Abstract
Texture is a characteristic used to partition and classify images into areas of interest.
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References
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D (2017) Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 44(1):151–165
Waugh SA, Purdie CA, Jordan LB, Vinnicombe S, Lerski RA, Martin P, Thompson AM (2016) Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 26(2):322–330
Wei L, Hong-ying D (2016) Real-time road congestion detection based on image texture analysis. Procedia Eng 137:196–201
Ogdahl W, Ward A, Knutson E, Liu J, Wirt S, Berg E, Sun X (2019) Predict beef tenderness using image texture features. Meat Muscle Biol 1(3):109–109
Liu L, Fieguth P, Guo Y, Wang X, Pietikäinen M (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recognit 62:135–160
Nath SS, Mishra G, Kar J, Chakraborty S, Dey N (2014) A survey of image classification methods and techniques. In: 2014 International conference on control, instrumentation, communication and computational technologies (ICCICCT), IEEE, pp 554–557
Mehta R, Egiazarian K (2016) Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recognit Lett 71:16–22
Yuan J, Wang D, Cheriyadat AM (2015) Factorization-based texture segmentation. IEEE Trans Image Process 24(11):3488–3497
Dey N, Rajinikanth V, Ashour A, Tavares JM (2018) Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10(2):51
Wu Q, Gan Y, Lin B, Zhang Q, Chang H (2015) An active contour model based on fused texture features for image segmentation. Neurocomputing 151:1133–1141
Verma M, Raman B (2016) Local tri-directional patterns: a new texture feature descriptor for image retrieval. Digit Signal Proc 51:62–72
Ikeda N, Gupta A, Dey N, Bose S, Shafique S, Arak T, Suri JS (2015) Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement. Ultrasound Med Biol 41(5):1247–1262
Ngan TT, Tuan TM, Minh NH, Dey N (2016) Decision making based on fuzzy aggregation operators for medical diagnosis from dental X-ray images. J Med Syst 40(12):280
Zhang X, Cui J, Wang W, Lin C (2017) A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors 17(7):1474
Brodatz texture album (http://www.ux.uis.no/~tranden/brodatz.html)
Lee H, Chen YPP (2015) Image based computer aided diagnosis system for cancer detection. Expert Syst Appl 42(12):5356–5365
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Chaki, J., Dey, N. (2020). Introduction to Texture Feature. In: Texture Feature Extraction Techniques for Image Recognition. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-15-0853-0_1
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DOI: https://doi.org/10.1007/978-981-15-0853-0_1
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