Binary impression dental1/4/2024 ![]() ![]() Įichmann G, Kasparis T (1988) Topologically invariant texture descriptors. Įbenuwa SH, Sharif MS, Alazab M, Al-Nemrat A (2019) Variance ranking attributes selection techniques for binary classification problem in imbalance data. Intelligent data analysis 1(3):131–156ĭu S, Yan Y, Ma Y (2014) Local spiking pattern and its application to rotation- and illumination-invariant texture classification. ACM Trans Graph 18(1):1–34ĭash M, Liu H (1997) Feature selection for classification. IEEE Trans Pattern Anal Mach Intell 13(8):803–808ĭana KJ, VanGinneken B, Nayar SK, Koenderink JJ (1999) Reflectance and texture of real world surfaces. In: IEEE conference on computer vision and pattern recognition (CVPR) 3828–3836, Ĭohen FS, Fan Z, Attali S (1991) Automated inspection of textile fabrics using textural models. IEEE Trans Pattern Anal Machine Intell 16(2):208–214Ĭimpoi M, Maji S, Vedaldi A (2015) Deep filter banks for texture recognition and segmentation. Ĭhen JL, Kundu A (1994) Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov model. IEEE Trans Image Process 13(6):782–791Ĭhakraborty S, Singh SK, Chakraborty P (2017) Local quadruple pattern: a novel descriptor for facial image recognition and retrieval. Journal of Mathematical Imaging and Vision 40(3):259–268Ĭampisi P, Neri A, Panci C, Scarano G (2004) Robust rotation-invariant texture classification using a model based approach. IEEE Proceedings Vision, Image, and Signal Processing 145(3):167–172īianconi F, Fernández A (2011) On the occurrence probability of local binary patterns: a theoretical study. IEEE Trans Geosci Remote Sens 33(5):1170–1181Īrof H, Deravi F (1998) Circular neighborhood and 1-DDFT features for texture classification and segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence 28(12):2037–2041Īnys H, He DC (1995) Evaluation of textural and multi polarization radar features for crop classification. ![]() Comparison results on the same datasets imply the superiority of the proposed schemes to the conventional methods.Īhonen T, Hadid A, Pietikäinen M (2006) Face recognition with local binary patterns: application to face recognition. ![]() Applying the introduced methods to the known benchmarks like Outex (TC3, TC10, TC13, TC12(t) and TC12(h)), UIUC, CUReT and Defect Fabric datasets indicates that even by adopting lower number of features, the classification rate is enhanced from 1% to 9% while the features number are decreased around 10% to 99%. Furthermore, a constraint feature selection method is proposed that selects discriminative features. All of the proposed mapping methods are rotation and illumination invariant. To reduce the size of features, in this paper, some mapping methods are proposed for feature reduction and mapping of these features into a histogram. Merging these histograms increases the features number significantly. Although completed local binary pattern is seemingly the most precise variant of this type of descriptor and provides high classification accuracy by joining three histograms of features. ![]() Local binary pattern is one of the most known descriptors, which is used for texture classification. ![]()
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