Ver registro no DEDALUS
Exportar registro bibliográfico

Metrics


Metrics:

Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor (2018)

  • Authors:
  • USP affiliated authors: GONZAGA, ADILSON - EESC
  • USP Schools: EESC
  • DOI: 10.1007/s11042-018-6204-1
  • Subjects: RECONHECIMENTO DE IMAGEM; ENGENHARIA ELÉTRICA
  • Language: Inglês
  • Imprenta:
  • Source:
  • Acesso online ao documento

    Online accessDOI or search this record in
    Informações sobre o DOI: 10.1007/s11042-018-6204-1 (Fonte: oaDOI API)
    • Este periódico é de assinatura
    • Este artigo NÃO é de acesso aberto
    • Cor do Acesso Aberto: closed

    How to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      VIEIRA, Raissa Tavares; NEGRI, Tamiris Trevisan; GONZAGA, Adilson. Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor. Multimedia Tools and Applications, Amsterdam, Netherlands, Springer, 2018. Disponível em: < https://doi.org/10.1007/s11042-018-6204-1 > DOI: 10.1007/s11042-018-6204-1.
    • APA

      Vieira, R. T., Negri, T. T., & Gonzaga, A. (2018). Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor. Multimedia Tools and Applications. doi:10.1007/s11042-018-6204-1
    • NLM

      Vieira RT, Negri TT, Gonzaga A. Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor [Internet]. Multimedia Tools and Applications. 2018 ;Available from: https://doi.org/10.1007/s11042-018-6204-1
    • Vancouver

      Vieira RT, Negri TT, Gonzaga A. Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor [Internet]. Multimedia Tools and Applications. 2018 ;Available from: https://doi.org/10.1007/s11042-018-6204-1

