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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Joni-Kristian Kamarainen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Finland

ISBN: (纸本)9781467325851

In applications of computer vision and image analysis, Gabor filters have maintained their popularity in feature extraction for almost three decades. The original reason that draw attention was the similarity between Gabor filters and the receptive field of simple cells in the visual cortex. A more practical reason is their success in many applications, e.g., face detection and recognition, iris recognition and fingerprint matching, where Gabor feature based methods are among the top performers. The derivation of Gabor features is elegant through the fundamental domains of signal processing: space (time) and frequency. Topped with many practical and computational advantages we will see their use also in future applications.

关键词： Feature extraction Frequency-domain analysis Face recognition Iris recognition Computer vision Visualization

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Visual saliency and categorisation of abstract images

Visual saliency and categorisation of abstract images

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International Conference on pattern recognition

作者： M. Laine-Hernandez T. Kinnunen J.-K. Kamarainen L. Lensu H. Kälviäinen P. Oittinen Department of Media Technology Aalto University Finland Lappeenranta University of Technology Finland Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Finland

ISBN: (纸本)9781467322164

Visual object categorisation problem has attracted significant attention during the last ten years, and the two main hypotheses adopted by virtually all methods are i) detection of visual saliency and ii) bag-of-visual-words based categorisation. It is, however, difficult to verify the hypotheses with humans since many recordings, such as gaze fixation locations, represent processing after the recognition and the object classification task is too easy for humans producing no information about uncertainties in the cognitive process. To the authors' best knowledge, this work is the first attempt to study the main hypotheses and state-of-the-art algorithms for visual object categorisation with abstract images. These images inhibit rapid recognition and cause the observers' opinions differ substantially in assigning the images into “similar categories”. Our work reveals interesting findings: the state-of-the-art methods' performances drop to almost pure chance while human observers remain surprisingly consistent.

关键词： Visualization Abstracts Observers Humans Image color analysis Image recognition Feature extraction

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Detection of bubbles as Concentric Circular Arrangements

Detection of bubbles as Concentric Circular Arrangements

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International Conference on pattern recognition

作者： Nataliya Strokina Jiri Matas Tuomas Eerola Lasse Lensu Heikki Kälviäinen Machine Vision and Pattern Recognition Laboratory (MVPR) Lappeenranta University of Technology Finland Center for Machine Perception (CMP) Czech Technical University in Prague Czech Republic

ISBN: (纸本)9781467322164

A method for the detection of bubble-like transparent objects with multiple interfaces in a liquid is proposed. Depending on the lighting conditions, bubble appearance varies significantly, including contrast reversal and multiple inter-reflections. We formulate the bubble detection problem as the detection of Concentric Circular Arrangements (CCA). The CCAs are recovered in a hypothesize-optimize-verify framework. The hypothesis generation proceeds by sampling from the components of the non-maximum suppressed responses of oriented ridge filters followed by CCA parameter estimation. Parameter optimization is carried out by minimizing a novel cost-function by the simplex method. The proposed method for bubble detection showed good performance in an industrial application requiring estimation of gas volume in pulp suspension, achieving 1.5% mean absolute relative error.

关键词： Image edge detection Suspensions Educational institutions Estimation Joining processes Liquids Parameter estimation

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Online non-feedback image re-ranking via dominant data selection

Online non-feedback image re-ranking via dominant data selec...

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20th ACM International Conference on Multimedia, MM 2012

作者： Cao, Chen Chen, Shifeng Li, Yuhong Liu, Jianzhuang Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab. Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450310895

Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches. © 2012 ACM.

关键词： Supervised learning

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Locating high-density clusters with noisy queries

Locating high-density clusters with noisy queries

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International Conference on pattern recognition

作者： Chen Cao Shifeng Chen Changqing Zou Jianzhuang Liu Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong China

ISBN: (纸本)9781467322164

Semi-supervised learning (SSL) relies on a few labeled samples to explore data's intrinsic structure through pairwise smooth transduction. The performance of SSL mainly depends on two folds: (1) the accuracy of labeled queries, (2) the integrity of manifolds in data distribution. Both of these qualities would be poor in real applications as data often consist of several irrelevant clusters and discrete noise. In this paper we propose a novel framework to simultaneously remove discrete noise and locate the high-density clusters. Experiments demonstrate that our algorithm is quite effective to solve several problems such as non-feedback image re-ranking and image co-segmentation.

关键词： Noise Noise measurement Clustering algorithms Databases Vectors Manifolds Semisupervised learning

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Thresholding-based detection of fine and sparse details

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Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 328-338页

作者： Alexander DROBCHENKO Joni-Kristian KAMARAINEN Lasse LENSU Jarkko VARTIAINEN Heikki KALVIAINEN Tuomas EEROLA Machine Vision and Pattern Recognition Laboratory Department of Information TechnologyFaculty of Technology ManagementLappeenranta University of TechnologyP.O.Box 20FI-53851 LappeenrantaFinland

Fine and sparse details appear in many quality inspection applications requiring machine *** on flat surfaces,such as paper or board,the details can be made detectable by oblique *** this study,a general definition of such details is given by defining sufficient statistical properties from *** statistical model allows simulation of data and comparison of methods designed for detail *** on the definition,utilization of the existing thresholding methods is shown to be well *** comparison shows that minimum error thresholding outperforms the other standard ***,the results are successfully applied to a paper printability inspection application,and the IGT picking assessment,in which small surface defects must be *** provided method and measurement system prototype provide automated assessment with results comparable to manual expert evaluations in this laborious task.

