This is the list of publications of this laboratory
2013
Journal Articles
Lopez-Juarez, Ismael; Castelan, Mario; Castro-Martînez, Francisco Javier; Peña-Cabrera, Mario; Osorio-Comparan, Roman
Using Object’s Contour, Form and Depth to Embed Recognition Capability into Industrial Robots Journal Article
In: Journal of Applied Research and Technology, vol. 11, no. 1, pp. 5 - 17, 2013, ISSN: 1665-6423.
@article{LopezJuarez20135,
title = {Using Object’s Contour, Form and Depth to Embed Recognition Capability into Industrial Robots},
author = {Lopez-Juarez, Ismael and Castelan, Mario and Castro-Mart\^{i}nez, Francisco Javier and Pe\~{n}a-Cabrera, Mario and Osorio-Comparan, Roman},
url = {http://www.sciencedirect.com/science/article/pii/S1665642313715116},
doi = {http://dx.doi.org/10.1016/S1665-6423(13)71511-6},
issn = {1665-6423},
year = {2013},
date = {2013-01-01},
journal = {Journal of Applied Research and Technology},
volume = {11},
number = {1},
pages = {5 - 17},
abstract = {Abstract Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location. Some manufacturers offer 3D guidance systems using robust vision and laser systems so that a 3D programmed point can be repeated even if the part is moved varying its location, rotation and orientation within the working space. Despite these developments, current industrial robots are still unable to recognize objects in a robust manner; that is, to distinguish an object among equally shaped objects taking into account not only the object’s contour but also its form and depth information, which is precisely the major contribution of this research. Our hypothesis establishes that it is possible to integrate a robust invariant object recognition capability into industrial robots by using image features from the object’s contour (boundary object information), its form (i.e., type of curvature or topographical surface information) and depth information (from stereo disparity maps). These features can be concatenated in order to form an invariant vector descriptor which is the input to an artificial neural network (ANN) for learning and recognition purposes. In this paper we present the recognition results under different working conditions using a KUKA KR16 industrial robot, which validated our approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location. Some manufacturers offer 3D guidance systems using robust vision and laser systems so that a 3D programmed point can be repeated even if the part is moved varying its location, rotation and orientation within the working space. Despite these developments, current industrial robots are still unable to recognize objects in a robust manner; that is, to distinguish an object among equally shaped objects taking into account not only the object’s contour but also its form and depth information, which is precisely the major contribution of this research. Our hypothesis establishes that it is possible to integrate a robust invariant object recognition capability into industrial robots by using image features from the object’s contour (boundary object information), its form (i.e., type of curvature or topographical surface information) and depth information (from stereo disparity maps). These features can be concatenated in order to form an invariant vector descriptor which is the input to an artificial neural network (ANN) for learning and recognition purposes. In this paper we present the recognition results under different working conditions using a KUKA KR16 industrial robot, which validated our approach.
2012
Journal Articles
Martinez-Gonzalez, Pablo; Castelan, Mario
Incorporating Angular Ratio Images into Two-Frame Stereo Algorithms Journal Article
In: Computación y Sistemas, vol. 16, no. 1, pp. 53-69, 2012, ISSN: 2007-9737.
@article{Martinez2012,
title = {Incorporating Angular Ratio Images into Two-Frame Stereo Algorithms},
author = {Martinez-Gonzalez, Pablo and Castelan, Mario },
url = {http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/1372},
issn = {2007-9737},
year = {2012},
date = {2012-01-02},
journal = {Computaci\'{o}n y Sistemas},
volume = {16},
number = {1},
pages = {53-69},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
