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检索条件"机构=Department of Machine Vision and Pattern Recognition Laboratory"

共 168 条记录，以下是121-130 订阅

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Unsupervised Visual Object Categorisation via Self-organisation

Unsupervised Visual Object Categorisation via Self-organisat...

引用

International Conference on pattern recognition

作者： Teemu Kinnunen Joni-Kristian Kamarainen Lasse Lensu Heikki Kälviäinen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Finland Computational Vision Group Kouvola Lappeenranta University of Technology Kouvola Finland

Visual object categorisation (VOC) has become one of the most actively investigated topic in computer vision. In the mainstream studies, the topic is considered as a supervised problem, but recently, the ultimate challenge has been posed: Unsupervised visual object categorisation. Hitherto only a few methods have been published, all of them being computationally demanding successors of their supervised counterparts. In this study, we address this problem with a simple and effective method: competitive learning leading to self organisation (self-categorisation). The unsupervised competitive learning approach is implemented using the Kohonen self-organising map algorithm (SOM). The SOM is used to perform the both unsupervised codebook generation and object categorisation. We present our method in detail and compare results to the supervised approach.

关键词： Visualization Accuracy Training Support vector machines pattern recognition Conferences Databases

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Making Visual Object Categorization More Challenging: Randomized Caltech-101 Data Set

Making Visual Object Categorization More Challenging: Random...

引用

International Conference on pattern recognition

作者： Teemu Kinnunen Joni-Kristian Kamarainen Lasse Lensu Jukka Lankinen Heikki Käviäinen Machine Vision and Pattern Recognition Laboratory Finland Computational Vision Group Kouvola Lappeenranta University of Technology Computational Vision Group Kouvola Lappeenranta University of Technology Finland

Visual object categorization is one of the most active research topics in computer vision, and Caltech-101 data set is one of the standard benchmarks for evaluating the method performance. Despite of its wide use, the data set has certain weaknesses: (i) the objects are practically in a standard pose and scale in the middle of the images and (ii) background varies too little in certain categories making it more discriminative than the foreground objects. In this work, we demonstrate how these weaknesses bias the evaluation results in an undesired manner. In addition, we reduce the bias effect by replacing the backgrounds with random landscape images from Google and by applying random Euclidean transformations to the foreground objects. We demonstrate how the proposed randomization process makes visual object categorization more challenging improving the relative results of methods which categorize objects by their visual appearance and are invariant to pose changes. The new data set is made publicly available for other researchers.

关键词： Visualization Training Computer vision Conferences Google Benchmark testing pattern recognition

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Recovering the Topology of Multiple Cameras by Finding Continuous Paths in a Trellis

Recovering the Topology of Multiple Cameras by Finding Conti...

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International Conference on pattern recognition

作者： Yinghao Cai Kaiqi Huang Tieniu Tan Matti Pietikäinen National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China Machine Vision Group University of Oulu Finland Chinese Academy of Sciences University of Oulu Finland

ISBN: (纸本)9781424475421

In this paper, we propose an unsupervised method for recovering the topology of multiple cameras with non-overlapping fields of view. The nodes in the topology graph are defined as entry/exit zones in each camera while the connectivity between nodes is inferred through finding continuous paths in a trellis where appearance information and temporal information of moving objects are encoded. Unlike previous methods which assume a single mode transition distribution between nodes, our method is capable of dealing with multi-modal transition situations when both cars and pedestrians are in the scene. Results on simulated and real-life datasets demonstrate the effectiveness of the proposed method.

关键词： Cameras Topology Network topology Image color analysis Search problems Color Nickel

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A novel method of fingerprint minutiae extraction based on Gabor phase

A novel method of fingerprint minutiae extraction based on G...

引用

IEEE International Conference on Image Processing

作者： Xin Gao Xiaoguang Chen Jia Cao Zirui Deng Chongjin Liu Jufu Feng Key Laboratory of Machine Perception MOE Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing China Institute of Image Processing and Pattern Recognition North-China University of Technology Beijing China

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.

关键词： Pixel Feature extraction Image quality Fingerprint recognition Detectors Frequency estimation Software algorithms

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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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Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering 08

Graph theoretical framework for simultaneously integrating v...

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17th International Conference on World Wide Web 2008, WWW'08

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

ISBN: (纸本)9781605580852

With the explosive growth of Web and the recent development in digital media technology, the number of images on the Web has grown tremendously. Consequently, Web image clustering has emerged as an important application. Some of the initial efforts along this direction revolved around clustering Web images based on the visual features of images or textual features by making use of the text surrounding the images. However, not much work has been done in using multimodal information for clustering Web images. In this paper, we propose a graph theoretical framework for simultaneously integrating visual and textual features for efficient Web image clustering. Specifically, we model visual features, images and words from surrounding text using a tripartite graph. Partitioning this graph leads to clustering of the Web images. Although, graph partitioning approach has been adopted before, the main contribution of this work lies in a new algorithm that we propose - Consistent Isoperimetric High-order Co-clustering (CIHC), for partitioning the tripartite graph. Computationally, CIHC is very quick as it requires a simple solution to a sparse system of linear equations. Our theoretical analysis and extensive experiments performed on real Web images demonstrate the performance of CIHC in terms of the quality, efficiency and scalability in partitioning the visual feature-image-word tripartite graph.

