uu.seUppsala universitets publikasjoner
Endre søk
Begrens søket
1 - 16 of 16
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Kylberg, Gustaf
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Automatic Virus Identification using TEM: Image Segmentation and Texture Analysis2014Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. It both serves as a routine diagnostic technique as well as an essential tool for detecting infectious agents in new and unusual disease outbreaks.

    The technique does not depend on virus specific targets and can therefore detect any virus present in the sample. New or reemerging viruses can be detected in EM images while being unrecognizable by molecular methods.

    One problem with diagnostic EM is its high dependency on experts performing the analysis. Another problematic circumstance is that the EM facilities capable of handling the most dangerous pathogens are few, and decreasing in number.

    This thesis addresses these shortcomings with diagnostic EM by proposing image analysis methods mimicking the actions of an expert operating the microscope. The methods cover strategies for automatic image acquisition, segmentation of possible virus particles, as well as methods for extracting characteristic properties from the particles enabling virus identification.

    One discriminative property of viruses is their surface morphology or texture in the EM images. Describing texture in digital images is an important part of this thesis. Viruses show up in an arbitrary orientation in the TEM images, making rotation invariant texture description important. Rotation invariance and noise robustness are evaluated for several texture descriptors in the thesis. Three new texture datasets are introduced to facilitate these evaluations. Invariant features and generalization performance in texture recognition are also addressed in a more general context.

    The work presented in this thesis has been part of the project Panvirshield, aiming for an automatic diagnostic system for viral pathogens using EM. The work is also part of the miniTEM project where a new desktop low-voltage electron microscope is developed with the aspiration to become an easy to use system reaching high levels of automation for clinical tissue sections, viruses and other nano-sized particles.

    Delarbeid
    1. Evaluation of noise robustness for local binary pattern descriptors in texture classification
    Åpne denne publikasjonen i ny fane eller vindu >>Evaluation of noise robustness for local binary pattern descriptors in texture classification
    2013 (engelsk)Inngår i: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, nr 17Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

    sted, utgiver, år, opplag, sider
    Springer, 2013
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-203664 (URN)10.1186/1687-5281-2013-17 (DOI)000321866700001 ()
    Tilgjengelig fra: 2013-07-17 Laget: 2013-07-17 Sist oppdatert: 2018-01-11bibliografisk kontrollert
    2. Regional Zernike Moments for Texture Recognition
    Åpne denne publikasjonen i ny fane eller vindu >>Regional Zernike Moments for Texture Recognition
    2012 (engelsk)Inngår i: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, s. 1635-1638Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

     Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick co-occurrence matrices and local binary patterns on two different texture image sets, and show that they are noise insensitive and very well suited for texture recognition.

    Emneord
    Statistical, Syntactic and Structural Pattern Recognition, Segmentation, Color and Texture, Classification and Clustering
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-188373 (URN)
    Konferanse
    ICPR 2012
    Tilgjengelig fra: 2012-12-17 Laget: 2012-12-17 Sist oppdatert: 2018-01-11
    3. Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern Features
    Åpne denne publikasjonen i ny fane eller vindu >>Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern Features
    (engelsk)Artikkel i tidsskrift (Fagfellevurdert) Submitted
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-217327 (URN)
    Tilgjengelig fra: 2014-02-02 Laget: 2014-02-02 Sist oppdatert: 2014-04-29
    4. Exploring Filter Banks Based on Orthogonal Moments for Texture Recognition
    Åpne denne publikasjonen i ny fane eller vindu >>Exploring Filter Banks Based on Orthogonal Moments for Texture Recognition
    (engelsk)Manuskript (preprint) (Annet vitenskapelig)
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-188371 (URN)
    Tilgjengelig fra: 2014-02-02 Laget: 2012-12-17 Sist oppdatert: 2014-04-29
    5. A Note on: Invariant Features, Overfitting and Generalization Performance in Texture Recognition
    Åpne denne publikasjonen i ny fane eller vindu >>A Note on: Invariant Features, Overfitting and Generalization Performance in Texture Recognition
    (engelsk)Manuskript (preprint) (Annet vitenskapelig)
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-217326 (URN)
    Tilgjengelig fra: 2014-02-02 Laget: 2014-02-02 Sist oppdatert: 2014-04-29
    6. Kylberg Texture Dataset v. 1.0
    Åpne denne publikasjonen i ny fane eller vindu >>Kylberg Texture Dataset v. 1.0
    2011 (engelsk)Rapport (Annet vitenskapelig)
    sted, utgiver, år, opplag, sider
    Uppsala: Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, 2011. s. 4
    Serie
    External report (Blue series) ; 35
    Emneord
    texture dataset
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-163125 (URN)
    Tilgjengelig fra: 2011-12-08 Laget: 2011-12-08 Sist oppdatert: 2018-01-12bibliografisk kontrollert
    7. Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
    Åpne denne publikasjonen i ny fane eller vindu >>Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
    2009 (engelsk)Inngår i: Proceedings of the 16th Scandinavian Conference on Image Analysis (SCIA), Berlin: Springer-Verlag , 2009, s. 169-178Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    When searching for viruses in an electron microscope thesample grid constitutes an enormous search area. Here, we present methodsfor automating the image acquisition process for an automatic virusdiagnostic application. The methods constitute a multi resolution approachwhere we first identify the grid squares and rate individual gridsquares based on content in a grid overview image and then detect regionsof interest in higher resolution images of good grid squares. Our methodsare designed to mimic the actions of a virus TEM expert manually navigatingthe microscope and they are also compared to the expert’s performance.Integrating the proposed methods with the microscope wouldreduce the search area by more than 99.99% and it would also removethe need for an expert to perform the virus search by the microscope.

