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检索条件"机构=Pattern Recognition and Image Processing LaboratoryDepartment of Computer Science"

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Performing clustering analysis on collaborative models

Performing clustering analysis on collaborative models

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作者： Shen, Hongbin Yang, Jie Chen, Ningjiang Dong, Yifei Wang, Shitong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China Philips Research East Asia Shanghai 200070 China School of Computer Science and Engineering University of New South Wales Australia Department of Information Science Southern Yangtze University Wuxi Jiangsu 214036 China

Conventional clustering algorithms are designed for a single independent dataset, i.e. Fuzzy C-Means (FCM) clustering algorithm. In the real world, a dataset is independent of other datasets but sometimes can be cooperative with others by exchanging information, such as the relationship between subsidiary companies. We should therefore consider the influence from other relative collaborative datasets while performing clustering learning under such collaborative circumstances. In this paper, three different collaborative models are discussed and new correct methods are proposed to quantitatively measure such collaboration between datasets, i.e. information gain. The corresponding collaborative clustering algorithms are presented accordingly and the theoretical analysis shows that the new cooperative clustering algorithms can finally converge to a local minimum. Experimental results demonstrate that the clustering structures obtained by new cooperative algorithms are different from those of conventional algorithms for the consideration of collaboration and the performances of these collaborative clustering algorithms can be much better than those conventional single clustering algorithms under the cooperating circumstances. ©?2005-IOS Press and the authors. All rights reserved.

关键词： Clustering algorithms

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Survey on Monocular Depth Estimation for Unmanned Aerial Vehicles using Deep Learning

Survey on Monocular Depth Estimation for Unmanned Aerial Veh...

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IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Horatiu Florea Sergiu Nedevschi Computer Science Dept. Image Processing and Pattern Recognition Research Center Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (纸本)9781665464383

Deep learning-based solutions for the ill-posed problem of Monocular Depth Estimation (MDE) from 2D color images have shown potential in recent years, spurring a very active field of research. Most state-of-the-art proposals focus on solving the problem in the context of automotive advanced driver assistance and/or autonomous driving systems. While presenting their own complexities and challenges, the vast majority of road environments exhibit a number of commonalities amongst themselves. The aerial domain in which modern Unmanned Aerial Vehicles (UAVs) operate is significantly different and features a large variety of possible scenes based on the specific mission carried out. The increasing number of applications for UAVs could benefit from more advanced learning-based MDE solutions for recovering 3D geometric information from the scene. In this paper, we conduct a study of existing research on the topic of MDE specifically tailored for aerial views, as well as presenting the datasets and tools currently supporting such research, high-lighting the challenges that remain. To the best of our knowledge, this is the first survey covering this field.

关键词： Deep learning Technological innovation Three-dimensional displays Roads Estimation Autonomous aerial vehicles Complexity theory

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A Services Selection Policy for ServiceBSP Model with QoS-aware in Grids

A Services Selection Policy for ServiceBSP Model with QoS-aw...

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International Conference on Convergence Information Technology

作者： Yan Jiang Weiqing Tong Wentao Zhao School of Computer Engineering and Science Shanghai University China Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424431779

ServiceBSP model is presented as an extension of BSP model with a view to the advantages of BSP model in grid environment where large-scale and geographically distributed resources (abstracted as services) are available. In this paper, we advocate a preferable service broker framework to support the services selection policy that could optimize the services selection for ServiceBSP model. It means that the services selection can increase the level of satisfaction measured by overall quality value and decrease execution time by considering services dependent relation and location in a subtask group. Meanwhile, the policy is preliminary work ensuring reasonable distribution of services and loading balance in ServiceBSP model. In an addition, we give a detailed process of services selection based on heuristic greedy algorithm. Finally, by comparing response time and price using proposed policy with other policy only considering QoS, the experimental simulation shows that our services selection policy possesses superior performance on response time and then provides satisfactory result for users.

关键词： Quality of service Ontologies Large-scale systems Delay Information technology Grid computing pattern recognition Costs Time measurement Greedy algorithms

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Head detection and localization from sparse 3D data

Head detection and localization from sparse 3D data

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24th German Association for pattern recognition Symposium, DAGM 2002

作者： Clabian, Markus Rötzer, Harald Bischof, Horst Kropatsch, Walter Advanced Computer Vision GmbH Donau City Str.1 ViennaA-1220 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 GrazA-8010 Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Computer Science Department Vienna University of Technology Favoritenstr. 9 ViennaA-1040 Austria

ISBN: (纸本)354044209X

Head detection is an important, but difficult task, if no restrictions such as static illumination, frontal face appearance or uniform background can be assumed. We present a system that is able to perform head detection under very general conditions by employing a 3D measurement system namely a structured light distance measurement. An algorithm of head detection from sparse 3D data (19x19 data points) is developed that reconstructs a 3D surface over the image plane and detects head hypotheses of ellipsoidal shape. We demonstrate that detection and rough localization is possible in up to 90% of the images. © Springer-Verlag Berlin Heidelberg 2002.

