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IEEE International Conference on Data Mining (ICDM)

作者： Yanhua Chen Manjeet Rege Ming Dong Jing Hua Department of Computer Science Machine Vision and Pattern Recognition Laboratory Detroit MI USA Department of Computer Science Graphics and Imaging Laboratory Detroit MI USA

Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they "must" or "cannot" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.

关键词： Iterative algorithms Clustering algorithms Symmetric matrices Data mining Databases Clustering methods Couplings machine vision pattern recognition computer graphics

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Image co-clustering with multi-modality features and user feedbacks 09

Image co-clustering with multi-modality features and user fe...

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17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Chen, Yanhua Dong, Ming Wan, Wanggen Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States School of Communication and Info. Eng. Shanghai University Shanghai 200072 China

ISBN: (纸本)9781605586083

In Content-based Image Retrieval (CBIR) research, advanced technology that fuses the heterogeneous information into image clustering has drawn extensive attention recently. However, using multiple features for co-clustering images without any user feedbacks is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) framework for image co-clustering. Our method computes new relational matrices by incorporating user provided feedbacks into images through simultaneous distance metric learning and feature selection for different low-level visual features. Using an iterative algorithm, we perform tri-factorizations of the new matrices to infer image clusters. Theoretically, we show the convergence and correctness of SS-NMF co-clustering and the advantages of SS-NMF co-clustering over existing approaches. Through extensive experiments conducted on image data sets, we demonstrate that SS-NMF provides an effective and efficient solution for image co-clustering. Copyright 2009 ACM.

关键词： Matrix algebra

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Robust stereo on multiple resolutions

Robust stereo on multiple resolutions

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13th International Conference on pattern recognition, ICPR 1996

作者： Menard, Christian Leonardis, Aleš Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 3A-1040 Vienna Austria University of Ljubljana Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia

ISBN: (纸本)081867282X

Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent postprocessing stages. In this paper we present a modification of the standard area-based correlation approach so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. The confidence measure of the correlation and the number of outliers provide two complementary sources of information which, when implemented in a multiresolution framework, result in a robust and efficient method. We present the results of this approach on a number of synthetic and real images. © 1996 IEEE.

关键词： Stereo image processing

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Design of surrogate models in civil engineering by neural networks 9

Design of surrogate models in civil engineering by neural ne...

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9th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Drahy, Vojtech Marik, Radek Kalviainen, Heikki Czech Technical University in Prague Department of Computer Science Prague Czech Republic Czech Technical University in Prague Department of Telecommunication Engineering Prague Czech Republic Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta Finland

ISBN: (纸本)9798350342482

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters' values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model's decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model. © 2024 IEEE.

关键词： Neural network models

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Reconstruction of retinal spectra from RGB data using a RBF network 6

Reconstruction of retinal spectra from RGB data using a RBF ...

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6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016

作者： Nguyen, Uyen Laaksonen, Lauri Uusitalo, Hannu Lensu, Lasse Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Department of Ophthalmology SILK University of Tampere Finland Tauh Eye Center Tampere University Hospital Finland

ISBN: (纸本)9781467389105

In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. The approaches make use of fuzzy c-means clustering to perform quantization of the image data, and the radial basis function network to learn the mapping from the three-component color representation to the spectral space. The dissimilarities between the reconstructed spectral images and the original ones are evaluated on a retinal image set with spectral and RGB images, and by using a standard spectral quality metric. The experimental results show that the proposed approaches are able to reconstruct spectral retinal images with a relatively high accuracy. © 2016 IEEE.

关键词： Radial basis function networks

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Communication via eye blinks and eyebrow raises: Video-based human-computer interfaces

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Universal Access in the Information Society 2003年第4期2卷 359-373页

作者： Grauman, K. Betke, M. Lombardi, J. Gips, J. Bradski, G.R. Vision Interface Group AI Laboratory Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Computer Science Department Boston University 111 Cummington St BostonMA02215 United States EagleEyes Computer Science Department Boston College Fulton Hall Chestnut HillMA02467 United States Vision Graphics and Pattern Recognition Microcomputer Research Laboratory Intel Corporation SC12-303 2200 Mission College Blvd Santa ClaraCA95054-1537 United States

Two video-based human-computer interaction tools are introduced that can activate a binary switch and issue a selection command. "BlinkLink," as the first tool is called, automatically detects a user's eye blinks and accurately measures their durations. The system is intended to provide an alternate input modality to allow people with severe disabilities to access a computer. Voluntary long blinks trigger mouse clicks, while involuntary short blinks are ignored. The system enables communication using "blink patterns:" sequences of long and short blinks which are interpreted as semiotic messages. The second tool, "EyebrowClicker," automatically detects when a user raises his or her eyebrows and then triggers a mouse click. Both systems can initialize themselves, track the eyes at frame rate, and recover in the event of errors. No special lighting is required. The systems have been tested with interactive games and a spelling program. Results demonstrate overall detection accuracy of 95.6% for BlinkLink and 89.0% for EyebrowClicker. © Springer-Verlag 2003.

关键词： computer vision

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Region-based Image Annotation using Asymmetrical Support Vector machine-based Multiple-Instance Learning

Region-based Image Annotation using Asymmetrical Support Vec...

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Conference on computer vision and pattern recognition (CVPR)

作者： Changbo Yang Ming Dong Jing Hua Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.

关键词： machine learning Support vector machines Image retrieval pattern recognition computer science Training data Supervised learning computer vision Feature extraction Image segmentation

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Automatic object segmentation from large scale 3D urban point clouds through manifold embedded mode seeking 11

Automatic object segmentation from large scale 3D urban poin...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Yu, Zhiding Xu, Chunjing Liu, Jianzhuang Au, Oscar C. Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

This paper presents a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. The system consists of three steps: The first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. The second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. The proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures. Copyright 2011 ACM.

关键词： Image segmentation

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Introduction

SpringerBriefs in Applied Sciences and Technology

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SpringerBriefs in Applied sciences and Technology 2018年第9789811065286期 1-12页

作者： Gurumoorthy, Sasikumar Muppalaneni, Naresh Babu Gao, Xiao-Zhi Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Lappeenranta Finland

The EEG is a measure of voltage as a function of time. The voltage of the EEG regulates its amplitude (measured from peak to peak). EEG amplitudes in the cortex range start from 500 to 1500 μV, but the amplitude...

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Image-based characterization of the pulp flows

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pattern recognition and Image Analysis 2016年第3期26卷 630-637页

作者： Sorokin, M. Strokina, N. Eerola, T. Lensu, L. Karttunen, K. Kalviainen, H. Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Computer Vision Group Department of Signal Processing Tampere University of Technology PO Box 527 TampereFI-33101 Finland Cemis-Oulu Unit of Measurement Technology Kajaani University Consortium University of Oulu PO Box 127 KajaaniFI-87400 Finland School of Information Technology Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor Darul Ehsan46150 Malaysia

Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions. © 2016, Pleiades Publishing, Ltd.

关键词： pattern matching

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