Abstract
Image texture analysis is ubiquitous as it finds applications in many scientific fields of interest, including biomedical and material science. The detection of meaningful texture properties such as directionality remains a challenging task, due to the complexity of texture. We build upon our past work on the design of convolutional neural networks (CNNs) for texture directionality detection. The CNNs are trained on a library of synthetic textures with known directionality and varying perturbation levels. The present effort focuses on enhancing the training data through a new perturbation procedure and a more diverse set of synthetic textures. We study the performance of new CNN architectures, such as grouped CNNs, on the enhanced synthetic texture library. The results yield novel insight into CNN-based texture directionality detection. Shallow and grouped CNNs show better performance than deep CNNs, unlike previously. We discuss this performance shift and its implications, and suggest possible future work directions.
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Kociołek, M., Cardone, A. (2023). A New Assessment of Convolutional Neural Networks for Texture Directionality Detection. In: Burduk, R., Choraś, M., Kozik, R., Ksieniewicz, P., Marciniak, T., Trajdos, P. (eds) Progress on Pattern Classification, Image Processing and Communications. CORES IP&C 2023 2023. Lecture Notes in Networks and Systems, vol 766. Springer, Cham. https://doi.org/10.1007/978-3-031-41630-9_13
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