    sted, utgiver, år, opplag, sider
    Berlin: Springer-Verlag, 2009
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 5575
    Emneord
    TEM, virus diagnostics, automatic image acquisition
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys
    Identifikatorer
    urn:nbn:se:uu:diva-108568 (URN)10.1007/978-3-642-02230-2_18 (DOI)978-3-642-02229-6 (ISBN)
    Tilgjengelig fra: 2009-09-22 Laget: 2009-09-22 Sist oppdatert: 2018-01-13
    8. Segmentation of virus particle candidates in transmission electron microscopy images
    Åpne denne publikasjonen i ny fane eller vindu >>Segmentation of virus particle candidates in transmission electron microscopy images
    Vise andre…
    2012 (engelsk)Inngår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 245, nr 2, s. 140-147Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

    sted, utgiver, år, opplag, sider
    Blackwell Publishing, 2012
    Emneord
    radial density profile, transmission electron microscopy, virus detection, virus segmentation
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-163761 (URN)10.1111/j.1365-2818.2011.03556.x (DOI)000298987100004 ()
    Tilgjengelig fra: 2011-10-04 Laget: 2011-12-14 Sist oppdatert: 2017-12-08bibliografisk kontrollert
    9. Virus texture analysis using local binary patterns and radial density profiles
    Åpne denne publikasjonen i ny fane eller vindu >>Virus texture analysis using local binary patterns and radial density profiles
    2011 (engelsk)Inngår i: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications / [ed] San Martin, César; Kim, Sang-Woon, Springer Berlin/Heidelberg, 2011, s. 573-580Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

    sted, utgiver, år, opplag, sider
    Springer Berlin/Heidelberg, 2011
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 7042
    Emneord
    virus morphology, texture analysis, local binary patterns, radial density profiles
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-163119 (URN)10.1007/978-3-642-25085-9_68 (DOI)978-3-642-25084-2 (ISBN)
    Konferanse
    16th Iberoamerican Congress on Pattern Recognition
    Tilgjengelig fra: 2011-12-08 Laget: 2011-12-08 Sist oppdatert: 2018-01-12bibliografisk kontrollert
    10. Virus recognition based on local texture
    Åpne denne publikasjonen i ny fane eller vindu >>Virus recognition based on local texture
    2014 (engelsk)Inngår i: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, s. 3227-3232Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    To detect and identify viruses in electron microscopy images is crucial in certain clinical emergency situations. It is currently a highly manual task, requiring an expert sittingat the microscope to perform the analysis visually. Here wefocus on and investigate one aspect towards automating the virusdiagnostic task, namely recognizing the virus type based on theirtexture once possible virus objects have been segmented. Weshow that by using only local texture descriptors we achievea classification rate of almost 89% on texture patches from 15different virus types and a debris (false object) class. We compareand combine 5 different types of local texture descriptors andshow that by combining the different types a lower classificationerror is achieved. We use a Random Forest Classifier and comparetwo approaches for feature selection.

    Serie
    International Conference on Pattern Recognition, ISSN 1051-4651
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-216290 (URN)10.1109/ICPR.2014.556 (DOI)000359818003060 ()978-1-4799-5208-3 (ISBN)
    Konferanse
    IEEE 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden
    Tilgjengelig fra: 2014-02-02 Laget: 2014-01-20 Sist oppdatert: 2018-01-11bibliografisk kontrollert
  • 2.
    Kylberg, Gustaf
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Kylberg Texture Dataset v. 1.02011Rapport (Annet vitenskapelig)
  • 3.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    A Note on: Invariant Features, Overfitting and Generalization Performance in Texture RecognitionManuskript (preprint) (Annet vitenskapelig)
  • 4.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern FeaturesArtikkel i tidsskrift (Fagfellevurdert)
  • 5.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Detecting Virus-like Particles Using Transmission Electron Microscopy2009Inngår i: Proceedings SSBA 2009, Symposium on Image Analysis / [ed] Josef Bigun, Antanas Verikas, Halmstad: EIS, Halmstad University , 2009, s. 13-16Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    We present a multi scale approach for automating the image acquisition process for an computerized virus diagnostic application. Our methods are designed to mimic the methodology used by virus TEM experts manually operating the microscope. The methods decrease the search area considerably. In addition we present a segmentation method for virus-like particles based on local intensity information and PCA. This method makes no assumption regarding shape which is vital since many viruses are highly pleomorphic, i.e., have different shapes. The only input parameter used is the approximate virus thickness, which is a conserved feature within a virus species.

