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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Justus Piater Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Lab CSC KTH Royal Institute of Technology Stockholm Sweden Intelligent and Interactive Systems group Institute of Computer Science University of Innsbruck Austria

The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by learning, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.

关键词： Grasping Shape Planning Dictionaries Databases Grippers Covariance matrix

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A model-driven classification and recursive segmentation method for automatic panel extraction from biological and medical papers

A model-driven classification and recursive segmentation met...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xiaohui Yuan Dongyu Ang Computer Vision and Intelligent Systems Lab Department of CSE University of North Texas Denton TX USA

We present a novel method to automatically extract panels from figures in biomedical articles. Our method consists of figure (or panel) classification and panel segmentation. Figure classification determines the existence of photograph in a figure. A Gaussian model is constructed for photographs and plots. Figures and panels are evaluated based on the model to determine their class. If it contains photographs, an iterative panel-splitting process follows. This process continues until no further straight lines are identified in the subfigures. Experiments were conducted with 182 figures from 25 articles published in different journals. Despite vast difference between figures, our method successfully extracted both plots and photographs and was able to identify zoom-in views that are superimposed on the original photographs.

关键词： Histograms Image segmentation Pixel Biomedical imaging Data mining Biology Transforms

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MULTI-CAMERA POSITIONING FOR AUTOMATED TRACKING systems IN DYNAMIC ENVIRONMENTS

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International Journal of Information Acquisition 2010年第3期7卷 225-242页

作者： YI YAO CHUNG-HAO CHEN BESMA ABIDI DAVID PAGE ANDREAS KOSCHAN MONGI ABIDI Visualization and Computer Vision Lab GE Global Research USA Department of Mathematics and Computer Science North Carolina Central University USA Imaging Robotics and Intelligent Systems Lab University of Tennessee Knoxville USA

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). According to recent literature, handoff safety margin is introduced to sensor planning so that sufficient overlapped FOVs among adjacent cameras are reserved for successful and smooth target transition. In this paper, we investigate the sensor planning problem when considering the dynamic interactions between moving targets and observing cameras. The probability of camera overload is explored to model the aforementioned interactions. The introduction of the probability of camera overload also considers the limitation that a given camera can simultaneously monitor or track a fixed number of targets and incorporates the target's dynamics into sensor planning. The resulting camera placement not only achieves the optimal balance between coverage and handoff success rate but also maintains the optimal balance in environments with various target densities. The proposed camera placement method is compared with a reference algorithm by Erdem and Sclaroff. Consistently improved handoff success rate is illustrated via experiments using typical office floor plans with various target densities.

关键词： Sensor planning visual surveillance multiple camera systems camera handoff

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Geographic information for vision-based road detection

Geographic information for vision-based road detection

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IEEE Symposium on intelligent Vehicle

作者： José M. Alvarez Felipe Lumbreras Theo Gevers Antonio M. López Computer Vision Center and Computer Science Dpt. Univ. Autonoma de Barcelona Spain Intelligent Systems Lab Amsterdam University of Amsterdam and Comp. Vision Center Barcelona Spain

Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.

关键词： Vehicle detection Remotely operated vehicles Road vehicles Mobile robots Vehicle driving Clustering algorithms Sensor phenomena and characterization Costs Energy consumption Image segmentation

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Surface Reconstruction from Multiple Images Filtering Non-Lambert Regions

Surface Reconstruction from Multiple Images Filtering Non-La...

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16th IEEE International Conference on Image Processing

作者： Buyukatalay, Soner Birgul, Ozlem Halici, Ugur Computer Vision and Intelligent Systems Research Lab. METU Electrical and Electronics Engineering Turkey Tübitak Uzay Turkey

ISBN: (纸本)9781424456536

In this study a new algorithm for 3D surface reconstruction from multiple images using a modified photometric stereo method is proposed and tested. The new algorithm, Filtered Lambert Photometric Stereo (FLPS), determines the non-Lambert pixels in the available images using a linearity test and constructs filtering masks for each image that corresponds to specular and self or cast shadow regions. Then, the photometric stereo is applied after eliminating the points in these masks. Tests carried out on synthetic images show that LPS on filtered images is a feasible solution when more than 4 images are available.