关键词： Digital storage

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Palmprint recognition using kernel PCA of Gabor features

Palmprint recognition using kernel PCA of Gabor features

引用

International International Symposium on Computer and Information Sciences (ISCIS)

作者： Murat Ekinci Murat Aykut Computer Vision and Pattern Recognition Laboratory Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palmprint features. The Gabor transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can produce salient features that are most suitable for palmprint recognition. The kernel PCA method then nonlinearly maps the Gabor-wavelet image into a high-dimensional feature space. The proposed algorithm has been successfully tested on two different public data sets from the PolyU palmprint databases for which the samples were collected in two different sessions.

关键词： Kernel Principal component analysis Image recognition Testing Biometrics Face recognition Lighting pattern recognition Computer vision Image databases

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Finding best measurable quantities for predicting human visual quality experience

Finding best measurable quantities for predicting human visu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： T. Eerola J.-K. Kamarainen T. Leisti R. Halonen L. Lensu H. Kalviainen P. Oittinen G. Nyman Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology Finland Department of Psychology University of Helsinki Finland Department of Media Technology Helsinki University of Technology Finland

The literature of visual quality is mainly concentrated on devising new physical, visual, or computational quality features which could indirectly reflect ldquotrue visual qualityrdquo. The problem is that the true visual quality is always a subjective and context sensitive judgement of a single individual or a group of individuals. Therefore, the developed methods are only loosely connected to this ultimate objective, and the existing de facto and official standards have been designed by forming a consensus among experts of a specific field (e.g., in the printing industry). In this study, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Then we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation trial. Finally by using the above framework, we devise the best combinations from a set of well-known measurable quantities. The devised combinations can be considered as optimal when agreement with the human visual quality experience is desired, and therefore, they also reveal completely novel information about measuring visual quality.

关键词： Humans Physics computing Psychology Printing Quality assessment machine vision pattern recognition Fluid flow measurement Histograms Standards development

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Is there hope for predicting human visual quality experience?

Is there hope for predicting human visual quality experience...

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IEEE International Conference on Systems, Man and Cybernetics

作者： T. Eerola J.-K. Kamarainen T. Leisti R. Halonen L. Lensu H. Kalviainen G. Nyman P. Oittinen Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology Finland Department of Psychology University of Helsinki Finland Department of Media Technology Helsinki University of Technology Finland

One of the most important research goals in media science is a computational model for the human perception of visual quality, that is, how to predict the subjective visual quality experience. This research area has converged to developing new and investigating existing lower-level measurable quantities, physical, visual or computational, which could explain the high level experience. A principal research question, whether the prediction of the visual quality experience based on any lower-level objective measurements is possible at all, has received much less attention. This question is investigated in this study. First, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Second, we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation. Finally, the presented ranking method is used to provide quantitative evidence that visual quality experience can be predicted using lower-level measurable quantities. This result is novel and by simultaneously revealing the underlying lower-level factors it should re-direct the future research towards the true model.

关键词： Humans Physics computing Printing Psychology Quality assessment Computer vision machine vision pattern recognition Q factor Fluid flow measurement

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Gabor volume based local binary pattern for face representation and recognition

Gabor volume based local binary pattern for face representat...

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International Conference on Automatic Face and Gesture recognition

作者： Zhen Lei Shengcai Liao Ran He Matti Pietikainen Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China Machine Vision Group University of Oulu Oulun yliopisto FI Finland

This paper presents a novel face representation and recognition approach. The face image is first decomposed by multi-scale and multi-orientation Gabor filters and local binary pattern (LBP) analysis is then applied on the derived Gabor magnitude responses. Different from (W.C. Zhang et al., 2005), the present method not only describes the neighboring relationship in spatial domain, but also exploit those between different scales (frequency) and orientations. Specifically, we first reformulate the Gabor magnitude responses as a 3rd-order volume and then apply LBP analysis on three orthogonal planes of the Gabor volume, named GV-LBP-TOP in short, in a hope to encode sufficient information for face representation. Further, a computationally effective version, E-GV-LBP, is proposed to depict the neighboring changes in spatial, frequency and orientation domains simultaneously. Finally, the weighted histogram intersection metric is utilized to measure the dissimilarity of faces. Experimental results on FERET and FRGC ver 2.0 databases show the significant advantages of the proposed method.

关键词： Face recognition pattern recognition Histograms Principal component analysis Independent component analysis Lighting Face detection Linear discriminant analysis Robustness Gabor filters

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