关键词： computers

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Fuzzy c-means in an MDL-framework

Fuzzy c-means in an MDL-framework

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International Conference on pattern recognition

作者： A. Selb H. Bischof A. Leonardis Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)0769507506

In this paper we present a minimum description length (MDL) framework for fuzzy clustering algorithms. This framework enables us to find an optimal number of cluster centers. We applied our approach to the fuzzy c-means algorithm for which we designed a computationally efficient procedure. We report the results of our approach on a 2D clustering problem and on RGB color image segmentation.

关键词： Clustering algorithms Radial basis function networks Encoding Color image segmentation image recognition image processing Algorithm design and analysis Unsupervised learning pattern recognition

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Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Linear Correction for Visual Odometry

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Line...

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IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Corvin-Petruţ Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

Our previously proposed linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method corrects it at selected points in time based on the alignment of one past and the current 3D LiDAR measurements (see [7]). In this paper we improve on that method essentially by adding multi-past frame LiDAR point cloud alignment constraints and by performing the correction more often. While this significantly improves the accuracy of the estimation, it has the downside effect of decreasing the overall runtime performance. We thus study the trade-off between accuracy and speed and propose a couple of higher accuracy but real time correction versions. Their evaluation on the KITTI dataset results in an overall performance falling into the so called SLAM-nonSLAM gap.

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From dense subgraph to graph matching: A label propagation approach

From dense subgraph to graph matching: A label propagation a...

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International Conference on Audio, Language and image processing, ICALIP

作者： Zhuoyi Zhao Yu Qiao Jie Yang Li Bai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China School of Computer Science University of Nottingham Nottingham UK

ISBN: (纸本)9781479939046

Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to provide solutions to many problems in computer vision. In this paper, we consider GM as a clustering problem in an association graph whose nodes represent candidate correspondences between two graphs to be matched. And we take the dense subgraph as a good prior for correct correspondences, thus we propose a label propagation approach to expand the dense subgraph to resolve the whole cluster. The label propagation approach is achieved by an affinity-preserving manifold ranking algorithm with a dynamic label vector which enforces the matching constraints. And the matching constraints is introduced through a doubly-stochastic normalization procedure. Extensive experiments demonstrate that our algorithm outperforms the state-of-the-art GM algorithms especially in the presence of outliers and deformation.

关键词： Manifolds Vectors Clustering algorithms Accuracy Heuristic algorithms Sparse matrices Noise

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Multiple eigenspaces by MDL

Multiple eigenspaces by MDL

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International Conference on pattern recognition

作者： A. Leonardis H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

ISBN: (纸本)0769507506

We propose an approach to constructing multiple eigenspaces from a set of training images based on the minimum description length (MDL) principle. The main idea is to systematically build a redundant set of eigenspaces, which are treated as hypotheses that are then subject to a selection procedure. The selection procedure, based on the MDL principle, selects the final resulting set of eigenspaces as an optimal representation of the training set. We have tested the proposed method on a number of standard image sets, and the significance of the approach with respect to the recognition rate has been clearly demonstrated.

关键词： Testing image recognition Principal component analysis Information science image analysis image reconstruction pattern recognition image processing Robustness Robust control

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Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods

Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Moti...

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IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Corvin-Petruț Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781665464383

We have previously proposed a linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method by correcting it at selected points in time based on the alignment of past and current 3D LiDAR measurements (see [7]). In this paper we assess the tolerance to noise of a series of methods derived from the one previously proposed, this time using both linear and non-linear optimization methods to calculate the correction transform. We generate synthetic datasets with various noise pollution levels and assess the performance of each method under investigation in recovering artificially induced odometry estimation errors.

关键词： Visualization Laser radar Three-dimensional displays Pollution Optimization methods Transforms Time measurement

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Illumination insensitive eigenspaces

Illumination insensitive eigenspaces

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International Conference on computer Vision (ICCV)

作者： H. Bischof H. Wildenauer A. Leonardis Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)0769511430

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The major idea is to incorporate a set of gradient based filter banks into the eigenspace recognition framework. This can be achieved since the eigenimage coefficients are invariant for linearly filtered images (input and eigenimages). To achieve further illumination insensitivity we devised a robust procedure for coefficient recovery. The proposed approach has been extensively evaluated on a set of 2160 images and the results were compared to other approaches.

关键词： Lighting Nonlinear filters Principal component analysis image coding Object recognition Filtering Filter bank image recognition Information science image sampling

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