Perception and Action in Robotics

National DPI Projects PAU (2009-2011) and PAU+ (2012-2014)

Object-action recognition and replication

Complete list of publications

Highlighted Publications

Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison.
W. Kazmi, S. Foix, G. Alenyà and H.J. Andersen.
ISPRS Journal of Photogrammetry and Remote Sensing, 88: 128-146, 2014.
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In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo-correspondence algorithms (local correlation and graph cuts).

Lock-in time-of-flight (ToF) cameras: a survey.
S. Foix, G. Alenyà and C. Torras.
IEEE Sensors Journal, 11(9): 1917-1926, 2011.
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This paper reviews the state-of-the art in the field of lock-in ToF cameras, their advantages, their limitations, the existing calibration methods, and the way they are being used, sometimes in combination with other sensors.

Robotized plant probing: Leaf segmentation utilizing time-of-flight data
G. Alenyà, B. Dellen, S. Foix and C. Torras.
IEEE Robotics and Automation Magazine, 20(3): 50-59, 2013.
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Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modeling for this application has strong demands, particularly in what concerns three-dimensional (3-D) information gathering and speed.

Bootstrapping boosted random Ferns for discriminative and efficient object classification.
M. Villamizar, J. Andrade-Cetto, A. Sanfeliu and F. Moreno-Noguer.
Pattern Recognition, 45(9): 3141-3153, 2012.
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In this paper we show that the performance of binary classifiers based on Boosted Random Ferns can be significantly improved by appropriately bootstrapping the training step. This results in a classifier which is both highly discriminant and computationally efficient and is particularly suitable when only small sets of training images are available.