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IEEE International Conference on Image Processing

作者： Jaime Melendez Domenec Puig Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain Department of Informatics Engineering Autonomous University of Madrid Spain

This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.

关键词： Prototypes Gabor filters Pixel Feature extraction Filter bank Testing intelligent robots Image texture analysis Pattern recognition Image recognition

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A new methodology for evaluation of edge detectors

A new methodology for evaluation of edge detectors

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IEEE International Conference on Image Processing

作者： Rodrigo Moreno Domenec Puig Carme Julià Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

This paper defines a new methodology for evaluating edge detectors through measurements on edginess maps instead of on binary edge maps as previous methodologies do. These measurements avoid possible bias introduced by the application-dependent process of generating binary edge maps from edginess maps. The features of completeness, discriminability, precision and robustness, which a general-purpose edge detector must comply with, are introduced. The R, DS, P and FAR-measurements in addition to PSNR applied to the edginess maps are defined to assess the performance of edge detection. The R, DS, P and FAR-measurements can be seen as generalizations of previously proposed measurements on binary edge maps. Well-known and state-of-the-art edge detectors have been compared by means of the new proposed metrics. Results show that it is difficult for an edge detector to comply with all the proposed features.

关键词： Image edge detection Detectors intelligent robots Robustness Robot vision systems computer vision computer science mathematics Informatics Tin

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Robust color edge detection through tensor voting

Robust color edge detection through tensor voting

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IEEE International Conference on Image Processing

作者： Rodrigo Moreno Miguel Angel Garcia Domenec Puig Carme Julià Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

This paper presents a new method for color edge detection based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information via tensors in order to propagate them into a neighborhood through a voting process specifically designed for color edge detection by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorithm is more robust and has a similar performance in precision when compared with the state-of-the-art.

关键词： Robustness Image edge detection Tensile stress Voting Colored noise Color Detectors Eigenvalues and eigenfunctions intelligent robots computer vision

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***: Transforming Colour into Melody and Implementing the Result in a Colour Sensor Device

***: Transforming Colour into Melody and Implementing the Re...

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International Conference on Visualisation, VIS

作者： Jessica Rossi Francisco Jose Perales Javier Varona Miquel Roca Computer Graphics Vision and Artificial Intelligence Group Mathematics and Computers Science Department University of Balearic Islands Spain

This paper represents a system which explores the reality of colour in relation to sound and dedicated firstly to blind people, the challenges they live with, their psychology and their emotions. Itpsilas an exploration of the infinite range of colours that they have either never been able to see or that they have lost the possibility to see. The system developed is based on the concept of synesthesia: a dasialiaisonpsila between colour and sound, colour that becomes sound. This paper synthesizes a wider range of study regarding people with diverse psychological and movement conditions. It represents a new method of expression and of self development through music therapy and game, dedicated mainly to children aged between 5 and 13. Finally we conclude with a portable prototype device that has been validated and patented. It is capable of detecting colour and transforming it into sound: a colour sensor identifies colours and transforms this data into musical notes. The prototype is totally adaptable to the requirements of the end user meaning that the user can choose the best combination between colour and sound in order to improve their communication with the environment. We are now developing a mathematical theoretical background in order to formalize the correspondence between colour and musical space.

关键词： Psychology Acoustic sensors Prototypes Cultural differences Auditory system vision defects Visualization intelligent sensors Sensor systems computer graphics

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Monocular pedestrian detection: Survey and experiments

Monocular pedestrian detection: Survey and experiments

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作者： Enzweiler, Markus Gavrila, Dariu M. Department of Mathematics and Computer Science Image and Pattern Analysis Group University of Heidelberg Speyerer St. 4 69115 Heidelberg Germany Environment Perception Department Assistance Systems and Chassis Daimler AG Group Research Wilhelm Runge St. 11 89081 Ulm Germany Intelligent Systems Lab Faculty of Science University of Amsterdam Kruislaan 403 1098 SJ Amsterdam Netherlands

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade [74], HOG/ linSVM [11], NN/LRF [75], and combined shape-texture detection [23]. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/ linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes. © 2009 IEEE.

