A New Assessment of Convolutional Neural Networks for Texture Directionality Detection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-41630-9_13
Reference20 articles.
1. Julesz, B., Gilbert, E.N., Shepp, L.A., Frisch, H.L.: Inability of humans to discriminate between visual textures that agree in second order statistics: revisited. Perception 2(4), 391–405 (1973). https://doi.org/10.1068/p020391
2. Kociołek, M., Kozłowski, M., Cardone, A.: A convolutional neural networks-based approach for texture directionality detection. Sensors 22, 5620 (2022). https://doi.org/10.3390/S22020562
3. Hassekar, P., Sawant, R.R.: Experimental analysis of perceptual based texture features for image retrieval. In: Proceedings - 2015 International Conference on Communication, Information and Computing Technology, ICCICT 2015, pp. 1–6 (2015). https://doi.org/10.1109/ICCICT.2015.7045666
4. Lin, X., Ye, L., Zhong, W., Zhang, Q.: Directionality-based modified coefficient scanning for image coding. In: Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015, vol. 1, no. CISP, pp. 194–198 (2016). https://doi.org/10.1109/CISP.2015.7407874
5. Maskey, M., Newman, T.S.: On measuring and employing texture directionality for image classification. Pattern Anal. Appl. (2021). https://doi.org/10.1007/s10044-021-01013-8
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