    Referências citadas na obra
    Andrearczyk V, Whelan PF (2016) Using filter banks in convolutional neural networks for texture classification. Pattern Recogn Lett 84:63–69
    Arivazhagan S, Ganesan L, Padam SP (2006) Texture classification using Gabor wavelets based rotation invariant features. Pattern Recogn Lett 27(16):1976–1982
    Brodatz P (1966) Textures: a photographic album for artists and designers. Dover Pubns
    Brodatz Texture Rotation Dataset (2017) Available online at < http://imagem.sel.eesc.usp.br/base/Brodatz_rotated/index.html >. Accessed 7 Mar 2018
    Chan TH, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032
    Charalampidis D, Kasparis T (2002) Wavelet-based rotational invariant roughness features for texture classification and segmentation. IEEE Trans Image Process 11(8):825–837
    Chen C, Zhang B, Su H, Li W, Wang L (2016) Land-use scene classification using multi-scale completed local binary patterns. SIViP 10(4):745–752
    Cimpoi M, Maji S, Vedaldi A (2015) Deep filter banks for texture recognition and segmentation. In: Computer vision and pattern recognition (CVPR), 2015 IEEE conference on, pp 3828–3836
    Cun H, Fei H, Hassanien AE, Xiao K Texture-based rotation-invariant histograms of oriented gradients. In: Computer Engineering Conference (ICENCO), 2015 11th International. IEEE, pp 223–228
    Deng H, David AC (2004) Gaussian MRF rotation-invariant features for image classification. IEEE Trans Pattern Anal Mach Intell 26(7):951–955
    Dharmagunawardhana C, Mahmoodi S, Bennett M, Niranjan M (2016) Rotation invariant texture descriptors based on gaussian markov random fields for classification. Pattern Recogn Lett 69:15–21
    Do MN, Vetterli M (2002) Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models. IEEE Transactions on Multimedia 4(4):517–527
    Fernández A, Ghita O, González E, Bianconi F, Whelan PF (2011) Evaluation of robustness against rotation of LBP CCR and ILBP features in granite texture classification. Mach Vis Appl 22(6):913–926
    Ferraz CT, Gonzaga A (2016) Object classification using a local texture descriptor and a support vector machine. Multimedia Tools and Applications 1–33
    Ferraz CT, Pereira Jr O, Gonzaga A (2014) Feature description based on center-symmetric local mapped patterns. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing. ACM, pp 39–44
    Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43(3):706–719
    Guo Z, Lei Z, David Z (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663
    Guo Z, Li Q, You J, Zhang D, Liu W (2012) Local directional derivative pattern for rotation invariant texture classification. Neural Comput & Applic 21(8):1893–1904
    Jafari-Khouzani K, Soltanian-Zadeh H (2005) Rotation-invariant multiresolution texture analysis using radon and wavelet transforms. IEEE Trans Image Process 14(6):783–795
    Jafari-Khouzani K, Soltanian-Zadeh H (2005) Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans Pattern Anal Mach Intell 27(6):1004–1008
    Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved LBP under Bayesian framework. In: Image and Graphics (ICIG’04), Third International Conference on. IEEE, pp 306–309
    Jing H, Xinge Y, Yuan Y, Feng Y, Lin L (2010) Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet. Neurocomputing 73(4):883–894
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
    Kun L, Henrik S, Thorsten S, Thomas B, Klaus P, Thomas B, Olaf R (2013) Rotation-invariant hog descriptors using fourier analysis in polar and spherical coordinates. Int J Comput Vis 106(3):342–346
    Kylberg G, Sintorn IM (2016) On the influence of interpolation method on rotation invariance in texture recognition. EURASIP Journal on Image and Video Processing 2016(1):17
    Kylberg G, Sintorn I M, Kylberg Sintorn Rotation dataset (2015) Available online at < http://www.cb.uu.se/~gustaf/KylbergSintornRotation/ >. Accessed 6 Aug 2017
    Li Z, Liu G, Yang Y, You J (2012) Scale- and rotation-invariant local binary pattern using scale-adaptive Texton and subuniform-based circular shift. IEEE Trans Image Process 21(4):2130–2140
    Liu L, Fieguth P, Guo Y, Wang X, Pietikäinen M (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recogn 62:135–160
    Negri TT, Zhou F, Obradovic Z, Gonzaga A (2017) A robust descriptor for color texture classification under varying illumination. In: Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 2017 12th International Joint Conference on, vol 4, pp 378–388
    Nosaka R, Fukui K (2014) Hep-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recogn 47(7):2428–2436
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
    Pillai A, Soundrapandiyan R, Satapathy S, Satapathy SC, Jung KH, Krishnan R (2018) Local diagonal extrema number pattern: a new feature descriptor for face recognition. Futur Gener Comput Syst 81:297–306
    Pun CM, Lee MC (2003) Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell 25(5):590–603
    Rassem TH, Khoo BE, Makbol NM, Alsewari AA (2017) Multi-scale colour completed local binary patterns for scene and event sport image categorisation. IAENG Int J Comput Sci 44(2)
    Ryu J, Hong S, Yang HS (2015) Sorted consecutive local binary pattern for texture classification. IEEE Trans Image Process 24(7):2254–2265
    Sánchez-Yáñez RE, Kurmyshev EV, Fernández A (2003) One-class texture classifier in the CCR feature space. Pattern Recogn Lett 24(9):1503–1511
    Sifre L, Mallat S (2012) Rotation, scaling and deformation invariant scattering for texture discrimination. In: Computer vision and pattern recognition (CVPR), 2013 IEEE conference on, pp 1233–1240
    Song Y, Zhang F, Li Q, Huang H, O’Donnell LJ, Cai W (2017) Locally-transferred fisher vectors for texture classification. Proc IEEE Conf Comput Vis Pattern Recognit 22-29:4912–4920. https://doi.org/10.1109/ICCV.2017.526
    Souza JM, Vieira RT, Gonzaga A (2015) Analysis of iris texture under pupil contraction/dilation for biometric recognition. In: Workshop de Visão Computacional, pp 128–133
    Tamrakar D, Khanna P (2016) Noise and rotation invariant RDF descriptor for palmprint identification. Multimedia Tools and Applications 75(10):5777–5794
    Vieira RT, Negri TT, Gonzaga A (2016) Robustness of rotation invariant descriptors for texture classification. In: International Symposium on Visual computing. Springer International Publishing, pp 268–277
    Vieira RT, Negri T, Cavichiolli A, Gonzaga A (2017) Human epithelial type 2 (HEp-2) cell classification by using a multiresolution texture descriptor. In: Computer vision (WVC), 2017 workshop of, pp 1–6
    Wan S, Lee HC, Huang X, Xu T, Xu T, Zeng X, Zhou C (2017) Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy. Med Image Anal 38:104–116
    Yadav AR, Anand RS, Dewal ML, Gupta S (2017) Binary wavelet transform-based completed local binary pattern texture descriptors for classification of microscopic images of hardwood species. Wood Sci Technol 51(4):909–927