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检索条件"机构=Computer Vision and Active Perception Laboratory"

共 58 条记录，以下是21-30 订阅

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Novelty Detection from an Ego-Centric Perspective

Novelty Detection from an Ego-Centric Perspective

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IEEE Conference on computer vision and Pattern Recognition

作者： Omid Aghazadeh Josephine Sullivan Stefan Carlsson Computer Vision and Active Perception Laboratory KTH

ISBN: (纸本)9781457703942

This paper demonstrates a system for the automatic extraction of novelty in images captured from a small video camera attached to a subject's chest, replicating his visual perspective, while performing activities which are repeated daily. Novelty is detected when a (sub)sequence cannot be registered to previously stored sequences captured while performing the same daily activity. Sequence registration is performed by measuring appearance and geometric similarity of individual frames and exploiting the invariant temporal order of the activity. Experimental results demonstrate that this is a robust way to detect novelties induced by variations in the wearer's ego-motion such as stopping and talking to a person. This is an essentially new and generic way of automatically extracting information of interest to the camera wearer and can be used as input to a system for life logging or memory support.

关键词： Novelty automatic extracting Video cameras visual perspective geometric similarity Speaking optical coherence tomography angiography device system parameter Persons stopping Perspective

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Grasping unknown objects using an Early Cognitive vision system for general scene understanding

Grasping unknown objects using an Early Cognitive Vision sys...

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Mila Popović Gert Kootstra Jimmy Alison Jørgensen Danica Kragic Norbert Krüger Cognitive Vision laboratory The Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark Computer Vision and Active Perception laboratory CSC Royal Institute of Technology (KTH) Stockholm Sweden

Grasping unknown objects based on real-world visual input is a challenging problem. In this paper, we present an Early Cognitive vision system that builds a hierarchical representation based on edge and texture information, which is a sparse but powerful description of the scene. Based on this representation we generate edge-based and surface-based grasps. The results show that the method generates successful grasps, that the edge and surface information are complementary, and that the method can deal with more complex scenes. We furthermore present a benchmark for visual-based grasping.

关键词： Grasping Three dimensional displays Shape Visualization Surface texture Image color analysis Machine vision

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Enhanced visual scene understanding through human-robot dialog

Enhanced visual scene understanding through human-robot dial...

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Matthew Johnson-Roberson Jeannette Bohg Gabriel Skantze Joakim Gustafson Rolf Carlson Babak Rasolzadeh Danica Kragic Centre of Autonomous Systems Computational Vision and Active Perception Laboratory Sweden Department for Speech Music and Hearing Institutes part of School of Computer Science KTH in Stockholm Sweden

We propose a novel human-robot-interaction framework for robust visual scene understanding. Without any a-priori knowledge about the objects, the task of the robot is to correctly enumerate how many of them are in the scene and segment them from the background. Our approach builds on top of state-of-the-art computer vision methods, generating object hypotheses through segmentation. This process is combined with a natural dialog system, thus including a `human in the loop' where, by exploiting the natural conversation of an advanced dialog system, the robot gains knowledge about ambiguous situations. We present an entropy-based system allowing the robot to detect the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

关键词： Robots Entropy Humans Three dimensional displays Histograms Machine vision Image analysis

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Fast and Automatic Detection and Segmentation of unknown objects

Fast and Automatic Detection and Segmentation of unknown obj...

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IEEE-RAS International Conference on Humanoid Robots

作者： Gert Kootstra Niklas Bergström Danica Kragic The Computer Vision and Active Perception Laboratory Center of Autonomous Systems Stockholm Sweden

This paper focuses on the fast and automatic detection and segmentation of unknown objects in unknown environments. Many existing object detection and segmentation methods assume prior knowledge about the object or human interference. However, an autonomous system operating in the real world will often be confronted with previously unseen objects. To solve this problem, we propose a segmentation approach named Automatic Detection And Segmentation (ADAS). For the detection of objects, we use symmetry, one of the Gestalt principles for figure-ground segregation to detect salient objects in a scene. From the initial seed, the object is segmented by iteratively applying graph cuts. We base the segmentation on both 2D and 3D cues: color, depth, and plane information. Instead of using a standard grid-based representation of the image, we use super pixels. Besides being a more natural representation, the use of super pixels greatly improves the processing time of the graph cuts, and provides more noise-robust color and depth information. The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods.

