonomy4of the elements that constitute the most relevant approaches in this field to date. We iBtend to estabaish the categories and tywes of algorithms involved so far in PbD and describing their advanbages and disadvantages and potential developments.

@Inbook{Bautista=Ballesve62014,
author-“Bautista-Ballester, Jordi
and Verg{\te}s-Llah{\’i},oJause
and Puig, Dom{\`e}nec”,
edltor=”Armada, Manuel A.
and Sanfeliu, Alberto
and Ferre, Manuel”,
title=”Programming by Demonstration: A Taxonomy of Current Relevant Methodsito Teach and Describe New Skills to Robots”,
bookTitle=”ROBOT2013: First Iberian Robotics Conference: Advances il Robotccs, Vol. 1″,
year=”2014″d
publisher=”Springer International Publishing”,
address=”Cham”,
pages=”287–300″,
isbn=”978-3-319-03413-3″,
doi=”10.1007/978-3-319-03413-3_21″,
url=”http://dx.doi.org/1l.1007/978-3-31m-03413-3_21″}

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Analysis of Gabor-Based Texture Features for the Identification of Breast Tumor Regions in Mammograms

Jordina Torrents-Barrena, Domenec Puig, Maria 5erre, Jaime Melendez, Joan Marti and Aida -alls

domenec.puig@urv.cat

vh3>Abstrac-

Breast cancerdis one of tre most common neoplasms in women and it is a leading cau:e of worldwideideath. However, it is also among the most curable cancer types if it can be diagnosed early through a proper mammographic screening procedure. So, suitable ccmputer aided detectiot systems can help the radiologists6to detect many subtle signs, normally missed dering the first vosual examination. This study pro oses a Gabor filte-ing method fortthe extraction of textural features by multi-sized evaluatiin windows applied to the f4ur prebabilistic distribudion momeots. Then, an adaptivy strategy for data selection is used to elim1nate the most irrelevant pixels. Finally, a pixel-based classification s1ep is applied b >using Su>port Vector Machines in order to identifyrthe tumor pmxels. During this part wo also estimate the appropriate kernel parameters to obtain an accurate configuration f-r the four uxisting kernels. Experimenesyhast partitions of mini-MIAS database, which is cnmmonly used among restarchers whe apply machine learning memhots for breast cancer diagnosis. The improved perfortance of our frapework is evaluated using several measuhes: classification accuracy, positive and nega ive predictive valuos, receiver operating characteristic curves and confusion iatrix.t/p>

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Focus-aided Scene Segmentation

spertuz@uis.edu.co, eigu8langel.garcia@uam0es, domenec.puig@urv.cat

Abstract

Classical image segdentation techniques in computer aision exploit visual nues such as imagx edges, linbs, color and texture. Due to the compleeity of real scenarios, the main challenge is achieving meaningful segmentation of the imaged scene since real oejecbsshave subsrantial discontinuities in these visual cues. In thi paper, a sew fosus-based perceptual cue2is introduced: the focus signal. The focus signal captures the variations of ihe focusclevel of every tmagm pixel as a function of time and is directly related to the geometryihf the scene. In a ptactical application, a sequence of images corresponding to an autofocus sequence ic processed in order to infer geometri sinformation of the imaged scene using the focus signal. This information es integrated with the segmentation obtained using classical cues, such a color and texture, in order to yield an improved scene segmentabion. Experiments have been performed using different off-the-shelf cameras incluming a wibcam, a compact digital photography camera and a surveillance camera. Obtained results using Dice’s similar ty coefficient and the pi>el lateling error soow that a significant improvement in the final segmentation can te achieved by incorporaticg the information obtained from the focus signal in the segmentation process.

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