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International Conference on image Analysis and processing

作者： R. Glantz M. Pelillo W.G. Kropatsch Dipartimento di Informatica Università Ca Foscari di Venezia Venezia-Mestre Italy Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Vienna Austria

When matching regions from "similar" images, one typically has the problem of missing counterparts due to local or even global variations of segmentation fineness. Matching segmentation hierarchies, however, not only increases the chances of finding counterparts, but also allows us to exploit the manifold constraints coming from the topological relations between the regions in a hierarchy. In this paper we match hierarchies from panoramic images by constructing an association graph G/sub A/ whose vertices represent potential matches and whose edges indicate topological consistency. Specifically, a maximal [maximum] weight clique of GA corresponds to a topologically consistent mapping with maximal [maximum] total similarity. To find "heavy" cliques, we adapt a greedy pivoting-based heuristic to the weighted case. Experiments on pairs of panoramic images demonstrate the reliability of the results.

关键词： image segmentation pattern recognition image processing automation pattern matching Topology image edge detection

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Single landmark based self-localization of mobile robots

Single landmark based self-localization of mobile robots

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Canadian Conference on computer and Robot Vision, CRV

作者： A. Bais R. Sablatnig J. Gu Robotics Research Group Department of Electrical and Computer Engineering Dalhousie University Halifax Canada Institute of Computer Technology University of Technology Vienna Vienna Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Vienna Austria

In this paper we discuss landmark based absolute localization of tiny autonomous mobile robots in a known environment. Landmark features are naturally occurring as it is not allowed to modify the environment with special navigational aids. These features are sparse in our application domain and are frequently occluded by other robots. This makes simultaneous acquisition of two or more landmarks difficult. Therefore, we propose a system that requires a single landmark feature. The algorithm is based on range measurement of a single landmark from two arbitrary points whose displacement can be measured using dead-reckoning sensors. Range estimation is done with a stereo vision system. Simulation results show that the robot can localize itself if it can estimates range of the same landmark from two different position and if the displacement between the two position is known.

关键词： Mobile robots Robot sensing systems Robot vision systems Cameras Navigation Position measurement Robotics and automation Displacement measurement Educational robots Goniometers

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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International Conference on pattern recognition

作者： M. Jogan A. Leonardis H. Wildenauer H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation University of Technology Vienna Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method.

关键词： Mobile robots Lighting computer vision Filter bank image recognition Indoor environments pattern recognition image processing computer graphics Information science

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Embedded Real-Time Ball Detection Unit for the YABIRO Biped Robot

Embedded Real-Time Ball Detection Unit for the YABIRO Biped ...

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Workshop on Intelligent Solutions in Embedded Systems

作者： Markus Bader Miguel Albero Robert Sablatnig Jose E. Simo Gines Benet Gregor Novak Francisco Blanes Institute of Computer Aided Automation Pattern Recognition and Image Processing Group University of Technology Vienna Austria Departamento de Informática de Sistemas y Computadores Universidad Politécnica de Valencia Spain

Estimation of objects in a 3D space is a fundamental problem in computer vision and robotics. This paper describes an algorithm and its implementation for a vision module as a sensor of a biped robot (YABIRO). The embedded vision sensor is able to estimate the position of objects like spheres in 3D space. Objects are defined with their size and color in a model. The vision sensor detects the positions or at least the directions to the objects and stores them in a history. The algorithm includes a new voting system for detected objects, based on how trustable the detection was, and a new edge filter to terminate edges on the circle border for the circle detection. The systems frame rate depends on the area of interest and lies between 5 Hz and 20 Hz. With a mechanical size of 36times32mm it is smaller than a matchbox

关键词： Object detection Robot sensing systems Robot vision systems computer vision Space technology Detection algorithms Hardware Embedded computing image edge detection Cameras

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Multi-label image segmentation via max-sum solver

Multi-label image segmentation via max-sum solver

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Conference on computer Vision and pattern recognition (CVPR)

作者： Banislav Micusik Tomas Pajdla Pattern Recognition and Image Processing Group Inst. of Computer Aided Automation University of Technology Vienna Austria Center for Machine Perception Dpt. of Cybernetics Czech Technical University Czech Republic

We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the global consistency error evaluated on the Berkeley segmentation database.

