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2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Lazaros Nalpantidis Mårten Björkman Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology (KTH) Stockholm Sweden

We address the problem of object segmentation in image sequences where no a-priori knowledge of objects is assumed. We take advantage of robots' ability to move, gathering multiple images of the scene. Our approach starts by extracting edges, uses a polar domain representation and performs integration over time based on a simple dilation operation. The proposed system can be used for providing reliable initial segmentation of unknown objects in scenes of varying complexity, allowing for recognition, categorization or physical interaction with the objects. The experimental evaluation on both self-captured and a publicly available dataset shows the efficiency and stability of the proposed method.

关键词： Image edge detection Image segmentation Object segmentation Correlation Optical imaging Robots Detectors

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ICE matching, a novel approach for localization problem

ICE matching, a novel approach for localization problem

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Maziar A. Sharbafi Sanaz Taleghani Edriss Esmaeili Abolfazl T. Haghighat Omid Aghazadeh Department of Electrical and Computer Engineering Azad University of Qazvin Qazvin Iran Computer Vision and Active Perception(CVAP) laboratory KTH Stockholm Sweden

This paper presents a novel technique for scan matching. The method is based on the family of feature to feature matching approaches. Our innovative method named ICE matching leads to a fast and accurate solution to solve the challenges of localization problem. Novelty in defining new features, matching mechanism and new state estimation approach, congregated in this method, creates a robust practical technique in terms of accuracy and convergence rate. Furthermore, The Comparison with some high quality scan matching methods from different viewpoints illustrates the performance of ICE matching.

关键词： Feature extraction Ice Robustness Simultaneous localization and mapping Mobile robots

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Topology, Big Data and Optimization

Studies in Big Data

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Studies in Big Data 2016年 18卷 147-176页

作者： Vejdemo-Johansson, Mikael Skraba, Primoz Computer Vision and Active Perception Laboratory KTH Royal Institute of Technology Stockholm100 44 Sweden AI Laboratory Jozef Stefan Institute Jamova 39 Ljubljana Slovenia

The idea of using geometry in learning and inference has a long history going back to canonical ideas such as Fisher information, Discriminant analysis, and Principal component analysis. The related area of Topological Data Analysis (TDA) has been developing in the last decade. The idea is to extract robust topological features from data and use these summaries for modeling the data. A topological summary generates a coordinate-free, deformation invariant and highly compressed description of the geometry of an arbitrary data set. Topological techniques are well-suited to extend our understanding of Big Data. These tools do not supplant existing techniques, but rather provide a complementary viewpoint to existing techniques. The qualitative nature of topological features do not give particular importance to individual samples, and the coordinate-free nature of topology generates algorithms and viewpoints well suited to highly complex datasets. With the introduction of persistence and other geometric-topological ideas we can find and quantify local-to-global properties as well as quantifying qualitative changes in data. © Springer International Publishing Switzerland 2016.

关键词： Big data

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Layered HMM for Motion Intention Recognition

Layered HMM for Motion Intention Recognition

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2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Daniel Aarno Danica Kragic School of Computer Science and Communication Centre for Autonomous Systems-Computational Vision and Active Perception Laboratory Royal Institute of Technology Sweden

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers

关键词： Hidden Markov models Intelligent robots Man machine systems Collaboration Learning Human robot interaction Robotics and automation Speech recognition Character recognition computer vision

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Task-based grasp adaptation on a humanoid robot

Task-based grasp adaptation on a humanoid robot

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10th IFAC Symposium on Robot Control, SYROCO 2012

作者： Bohg, Jeannette Welke, Kai León, Beatriz Do, Martin Song, Dan Wohlkinger, Walter Madry, Marianna Aldóma, Aitor Przybylski, Markus Asfour, Tamim Martí, Higinio Kragic, Danica Morales, Antonio Vincze, Markus Humanoids and Intelligence Systems Lab. Institute for Anthropomatics KIT Germany Autonomous Motion Lab. MPI for Intelligent Systems Germany Robotic Intelligence Laboratory Department of Computer Science and Engineering UJI Spain Computer Vision and Active Perception Lab. KTH Sweden Automation and Control Institute TUW Austria

ISBN: (纸本)9783902823113

In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effectiveness of the approach is demonstrated on the humanoid robot ARMAR-IIIa. © 2012 IFAC.

关键词： Anthropomorphic robots

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Modeling Image Context Using Object Centered Grid

Modeling Image Context Using Object Centered Grid

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Sobhan Naderi Parizi Ivan Laptev Alireza Tavakoli Targhi Computer Vision and Active Perception Laboratory Royal Institute of Technology (KTH) Stockholm Sweden INRIA / École Normale Superieure Paris France

Context plays a valuable role in any image understanding task confirmed by numerous studies which have shown the importance of contextual information in computer vision tasks, like object detection, scene classification and image retrieval. Studies of human perception on the tasks of scene classification and visual search have shown that human visual system makes extensive use of contextual information as postprocessing in order to index objects. Several recent computer vision approaches use contextual information to improve object recognition performance. They mainly use global information of the whole image by dividing the image into several predefined subregions, so called fixed grid. In this paper we propose an alternative approach to retrieval of contextual information, by customizing the location of the grid based on salient objects in the image. We claim this approach to result in more informative contextual features compared to the fixed grid based strategy. To compare our results with the most relevant and recent papers, we use PASCAL 2007 data set. Our experimental results show an improvement in terms of Mean Average Precision.

关键词： Context modeling Layout computer vision Object detection Humans Detectors Information retrieval Image retrieval Histograms Digital images

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Recognizing human actions: a local SVM approach

Recognizing human actions: a local SVM approach

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International Conference on Pattern Recognition

作者： C. Schuldt I. Laptev B. Caputo Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.

关键词： Humans Support vector machines computer vision Pattern recognition Support vector machine classification Cameras Frequency Spatial databases Performance evaluation Image recognition

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Velocity adaptation of space-time interest points

Velocity adaptation of space-time interest points

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International Conference on Pattern Recognition

作者： I. Laptev T. Lindeberg Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computer Science KTH Royal Institute of Technology Stockholm Sweden

The notion of local features in space-time has recently been proposed to capture and describe local events in video. When computing space-time descriptors, however, the result may strongly depend on the relative motion between the object and the camera. To compensate for this variation, we present a method that automatically adapts the features to the local velocity of the image pattern and, hence, results in a video representation that is stable with respect to different amounts of camera motion. Experimentally we show that the use of velocity adaptation substantially increases the repeatability of interest points as well as the stability of their associated descriptors. Moreover, for an application to human action recognition we demonstrate how velocity-adapted features enable recognition of human actions in situations with unknown camera motion and complex, non-stationary backgrounds.

关键词： Cameras Humans Image motion analysis Pattern recognition Motion measurement Optical recording Covariance matrix computer vision Laboratories Numerical analysis

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Galilean-diagonalized spatio-temporal interest operators

Galilean-diagonalized spatio-temporal interest operators

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International Conference on Pattern Recognition

作者： T. Lindeberg A. Akbarzadeh I. Laptev Computational Vision and Active Perception Laboratory (CVAP) Department of Numerical Analysis and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents a set of image operators for detecting regions in space-time where interesting events occur. To define such regions of interest, we compute a spatio-temporal second-moment matrix from a spatio-temporal scale-space representation, and diagonalize this matrix locally, using a local Galilean transformation in space-time, optionally combined with a spatial rotation, so as to make the Galilean invariant degrees of freedom explicit. From the Galilean-diagonalized descriptor so obtained, we then formulate different types of space-time interest operators, and illustrate their properties on different types of image sequences.

关键词： Cameras Covariance matrix computer vision Laboratories Numerical analysis computer science Image sequences Layout Councils Filters

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View-based 3D object recognition with support vector machines

View-based 3D object recognition with support vector machine...

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IEEE Workshop on Neural Networks for Signal Processing

作者： D. Roobaert M.M. Van Hulle Computer Vision and Active Perception Laboratory Royal Institute of Technology Stockholm Sweden Laboratory for Neuro-and Psychophysiology K.U. Leuven Leuven Belgium

Support vector machines have demonstrated excellent results in pattern recognition tasks and 3D object recognition. We confirm some of the results in 3D object recognition and compare it to other object recognition systems. We use different pixel-level representations to perform the experiments, while we extend the setting to the more challenging and practical case when only a limited number of views of the object are presented during training. We report high correct classification of unseen views, especially considering that no domain knowledge is including into the proposed system. Finally, we suggest an active learning algorithm to reduce further the required number of training views.

关键词： Object recognition Support vector machines Laboratories Pattern recognition Humans Neural networks Automatic control Control systems Training data computer vision

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