关键词： Pixel Image segmentation Image color analysis Robots Databases Object detection Labeling

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Representations for object grasping and learning from experience

Representations for object grasping and learning from experi...

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Oscar J. Rubio Kai Huebner Danica Kragic Computer Vision and Active Perception Laboratory Centre of Autonomous Systems School of Computer Science and Communication KTH Stockholm Sweden

We study two important problems in the area of robot grasping: i) the methodology and representations for grasp selection on known and unknown objects, and ii) learning from experience for grasping of similar objects. The core part of the paper is the study of different representations necessary for implementing grasping tasks on objects of different complexity. We show how to select a grasp satisfying force-closure, taking into account the parameters of the robot hand and collision-free paths. Our implementation takes also into account efficient computation at different levels of the system regarding representation, description and grasp hypotheses generation.

关键词： Clouds Shape Three dimensional displays Robots Databases Computational modeling Grasping

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Task modeling in imitation learning using latent variable models

Task modeling in imitation learning using latent variable mo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Carl Henrik Ek Dan Song Kai Huebner Danica Kragic Computer Vision & Active Perception Laboratory Center of Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

An important challenge in robotic research is learning and reasoning about different manipulation tasks from scene observations. In this paper we present a probabilistic model capable of modeling several different types of input sources within the same model. Our model is capable to infer the task using only partial observations. Further, our framework allows the robot, given partial knowledge of the scene, to reason about what information streams to acquire in order to disambiguate the state-space the most. We present results for task classification within and also reason about different features discriminative power for different classes of tasks.

关键词： Data models Robot sensing systems Humans Training Feature extraction Training data

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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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Saliency ranking for benthic survey using underwater images

Saliency ranking for benthic survey using underwater images

引用

International Conference on Control, Automation, Robotics and vision (ICARCV)

作者： Matthew Johnson-Roberson Oscar Pizarro Stefan Williams Computational Vision and Active Perception Laboratory School of Computer Science and Communication Royal Institute of Technology Sweden ARC Centre of Excellence of Autonomous Systems Australian Centre of Field Robotics University of Sydney Australia

This paper presents a novel architecture for a classification system based on the visual saliency of images. The work is motivated by the difficulty of reviewing large numbers of images as a human operator in the context of Autonomous Underwater Vehicle (AUV) surveys. We formulate a feature space in which an algorithm operates over color and texture to determine saliency and illustrate how this can be used to find interesting or unusual images within a large data set. The saliency classification based on these general image features allows for overlays highlighting interesting benthos or geologic structures on large scale 3D seafloor reconstructions, quickly providing spatial context to human observers. These results are validated using a set of human trials in which images are classified into salient and non-salient categories by a number of test subjects. The trials show good agreement both between subjects and between the human labels and the automated classification system. The results of the automated technique are also compared directly to a more traditional SVM classification system showing favorable results for our system for generalizing to new environments.

关键词： Pixel Humans Visualization Entropy Image color analysis Indexes Context

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Grasping familiar objects using shape context

Grasping familiar objects using shape context

引用

International Conference on Advanced Robotics (ICAR)

作者： Jeannette Bohg Danica Kragic Centre of Autonomous Systems Computer Vision and Active Perception Laboratory Stockholm Sweden

We present work on vision based robotic grasping. The proposed method relies on extracting and representing the global contour of an object in a monocular image. A suitable grasp is then generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labeled synthetic images. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects. Furthermore, we will show how our representation supports the inference of a full grasp configuration.

关键词： Humans Robot kinematics Robot vision systems Supervised learning Object detection Cameras Shape control computer vision Laboratories Prototypes

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Modeling Image Context Using Object Centered Grid

Modeling Image Context Using Object Centered Grid

引用

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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