liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 15 of 15
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Adan, Antonio
    et al.
    Alpaydin, Ethem
    Andreadis, I.
    Baldock, Richard
    Basu, Anup
    Bayro-Corrochano, Eduardo
    Berberidis, Kostas
    Bergevin, Robert
    Bhanu, Bir
    Biehl, Michael
    Pattern Recognition Referees 20092010In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 43, no 1, p. 1-4Article in journal (Refereed)
  • 2.
    Björnsdotter, Malin
    et al.
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden.
    Wessberg, Johan
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden.
    Clustered sampling improves random subspace brain mapping2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 6, p. 2035-2040Article in journal (Refereed)
    Abstract [en]

    Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.

  • 3.
    Danielis, Alessandro
    et al.
    CNR, Italy.
    Giorgi, Daniela
    CNR, Italy.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, Faculty of Science & Engineering.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, Faculty of Science & Engineering.
    Colantonio, Sara
    CNR, Italy.
    Salvetti, Ovidio
    CNR, Italy.
    Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images2017In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 63, p. 355-370Article in journal (Refereed)
    Abstract [en]

    Lip segmentation is a non-trivial task because the colour difference between the lip and the skin regions maybe not so noticeable sometimes. We propose an automatic lip segmentation technique for hyper-spectral images from an imaging prototype with medical applications. Contrarily to many other existing lip segmentation methods, we do not use colour space transformations to localise the lip area. As input image, we use for the first time a parametric blood concentration map computed by using narrow spectral bands. Our method mainly consists of three phases: (i) for each subject generate a subset of face images enhanced by different simulated Lambertian illuminations, then (ii) perform lip segmentation on each enhanced image by using constrained morphological operations, and finally (iii) extract features from Fourier-based modeled lip boundaries for selecting the lip candidate. Experiments for testing our approach are performed under controlled conditions on volunteers and on a public hyper-spectral dataset. Results show the effectiveness of the algorithm against low spectral range, moustache, and noise.

  • 4.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Department of Information Science and Media Studies, University of Bergen, Norway.
    A pattern recognition framework for detecting dynamic changes on cyclic time series2015In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 3, p. 696-708Article in journal (Refereed)
    Abstract [en]

    This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed  model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.

  • 5.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Statistical Analysis of Chromosome Characteristics1974In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 6, no 2, p. 115-126Article in journal (Refereed)
    Abstract [en]

    The advent of new stains for chromosomes has increased the possibility of implementing useful automated chromosome analysis. The case with which chromosomes can now be recognized makes it possible to perform detailed statistical analysis of the chromosomes of an individual. This paper describes methods for assembling chromosome information from several cells in such a way that accidental variations due to preparation, etc. can be eliminated and an undistorted set of characteristics of the chromosome complement can be established. This set of characteristics can then be compared with various references, and statements can be made concerning the relationships between variations in the chromosome complement and genetic traits. These same methods can be employed in multiple-cell karyotyping to circumvent the classical problem of touching and overlapping chromosomes. The methods also allow one to achieve very reliable descriptions of the chromosome complement. The importance of appropriate descriptors of the chromosomes is illustrated.

  • 6.
    Liu, Jin
    et al.
    School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangshu, China; School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
    Pham, Tuan D
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
    A spatially constrained fuzzy hyper-prototype clustering algorithm2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 4, p. 1759-1771Article in journal (Refereed)
    Abstract [en]

    We present in this paper a fuzzy clustering algorithm which can handle spatially constraint problems often encountered in pattern recognition. The proposed method is based on the notions of hyperplanes, the fuzzy c-means, and spatial constraints. By adding a spatial regularizer into the fuzzy hyperplane-based objective function, the proposed method can take into account additionally important information of inherently spatial data. Experimental results have demonstrated that the proposed algorithm achieves superior results to some other popular fuzzy clustering models, and has potential for cluster analysis in spatial domain.