关键词： photometric stereo Lambert reflection model filtered photometric stereo

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Physics-based edge evaluation for improved color constancy

Physics-based edge evaluation for improved color constancy

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2009 IEEE Conference on computer vision and Pattern Recognition, CVPR 2009

作者： Gijsenij, Arjan Gevers, Theo Weijer, Joost Van De Intelligent Systems Lab Amsterdam University of Amsterdam Science Park 107 Amsterdam Netherlands Computer Vision Center Campus Universitat Autònoma De Barcelona 08193 Bellaterra Spain

ISBN: (纸本)9781424439935

Edge-based color constancy makes use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as shadow, geometry, material and highlight edges. These different edge types may have a distinctive inf luence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their ref lectance properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation, it is derived that certain edge types are more valuable than material edges for the estimation of the illuminant. To this end, the weighted Grey-Edge algorithm is proposed in which certain valuable edge types are more emphasized for the estimation of the illuminant. From the experimental results, it is shown that the proposed weighted Grey-Edge algorithm based on the shadowshading variant, i.e. assigning higher weights to edges with more energy in the shadow-shading direction, results in the best performance. Moreover, all current state-of-theart methods, including pixel-based methods and edge-based methods, have been signif icantly outperformed by the proposed weighted Grey-Edge algorithm, resulting in an improvement of 9% over the current best-performing algorithm. © 2009 IEEE.

关键词： Color

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Physics-based edge evaluation for improved color constancy

Physics-based edge evaluation for improved color constancy

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Arjan Gijsenij Theo Gevers Joost van de Weijer Intelligent Systems Lab Amsterdam University of Amsterdam Amsterdam Netherlands Computer Vision Center Campus Universitat Autònoma de Barcelona Bellaterra Barcelona

关键词： Image color analysis Performance analysis Light sources computer vision Geometry Taxonomy Lighting Higher order statistics Reflectivity intelligent systems

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On averaging face images for recognition under pose variations

On averaging face images for recognition under pose variatio...

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作者： Zhang, Xiaozheng Zhao, Sanqiang Gao, Yongsheng Computer Vision and Image Processing Lab Institute for Integrated and Intelligent Systems Griffith University Australia

ISBN: (纸本)9781424421756

Recently, psychological studies showed that averaging human face images greatly improves the performance of face recognition under various pose, illumination, expression, and/or aging conditions. This paper investigates quantitatively the mechanism of the face averaging process in face recognition specifically against pose variations. Facilitated with 3D face dataset, the process of face averaging is tested on face images free from human errors and misalignments. Single images are chosen as gallery and the averaged views as probe in all the experiments. Three different scenarios are experimented, i.e., identification using single gallery images, identification using different gallery images, and averaging using unbalanced range of input image. The experimental results show that the averaging process under pose variations is equivalent to generating a face view in an average pose and the improvement in face recognition is subject to the conditions that the gallery pose is close to the average probe pose. © 2008 IEEE.

关键词： Face recognition

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On averaging face images for recognition under pose variations

On averaging face images for recognition under pose variatio...

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International Conference on Pattern Recognition

作者： Xiaozheng Zhang Sanqiang Zhao Yongsheng Gao Computer Vision and Image Processing Lab Institute for Integrated and Intelligent Systems Griffith University Australia

ISBN: (纸本)9781424421749

关键词： Face recognition Image recognition Probes Lighting Testing Humans Face detection Psychology Aging Image databases

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Object recognition via local patch labelling

Object recognition via local patch labelling

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First International Workshop on Deterministic and Statistical Methods in Machine Learning

作者： Bishop, Christopher M. Ulusoy, Ilkay Microsoft Research 7 J J Thompson Avenue Cambridge United Kingdom METU Computer Vision and Intelligent Systems Research Lab. 06531 Ankara Turkey

ISBN: (纸本)3540290737

In recent years the problem of object recognition has received considerable attention from both the machine learning and computer vision communities. The key challenge of this problem is to be able to recognize any member of a category of objects in spite of wide variations in visual appearance due to variations in the form and colour of the object, occlusions, geometrical transformations (such as scaling and rotation), changes in illumination, and potentially non-rigid deformations of the object itself. In this paper we focus on the detection of objects within images by combining information from a large number of small regions, or 'patches', of the image. Since detailed hand-segmentation and labelling of images is very labour intensive, we make use of 'weakly labelled' data in which the training images are labelled only according to the presence or absence of each category of object. A major challenge presented by this problem is that the foreground object is accompanied by widely varying background clutter, and the system must learn to distinguish the foreground from the background without the aid of labelled data. In this paper we first show that patches which are highly relevant for the object discrimination problem can be selected automatically from a large dictionary of candidate patches during learning, and that this leads to improved classification compared to direct use of the full dictionary. We then explore alternative techniques which are able to provide labels for the individual patches, as well as for the image as a whole, so that each patch is identified as belonging to one of the object categories or to the background class. This provides a rough indication of the location of the object or objects within the image. Again these individual patch labels must be learned on the basis only of overall image class labels. We develop two such approaches, one discriminative and one generative, and compare their performance both in terms of patch labelling and

关键词： Object recognition

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