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Goniometric imaging of paper gloss

Goniometric imaging of paper gloss

引用

10th IAPR Conference on machine vision Applications, MVA 2007

作者： Kauppi, Tomi Sadovnikov, Albert Lensu, Lasse Kamarainen, Joni-Kristian Silfsten, Pertti Kälviäinen, Heikki Machine Vision and Pattern Recognition Research Group Department of Information Technology Lappeenranta University of Technology Finland Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

ISBN: (纸本)9784901122078

Gloss is a property of paper that is used to emphasize print quality and color appearance. However, standard gloss measuring devices are not able to provide sufficiently accurate information to reliably develop papers with specific gloss characteristics. In this paper, the behavior of gloss is studied using goniometric imaging and spectral color information. A goniometric imaging system is proposed to capture reflection images that reveal the spatial variation of the gloss over paper samples. The characteristics of the gloss are discussed by comparing the results of goniometric imaging, standard gloss measurements and spectroradiometric measurements.

关键词： Gloss measurement

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Gene Expression Clustering: a Novel Graph Partitioning Approach

Gene Expression Clustering: a Novel Graph Partitioning Appro...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yanhua Chen Ming Dong Manjeet Rege Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

In order to help understand how the genes are affected by different disease conditions in a biological system, clustering is typically performed to analyze gene expression data. In this paper, we propose to solve the clustering problem using a graph theoretical approach, and apply a novel graph partitioning model -isoperimetric graph partitioning (IGP), to group biological samples from gene expression data. The IGP algorithm has several advantages compared to the well-established spectral graph partitioning (SGP) model. First, IGP requires a simple solution to a sparse system of linear equations instead of the eigen-problem in the SGP model. Second, IGP avoids degenerate cases produced by spectral approach to achieve a partition with higher accuracy. Moreover, we integrate unsupervised gene selection into the proposed approach through two-way ordering of gene expression data, such that we can eliminate irrelevant or redundant genes in the data and obtain an improved clustering result. We evaluate our approach on several well-known problems involving gene expression profiles of colon cancer and leukemia subtypes. Our experiment results demonstrate that IGP constantly outperforms SGP and produces a better result that is closer to the original labeling of sample sets provided by domain experts. Furthermore, the clustering accuracy is improved significantly when IGP is integrated with the unsupervised gene (feature) selection.

关键词： Gene expression Biological system modeling Diseases Biological systems Data analysis Performance analysis Clustering algorithms Partitioning algorithms Equations Colon

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The DIARETDB1 diabetic retinopathy database and evaluation protocol

The DIARETDB1 diabetic retinopathy database and evaluation p...

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2007 18th British machine vision Conference, BMVC 2007

作者： Kauppi, T. Kalesnykiene, V. Kamarainen, J.-K. Lensu, L. Sorri, I. Raninen, A. Voutilainen, R. Pietilä, J. Kälviäinen, H. Uusitalo, H. Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology Finland Department of Ophthalmology Faculty of Medicine University of Kuopio France Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Perimetria Ltd. Helsinki Finland

ISBN: (纸本)1901725340

Automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. The research interest is justified by the excellent potential for new products in the medical industry and significant reductions in health care costs. However, the maturity of proposed algorithms cannot be judged due to the lack of commonly accepted and representative image database with a verified ground truth and strict evaluation protocol. In this study, an evaluation methodology is proposed and an image database with ground truth is described. The database is publicly available for benchmarking diagnosis algorithms. With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice.

关键词： Database systems

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Incorporating User Provided Constraints into Document Clustering

Incorporating User Provided Constraints into Document Cluste...

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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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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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Clustering Web images with multi-modal features

Clustering Web images with multi-modal features

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15th ACM International Conference on Multimedia, MM'07

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.

关键词： Image analysis

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Using machine vision for observation of phenomena during laser hybrid welding

Using machine vision for observation of phenomena during las...

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ICALEO 2006 - 25th International Congress on Applications of Laser and Electro-Optics

作者： Fellman, Anna Fennander, Henri Salminen, Antti Kyrki, Ville Lappeenranta University of Technology Department of Mechanical Engineering Laser Processing Laboratory Lappeenranta Finland Lappeenranta University of Technology Department of Information Technology Machine Vision and Pattern Recognition Research Group

Laser hybrid welding process is widely studied today, with many research groups trying to clarify the phenomena during the process. Many of the changes occurring during laser hybrid welding process can be observed with the help of high speed imaging. The high speed of the droplet formation and their movement to the melt pool set strict requirements for the imaging frequency in order to provide reliable information about the process. Because of large number of images produced by the high-speed (over 1000 Hz) imaging, it is time-consuming, error-prone, and frustrating to inspect the process manually, using only the human eyes for the observation. These problems could be alleviated by an automated system for the observation. This paper presents an off-line machine vision system created and used to study videos of CO2 laser-MAG hybrid welding experiments in which welding parameters were changed one at a time. The quality of the welds was observed by visual and macrographic examination. In the study, the regularity of pulse frequency of the arc and direction of filler material droplet flight were measured with the help of machine vision. Due to stable nature of keyhole welding part of the process, these characteristics show the stability of the whole welding process. It was found out that the automatic observation can be used if the quality of the images remains good enough. Experimentally, the inspection system provided information that the welding parameters have an effect on direction of droplet movement. This kind of automatic observation of changes in the process could provide a valuable tool for process optimization. The gathered information can be utilized in achieving an optimized process giving the best possible productivity.

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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2006 IEEE Computer Society Conference on Computer vision and pattern recognition, CVPR 2006

作者： Yang, Changbo Dong, Ming Hua, Jing Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 Department of Computer Science Wayne State University Detroit MI 48202

ISBN: (纸本)0769525970

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. © 2006 IEEE.

关键词： Image processing

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Localized Support Vector machines for Classification

Localized Support Vector Machines for Classification

引用

International Joint Conference on Neural Networks (IJCNN)

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

Support vector machines (SVMs) have been promising methods in pattern recognition because of their solid mathematical foundation. In this paper, we propose a localized SVM classification scheme (LSVM). In which we first cluster the training data in each category, and then train a set of SVMs based on these dusters. The SVMs trained from the clusters in each category that are nearest to the given input pattern are then selected for the final classification. Our experiments on six UCI datasets show that LSVM outperforms the traditional SVM.

关键词： Support vector machines Support vector machine classification Kernel USA Councils Solids machine learning Training data Nails Neural networks Clustering algorithms

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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

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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