  • 7.
    Markus, Nenad
    et al.
    University of Zagreb, Croatia .
    Frljak, Miroslav
    University of Zagreb, Croatia .
    Pandzic, Igor S.
    University of Zagreb, Croatia .
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Eye pupil localization with an ensemble of randomized trees2014In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 47, no 2, p. 578-587Article in journal (Refereed)
    Abstract [en]

    We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices.

  • 8.
    Ng, Theam Foo
    et al.
    The University of New South Wales, Canberra, ACT 2600, Australia.
    Pham, Tuan D
    The University of New South Wales, Canberra, ACT 2600, Australia.
    Jia, Xiuping
    The University of New South Wales, Canberra, ACT 2600, Australia.
    Feature interaction in subspace clustering using the Choquet integral2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 7, p. 2645-2660Article in journal (Refereed)
    Abstract [en]

    Subspace clustering has recently emerged as a popular approach to removing irrelevant and redundant features during the clustering process. However, most subspace clustering methods do not consider the interaction between the features. This unawareness limits the analysis performance in many pattern recognition problems. In this paper, we propose a novel subspace clustering technique by introducing the feature interaction using the concepts of fuzzy measures and the Choquet integral. This new framework of subspace clustering can provide optimal subsets of interacted features chosen for each cluster, and hence can improve clustering-based pattern recognition tasks. Various experimental results illustrate the effective performance of the proposed method.

  • 9.
    Pham, Tuan
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    The Kolmogorov-Sinai entropy in the setting of fuzzy sets for image texture analysis and classification2016In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 53, p. 229-237Article in journal (Refereed)
    Abstract [en]

    The Kolmogorov–Sinai (K–S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. Sequence probabilities therefore play a central role for the computation of the entropy rate to determine if the dynamical system under study is deterministically non-chaotic, deterministically chaotic, or random. This paper extends the notion of the K–S entropy to measure the entropy rate of imprecise systems using sequence membership grades, in which the underlying deterministic paradigm is replaced with the degree of fuzziness. While constructing sequential probabilities for the calculation of the K–S entropy is difficult in practice, the estimate of the K–S entropy in the setting of fuzzy sets in an image is feasible and can be useful for modeling uncertainty of pixel distributions in images. The fuzzy K–S entropy is illustrated as an effective feature for image analysis and texture classification.

  • 10.
    Pham, Tuan D
    School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.
    Fuzzy posterior-probabilistic fusion2011In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 44, no 5, p. 1023-1030Article in journal (Refereed)
    Abstract [en]

    The paradigm of the permanence of updating ratios, which is a well-proven concept in experimental engineering approximation, has recently been utilized to construct a probabilistic fusion approach for combining knowledge from multiple sources. This ratio-based probabilistic fusion, however, assumes the equal contribution of attributes of diverse evidences. This paper introduces a new framework of a fuzzy probabilistic data fusion using the principles of the permanence of ratios paradigm, and the theories of fuzzy measures and fuzzy integrals. The fuzzy sub-fusion of the proposed approach allows an effective model for incorporating evidence importance and interaction.

  • 11.
    Pham, Tuan D
    School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia.
    GeoEntropy: A measure of complexity and similarity2010In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 43, no 3, p. 887-896Article in journal (Refereed)
    Abstract [en]

    Measuring the complexity of a pattern expressed either in time or space has been introduced to quantify the information content of the pattern, which can then be applied for classification. Such information measures are particularly useful for the understanding of systems complexity in many fields of sciences, business and engineering. The novel concept of geostatistical entropy (GeoEntropy) as a measure of pattern complexity and similarity is addressed in this paper. It has been experimentally shown that GeoEntropy is an effective algorithm for studying signal predictability and has superior capability of classifying complex bio-patterns.

  • 12.
    Savas, Berkant
    et al.
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Eldén, Lars
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Handwritten digit classification using higher order singular value decomposition2007In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 40, no 3, p. 993-1003Article in journal (Refereed)
    Abstract [en]

    In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5% even though the approximation reduces the original training data with more than 98% before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one.