关键词： adaptive thresholding paper quality inspection picking machine vision

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Development of color density concept with color difference formulas in respect to human vision system

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Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 381-387页

作者： Arto KAARNA Wei LIU Heikki KALVIAINEN Machine Vision and Pattern Recognition Laboratory Department of Information TechnologyFaculty of Technology ManagementLappeenranta University of TechnologyP.O.Box 20FI-53851 LappeenrantaFinland

The aims of this study are to develop the color density concept and to propose the color density based color difference *** color density is defined using the metric coefficients that are based on the discrimination ellipses and the locations of the colors in the color *** ellipse sets are the MacAdam ellipses in the CIE 1931 xy-chromaticity diagram and the chromaticity-discrimination ellipses in the CIELAB *** latter set was originally used to develop the CIEDE2000 color difference *** color difference can be calculated from the color density for the two colors under *** a result,the color density represents the perceived color difference more accurately,and it could be used to characterize a color by a quantity attribute matching better to the perceived color difference from this *** from this,the color density concept provides simply a correction term for the estimation of the color *** the experiments,the line element formula and the CIEDE2000 color difference formula performed better than the color density based difference *** reason behind this is in the current modeling of the color density *** discrimination ellipses are typically described with three-dimensional data consisting of two axes,the major and the minor,and the inclination *** proposed color density is only a one-dimensional corrector for color differences;thus,it cannot capture all the details of the ellipse ***,the color density gives clearly more correct estimations to perceived color differences than Euclidean distances using directly the coordinates of the color space.

关键词： color density discrimination ellipses color difference formulas color vision

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Local frequency descriptor for low-resolution face recognition

Local frequency descriptor for low-resolution face recogniti...

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IEEE International Conference on Automatic Face and Gesture recognition

作者： Lei, Zhen Ahonen, Timo Pietikainen, Matti Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Chinese Academy of Sciences 95 Zhongguancun Donglu Beijing 100190 China Machine Vision Group University of Oulu FI-90014 Oulun yliopisto Finland

ISBN: (纸本)9781424491407

Face recognition from low-resolution images is a common yet challenging case in real applications. Since the high-frequency information is lost in low-resolution images, it is necessary to explore robust information in the low frequency domain. In this paper, we propose an effective local frequency descriptor (LFD) for low resolution face recognition, by building upon the ideas behind local phase quantization (LPQ) and exploring both blur-invariant magnitude and phase information in the low frequency domain. The proposed descriptor is more descriptive than LPQ with more comprehensive information. In addition, a statistical uniform pattern definition method is introduced to improve the efficiency of the proposed descriptor. Experimental results on FERET and a real video database show that LFD is effective and robust for low-resolution face recognition. © 2011 IEEE.

关键词： Face recognition

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Detection and decision-support diagnosis of diabetic retinopathy using machine vision

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pattern recognition and Image Analysis 2011年第2期21卷 140-143页

作者： Kauppi, T. Kamarainen, J.K. Lensu, L. Kälviäinen, H. Uusitalo, H. Machine Vision and Pattern Recognition Laboratory Lappeenranta Finland P.O. Box 20 FIN-53850 Lappeenranta Finland University of Tampere Medical School University of Tampere FIN-33014 Tampere Finland Tampere University Hospital Tampere Finland P.O. Box 1627 70211 Kuopio Finland

This invited paper considers the results of the IMAGERET project. The goal of the project is to demonstrate how lesions in a retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. The project consists of the following results: an image annotation tool for medical expert annotation, diabetic retinopathy databases, an evaluation framework for development and comparison of methods, image-based and pixel-based methods, and new imaging solutions. The automated diagnosis can be seen in two steps: in the first step, it is decided whether an eye needs further analysis (too many lesions visible) or not (the eye is healthy enough). In the second step, fundus images selected for further analysis are automatically diagnosed. The developed system saves both resources of medical experts and costs in healthcare. It will further offer a tool for the health care providers to improve the quality of the life of diabetes patients. This is important since the number of diabetes patients is increasing, especially rapidly in the developed countries. © 2011 Pleiades Publishing, Ltd.

关键词： Diagnosis

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Automatic motion-guided video stylization and personalization 11

Automatic motion-guided video stylization and personalizatio...

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

作者： Cao, Chen Chen, Shifeng Zhang, Wei Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

Video stylization transfers a source video into an artistic version while maintaining temporal coherence between adjacent frames. In this paper, we formulate the unsupervised example-based video stylization with Markov random field model. In our algorithm, we implement an improved optical flow algorithm to maintain temporal coherence while improve the accuracy of estimation along motion boundaries. We also extend our algorithm to the application of video personalization, in which human faces keep clear and distinguishable. A series of techniques are fused in video personalization, including face detection and alignment, motion flow, skin detection, and illumination blending. Given a source video and a style template image, our algorithm produces the stylized and/or personalized video(s) automatically. Experimental results demonstrate that our algorithm performs excellently in both video stylization and personalization. Copyright 2011 ACM.

关键词： Face recognition

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