关键词： image segmentation image databases Belief propagation pattern recognition image processing automation Cybernetics Color Linear programming Markov random fields

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Virtual Vessel Reconstruction from a Fragment's Profile

Virtual Vessel Reconstruction from a Fragment's Profile

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4th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage, VAST 2003

作者： Kampel, M. Melero, F.J. Vienna University of Technology Institute of Computer Aided Automation Pattern Recognition and Image Processing Group Favoritenstr. 9 183-2 ViennaA-1040 Austria Universidad de Granada E.T.S. Ingeniería Informática Dpt. Lenguajes y Sistemas Informáticos C/. Daniel Saucedo Aranda s/n GranadaE-18071 Spain

ISBN: (纸本)3905673088

Every archaeological excavation must deal with a vast number of ceramic fragments. The documentation, administration and scientific processing of these fragments represent a temporal, personnel, and financial problem. Up to now documentation and classification have been done manually which means a lot of routine work for archaeologists and a very inconsistent representation of the real object. First, there may be errors in the measuring process (diameter or height may be inaccurate). Second, the drawing of the fragment should be in a consistent style, which is not possible since a drawing of an object without interpreting it is very hard to do. We are developing a documentation system for archaeological fragments based on their profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. Hence the position of a fragment (orientation) on a vessel is important, since it allows a good profile to be obtained. This paper presents two different approaches to the estimation of the orientation of archaeological fragments and the profile extraction. One method is based on a Hough inspired approach and the other is based on genetic algorithms and expert interaction. We compare the methods and show results with respect to robustness, applicability, and accuracy. © The Eurographics Association 2003.

关键词： Genetic algorithms

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Line-based landmark recognition for self-localization of soccer robots

Line-based landmark recognition for self-localization of soc...

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International Conference on Emerging Technologies, ICET

作者： A. Baist R. Sablatnig G. Novak Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria Institute of Computer Technology Vienna University of Technology Vienna Austria

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Robust occupancy detection from stereo images

Robust occupancy detection from stereo images

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International Conference on Intelligent Transportation

作者： B. Alefs M. Clabian H. Bischof W. Kropatsch F. Khairallah Advanced Computer Vision GmbH-ACV Vienna Austria Advanced Computer Vision GmbH-ACV Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Computer Science Department University of Technology Vienna Vienna Austria TRW Automotive GmbH Farmington Hills MI USA

Vehicle occupants that are out-of-position can be deadly injured by the deployment of the air bag in a crash situation. In recent years many different sensors and systems have been proposed to detect the type of occupant and the position of the occupant's head. This work presents a method for classification and occupant's head detection based on passive stereo vision. The proposed system uses depth surface analysis and scene statistics together with support vector machines for classification and selection of head candidates. Evaluation of the method shows 99% correct for classification and 98% correct for head detection, using large sets of image data, and image sequences recorded in a driving vehicle.

关键词： Robustness Magnetic heads Vehicle crash testing Sensor systems Stereo vision Layout Statistical analysis Support vector machines Support vector machine classification Vehicle detection

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Cross-modal subspace learning for fine-grained sketch-based image retrieval

arXiv

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arXiv 2017年

作者： Xu, Peng Yin, Qiyue Huang, Yongye Song, Yi-Zhe Ma, Zhanyu Wang, Liang Xiang, Tao Bastiaan Kleijn, W. Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Communications and Signal Processing Group Victoria University of Wellington New Zealand

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

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Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2130卷 v页

作者： Dorffner, Georg Bischof, Horst Hornik, Kurt University of Vienna Dept. of Mecidal Cybernetics and Artificial Intelligence Freyung 6/2 Vienna Austria Technical University of Vienna Institute for Computer Aided Automation Pattern Recognition and Image Processing Group Favoritenstr. 9/1832 Vienna Austria Wirtschaftsuniversität Wien Institut für Statistik Augasse 2-6 Wien Austria

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