  • 13.
    Su, Ran
    et al.
    The University of New South Wales, Canberra, ACT 2600, Australia/ North Ryde, NSW 1670, Australia.
    Sun, Changming
    Informatics and Statistics, North Ryde, NSW 1670, Australia.
    Pham, Tuan D
    The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
    Junction detection for linear structures based on Hessian, correlation and shape information2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 10, p. 3695-3706Article in journal (Refereed)
    Abstract [be]

    Junctions have been demonstrated to be important features in many visual tasks such as image registration, matching, and segmentation, as they can provide reliable local information. This paper presents a method for detecting junctions in 2D images with linear structures as well as providing the number of branches and branch orientations. The candidate junction points are selected through a new measurement which combines Hessian information and correlation matrix. Then the locations of the junction centers are refined and the branches of the junctions are found using the intensity information of a stick-shaped window at a number of orientations and the correlation value between the intensity of a local region and a Gaussian-shaped multi-scale stick template. The multi-scale template is used here to detect the structures with various widths. We present the results of our algorithm on images of different types and compare our algorithm with three other methods. The results have shown that the proposed approach can detect junctions more accurately.

  • 14.
    Tan, Xiao
    et al.
    The University of New South Wales, Canberra, ACT 2600, Australia; CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 1670, Australia.
    Sun, Changming
    CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 1670, Australia.
    Sirault, Xavier
    CSIRO Agriculture Flagship, Clunies Ross Street, Canberra, ACT 2601, Australia.
    Furbank, Robert
    CSIRO Agriculture Flagship, Clunies Ross Street, Canberra, ACT 2601, Australia.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, The University of Aizu, Fukushima 965-8580, Japan.
    Feature matching in stereo images encouraging uniform spatial distribution2015In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 8, p. 2530-2542Article in journal (Refereed)
    Abstract [en]

    Finding feature correspondences between a pair of stereo images is a key step in computer vision for 3D reconstruction and object recognition. In practice, a larger number of correct correspondences and a higher percentage of correct matches are beneficial. Previous researches show that the spatial distribution of correspondences are also very important especially for fundamental matrix estimation. So far, no existing feature matching method considers the spatial distribution of correspondences. In our research, we develop a new algorithm to find good correspondences in all the three aspects mentioned, i.e., larger number of correspondences, higher percentage of correct correspondences, and better spatial distribution of correspondences. Our method consists of two processes: an adaptive disparity smoothing filter to remove false correspondences based on the disparities of neighboring correspondences and a matching exploration algorithm to find more correspondences according to the spatial distribution of correspondences so that the correspondences are as uniformly distributed as possible in the images. To find correspondences correctly and efficiently, we incorporate the cheirality constraint under an epipole polar transformation together with the epipolar constraint to predict the potential location of matching point. Experiments demonstrate that our method performs very well on both the number of correct correspondences and the percentage of correct correspondences; and the obtained correspondences are also well distributed over the image space.

  • 15.
    Tan, Xiao
    et al.
    The University of Hong Kong, Pokfulam, Hong Kong.
    Sun, Changming
    CSIRO Computational Informatics, North Ryde, NSW, Australia.
    Wong, Kwan-YeeK.
    The University of Hong Kong, Pokfulam, Hong Kong.
    Pham, Tuan
    Aizu Research Cluster for Medical Engineering and Informatics, The University of Aizu, Fukushima, Japan.
    Guided image completion by confidence propagation2016In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 50, p. 210-222Article in journal (Refereed)
    Abstract [en]

    This paper presents a new guided image completion method which fills any missing values by considering information from a guidance image. We develop a confidence propagation scheme that allows the filling process to be carried out simultaneously without the need of deciding the filling order. We conduct experiments in several applications where the problem can be formulated into a guided image completion problem, such as interactive segmentation and colorization. The experimental results show that our method provides good results and succeeds in situations where conventional methods fail. In addition, as all building blocks are parallel processes, our method is much suitable for hardware acceleration.

1 - 15 of 15
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf