Semantic air segmentation in core with noise reduction and post-processing - Mikhail I. Volkov; Polina S. Kargina (Известия Томского политехнического университета: Промышленная кибернетика)
This paper presents results on semantic air segmentation for digital concrete cores. Brightness normalization and noise reduction were used as image preprocessing. To increase the efficiency of segmentation, a method is proposed for segmentation in three directions, followed by summing the resulting masks and using a median filter. The resulting image masks have a three-dimensional structure and can be used to generate a training dataset for 3D convolutional neural network architectures. The method proposed can be used as well to enlarge images in the training set if data labeling takes a long time.