  • 6.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Evaluation of noise robustness for local binary pattern descriptors in texture classification2013Inngår i: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, nr 17Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

  • 7.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Exploring Filter Banks Based on Orthogonal Moments for Texture RecognitionManuskript (preprint) (Annet vitenskapelig)
  • 8. Kylberg, Gustaf
    et al.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    On the influence of interpolation method on rotation invariance in texture recognition2016Inngår i: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, Vol. 2016, artikkel-id 17Artikkel i tidsskrift (Fagfellevurdert)
  • 9.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Refinement of Segmented Virus Particel Candidates in TEM Images2011Konferansepaper (Annet vitenskapelig)
  • 10.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Borgefors, Gunilla
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest2009Inngår i: Proceedings of the 16th Scandinavian Conference on Image Analysis (SCIA), Berlin: Springer-Verlag , 2009, s. 169-178Konferansepaper (Fagfellevurdert)
    Abstract [en]

    When searching for viruses in an electron microscope thesample grid constitutes an enormous search area. Here, we present methodsfor automating the image acquisition process for an automatic virusdiagnostic application. The methods constitute a multi resolution approachwhere we first identify the grid squares and rate individual gridsquares based on content in a grid overview image and then detect regionsof interest in higher resolution images of good grid squares. Our methodsare designed to mimic the actions of a virus TEM expert manually navigatingthe microscope and they are also compared to the expert’s performance.Integrating the proposed methods with the microscope wouldreduce the search area by more than 99.99% and it would also removethe need for an expert to perform the virus search by the microscope.

  • 11.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Uppström, Mats
    Ryner, Martin
    Local Intensity and PCA Based Detection of Virus Particle Candidates in Transmission Electron Microscopy Images2009Inngår i: Proc. 6th International Symposium on Image and Signal Processing and Analysis: ISPA 2009, Piscataway, NJ: IEEE , 2009, s. 426-431Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a general method using local intensity informationand PCA to detect objects characterized onlyby that they differ from their surroundings. We apply ourmethod to the problem of automatically detecting virus particlecandidates in transmission electron microscopy images.Viruses have very different shapes and sizes, manyspecies are spherical whereas others are highly pleomorphic.To detect any kind of virus particles in electron microscopyimages it is therefore necessary to use a methodnot restricted to detection of a specific shape. The methodproposed here uses only one input parameter, the approximatevirus thickness, which is a conserved feature withina virus species. It is capable to detect virus particles ofvery varying shapes. Results on images with highly texturedbackground of several different virus species are presented.

  • 12.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Uppström, Mats
    Hedlund, Kjell-Olof
    Borgefors, Gunilla
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Segmentation of virus particle candidates in transmission electron microscopy images2012Inngår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 245, nr 2, s. 140-147Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

  • 13.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Uppström, Mats
    Hedlund, Kjell-Olof
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Towards Identification of Highly Pathogenic Viruses Based on Image Analysis and TEM2010Inngår i: TAMSEC 2010 / [ed] Fredrik Gustafsson, 2010, s. 25-25Konferansepaper (Annet (populærvitenskap, debatt, mm))
  • 14.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Uppström, Mats
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Virus texture analysis using local binary patterns and radial density profiles2011Inngår i: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications / [ed] San Martin, César; Kim, Sang-Woon, Springer Berlin/Heidelberg, 2011, s. 573-580Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

  • 15.
    Sintorn, Ida-Maria
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Kylberg, Gustaf
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Regional Zernike Moments for Texture Recognition2012Inngår i: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, s. 1635-1638Konferansepaper (Fagfellevurdert)
    Abstract [en]

     Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick co-occurrence matrices and local binary patterns on two different texture image sets, and show that they are noise insensitive and very well suited for texture recognition.

  • 16.
    Sintorn, Ida-Maria
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Kylberg, Gustaf
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Virus recognition based on local texture2014Inngår i: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, s. 3227-3232Konferansepaper (Fagfellevurdert)
    Abstract [en]

    To detect and identify viruses in electron microscopy images is crucial in certain clinical emergency situations. It is currently a highly manual task, requiring an expert sittingat the microscope to perform the analysis visually. Here wefocus on and investigate one aspect towards automating the virusdiagnostic task, namely recognizing the virus type based on theirtexture once possible virus objects have been segmented. Weshow that by using only local texture descriptors we achievea classification rate of almost 89% on texture patches from 15different virus types and a debris (false object) class. We compareand combine 5 different types of local texture descriptors andshow that by combining the different types a lower classificationerror is achieved. We use a Random Forest Classifier and comparetwo approaches for feature selection.

1 - 16 of 16
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf