A REAL-TIME ROBOT VISION APPROACH COMBINING VISUAL SALIENCY AND UNSUPERVISED LEARNING
2011; Linguagem: Inglês
10.1142/9789814374286_0028
ISSN2771-3989
AutoresDominik M. Ramík, Christophe Sabourin, Kurosh Madani,
Tópico(s)Robotics and Sensor-Based Localization
ResumoField Robotics, pp. 241-248 (2011) No AccessA REAL-TIME ROBOT VISION APPROACH COMBINING VISUAL SALIENCY AND UNSUPERVISED LEARNINGDOMINIK MAXIMILIÁN RAMÍK, CHRISTOPHE SABOURIN, and KUROSH MADANIDOMINIK MAXIMILIÁN RAMÍKSignals, Images, and Intelligent Systems Laboratory (LISSI / EA 3956), Paris Est University, Senart Institute of Technology, Avenue Pierre Point, 77127 Lieusaint, France, CHRISTOPHE SABOURINSignals, Images, and Intelligent Systems Laboratory (LISSI / EA 3956), Paris Est University, Senart Institute of Technology, Avenue Pierre Point, 77127 Lieusaint, France, and KUROSH MADANISignals, Images, and Intelligent Systems Laboratory (LISSI / EA 3956), Paris Est University, Senart Institute of Technology, Avenue Pierre Point, 77127 Lieusaint, Francehttps://doi.org/10.1142/9789814374286_0028Cited by:6 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Facing the complexity of the real world, no robot can rely solely on pre-programmed knowledge and human-prepared data. If we are to create a truly autonomous robot, learning on-the-fly in the real environment is a must. The aim of this paper is to present a novel hybrid approach to unsupervised real-time learning of objects in context of mobile robotics. We develop on techniques inspired by human visual system and by research on how human infants learn. We combine our contribution on salient object detection with state-of-the-art object recognition algorithms in order to acquire both fast learning and recognition capabilities for a humanoid robot. To test it, verification on the MSRA Salient Object Database benchmark is carried out as well as several experiments with learning generic objects in a real office environment using humanoid robot Nao. FiguresReferencesRelatedDetailsCited By 6Artificial Curiosity Emerging Human-Like Behavior: Toward Fully Autonomous Cognitive RobotsKurosh Madani, Christophe Sabourin and Dominik M. Ramík20 November 2015At Odds with Curious Cats, Curious Robots Acquire Human-Like IntelligenceDominik M. Ramík, Kurosh Madani and Christophe Sabourin1 Jan 2014From visual patterns to semantic description: A cognitive approach using artificial curiosity as the foundationDominik Maximilián Ramík, Kurosh Madani and Christophe Sabourin1 Oct 2013 | Pattern Recognition Letters, Vol. 34, No. 14A Cognitive Approach for Robots' Autonomous LearningDominik M. Ramík, Kurosh Madani and Christophe Sabourin1 Jan 2013Spherical coordinates framed RGB color space dichromatic reflection model based image segmentation: Application to wildland fires' outlines extractionV. Amarger, D. M. Ramik, C. Sabourin, K. Madani and R. Moreno et al.1 Oct 2012Hybrid Salient Object Extraction Approach with Automatic Estimation of Visual Attention ScaleDominik Maximili´n Ramik, Christophe Sabourin and Kurosh Madani1 Nov 2011 Field RoboticsMetrics History PDF download
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