关键词： Benchmarking

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Mining discriminative adjectives and prepositions for natural scene recognition

Mining discriminative adjectives and prepositions for natura...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Bangpeng Yao Juan Carlos Niebles Li Fei-Fei Department of Computer Science Princeton University NJ USA Robotics and Intelligent Systems Group Universidad del Norte Colombia Department of Electrical Engineering Princeton University NJ USA

This paper presents a method that considers not only patch appearances, but also patch relationships in the form of adjectives and prepositions for natural scene recognition. Most of the existing scene categorization approaches only use patch appearances or co-occurrence of patch appearances to determine the scene categories, but the relationships among patches remain ignored. Those relationships are, however, critical for recognition and understanding. For example, a `beach' scene can be characterized by a `sky' region above `sand', and a `water' region between `sky' and `sand'. We believe that exploiting such relations between image regions can improve scene recognition. In our approach, each image is represented as a spatial pyramid, from which we obtain a collection of patch appearances with spatial layout information. We apply a feature mining approach to get discriminative patch combinations. The mined patch combinations can be interpreted as adjectives or prepositions, which are used for scene understanding and recognition. Experimental results on a fifteen class scene dataset show that our approach achieves competitive state-of-the-art recognition accuracy, while providing a rich description of the scene classes in terms of the mined adjectives and prepositions.

关键词： Layout Road transportation Image representation intelligent robots Image recognition Data mining computer science intelligent systems Histograms

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Motion planning for a vigilant humanoid robot

Motion planning for a vigilant humanoid robot

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IEEE-RAS International Conference on Humanoid Robots

作者： Jean-Bernard Hayet Claudia Esteves Gustavo Arechavaleta Eiichi Yoshida Computer Science Group Centro de Investigación en Matemáticas Guanajuato Mexico Department of Mathematics Universidad de Guanajuato Guanajuato Mexico Robotics and Advance Manufacturing Group Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV) Saltillo Coahuila Mexico Intelligent Systems Research Institute National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (纸本)9781424445875

This paper presents a motion planner for a vigilant humanoid robot. In this context of surveillance, the robot task is to keep a distinctive point in the environment in sight during all of its motion. The method we propose consists of three main ingredients: (1) A motion planner for an appropriate simplified model of the walking robot, adapted to the particular needs of humanoid robots, that outputs an admissible path with local optimality properties. This path is guaranteed to satisfy the visibility constraints resulting both from the landmark and from the angular limits of the mechanical system; (2) A generic walking pattern generator that produces stable walking motions; (3) A generalized inverse-kinematics module to satisfy the remaining collisions and posture constraints, in particular the gaze direction. The effectiveness of this method is shown with several examples on the humanoid robot plataform HRP-2.

关键词： Motion planning Humanoid robots Legged locomotion Service robots Surveillance Monitoring Collision avoidance Robot sensing systems Mechanical systems Humans

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The Third International Conference on human-robot interaction

The Third International Conference on human-robot interactio...

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作者： Fong, Terry Dautenhahn, Kerstin Scheutz, Matthias Demiris, Yiannis Intelligent Robotics Group NASA Ames Research Center Department of Artificial Intelligence School of Computer Science University of Herfordshire Herfordshire United Kingdom Adaptive Systems Research Group University of Herfordshire Herfordshire United Kingdom Department of Cognitive Science Computer Science and Informatics Cognitive Science Program Indiana University Bloomington IN United States Intelligent Robotics Team Department of Electrical Engineering Imperial College London London United Kingdom

The third international conference on Human-Robot Interaction (HRI-2008) was held in Amsterdam, The Netherlands, March 12-15, 2008. The theme of HRI-2008, "living with robots," highlights the importance of the technical and social issues underlying human-robot interaction with companion and assistive robots for long-term use in everyday life and work activities. More than 250 researchers, practitioners, and exhibitors attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2009 will be held in San Diego, California, from March 11-13, 2009. Copyright © 2008, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

No abstract available

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Spatial-Temporal correlatons for unsupervised action classification

Spatial-Temporal correlatons for unsupervised action classif...

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Workshop on Motion and Video Computing

作者： Silvio Savarese Andrey DelPozo Juan Carlos Niebles Li Fei-Fei Beckman Institute University of Illinois Urbana-Champaign USA Department of Computer Science University of Illinois Urbana-Champaign USA Robotics and Intelligent Systems Group Universidad del Norte Colombia Department of Computer Science Princeton University USA

Spatial-temporal local motion features have shown promising results in complex human action classification. Most of the previous works [6],[16],[21] treat these spatial- temporal features as a bag of video words, omitting any long range, global information in either the spatial or temporal domain. Other ways of learning temporal signature of motion tend to impose a fixed trajectory of the features or parts of human body returned by tracking algorithms. This leaves little flexibility for the algorithm to learn the optimal temporal pattern describing these motions. In this paper, we propose the usage of spatial-temporal correlograms to encode flexible long range temporal information into the spatial-temporal motion features. This results into a much richer description of human actions. We then apply an unsupervised generative model to learn different classes of human actions from these ST-correlograms. KTH dataset, one of the most challenging and popular human action dataset, is used for experimental evaluation. Our algorithm achieves the highest classification accuracy reported for this dataset under an unsupervised learning scheme.

关键词： Humans Cameras Robustness Video sequences computer science intelligent robots Legged locomotion Feature extraction intelligent systems Trajectory

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