Research at the learning and vision mobile robotics group 2004-2005

Spanish Congress on Informatics (CEDI), 2005, Granada (España)This article presents the current trends on wheeled mobile robotics being pursued at the Learning and Vision Mobile Robotics Group (IRI). It includes an overview of recent results produced in our group in a wide range of areas, including robot localization, color invariance, segmentation, tracking, audio processing and object learning and recognition.This work was supported by projects: 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063), 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe

Clique-to-clique distance computation using a specific architecture

Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (SSPR&SPR), 1998, Sydney (Australia)In this paper, we present a new fast architecture to compute the distance between cliques in different graphs. The distance obtained is used as a support function for graph labelling using probabilistic relaxation techniques. The architecture presented consists on a pipe-lined structure which computes the distance between an input clique and k reference cliques. The number of processing elements needed is m2, and the number of cycles required to compute the distance is ni (being m the number of external nodes in the input clique, and ni the number of external nodes in the i-th reference clique). The processing elements are very simple basic cells and very simple communication between them is needed, which makes it suitable for VLSI implementation.Peer Reviewe

Low cost architecture for structure measure distance computation

Huge and expensive computation resources are usually required to perform graph labelling at high speed. This fact restricts an extensive use of this methodology in industrial applications such as visual inspection. A new systolic architecture is presented which computes structural distances between cliques of different graphs based on a modified incremental Levenshtein distance algorithm. The distances obtained are used as a support function for graph labelling using probabilistic relaxation techniques. The proposed architecture computes the distances between k input cliques of an input graph and one reference clique of a reference graph. It does not limit the number of cliques nor cliques complexity of the input graph, so any input graph can be labelled. A low cost solution has been implemented based on FPGAs.Peer Reviewe

Colour image segmentation solving hard-constraints on graph partitioning greedy algorithms

International Conference on Pattern Recognition (ICPR), 2000, Barcelona (España)A graph partitioning greedy algorithm is presented. This algorithm avoids the hard-constraints of others similar approaches such as the impossibility for some regions to grow after certain step of the algorithm and the uniqueness of the solution. Nevertheless, it allows attaining global results by local approximations using a generalised concept of not over-segmentation, which includes an energy function, and eliminating the not sub-segmentation criterion using a probabilistic criterion similar to that of annealing. The high-variability region problems such as borders are also eliminated identifying them and distributing their pixels among the other neighbour regions. Thus, it is possible to keep the time complexity of usual graph partitioning greedy algorithm and avoiding its high variability region problems, obtaining better results.This work was supported by the project 'Active vision systems based in automatic learning for industrial applications' ().Peer Reviewe

Graph-based representations and techniques for image processing and image analysis

In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robòtica i Informàtica Industrial within the project “Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)”. Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated function-described graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.This work was supported by the project 'Active vision systems based in automatic learning for industrial applications'. This work has been partially granted by the Ministerio de Educación y Cultura TAP1998-0473.Peer Reviewe

Pattern recognition research at the IRI-CSIC/ESAII group

Foro Iberoamericano de Reconocimiento de Formas y Análisis de Imágenes, 2000, Barcelona (España)The pattern recognition group formed by researchers in the IRI-CSIC and the ESAII Dept at the UPC has been created four years ago but its activity is very high participating in research projects, international publishing and orginizing relevant events such as ICPR'00. In this paper we show part of this activity.This work was supported by the project 'Active vision systems based in automatic learning for industrial applications' ().Peer Reviewe

MARCO: a mobile robot with learning capabilities to perceive and interact with its environment

Spanish Symposium on Pattern Recognition and Image Analysis (SNRFAI), 2001, Benicassim (España)Marco is the name of a research mobile robot that is being developed at the Instituto de Robótica e Infomática Industrial of UPC-CSIC. It is designed with learning abilities to acquire information about indoor environments using various perception sensors. Marco uses video cameras and ultrasonic sensors to perceive the world, and pattern recognition and computer vision techniques to extract knowledge about it. In this paper we explain the objectives of this project and the techniques developed so far.This work was supported by the project 'Active vision systems based in automatic learning for industrial applications' ().Peer Reviewe

Research at the learning and vision mobile robotics group

Jornada de Recerca en Automàtica, Visió i Robòtica (AVR), 2004, Barcelona (España)This article presents the current trends on wheeled mobile robotics being pursued at ESAII-IRI UPC/CSIC. It includes an overview of recent results produced in our group in a wide range of areas, including robot localization, color invariance, segmentation, tracking, visual servoing, audio processing and object and face recognition.This work was supported by the project 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063).Peer Reviewe