06/14/2019
Textures (that feel, appearance, or consistency of a surface or a substance) are highly variable, which makes them very difficult to define in a mathematical model. A new paper by CSRC associated faculty Jerome Gilles may shed some light on the analysis of textures in images with many possible applications. Huang, et al, Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation, Neurocomputing 349 (2019) 31–43.
In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional…