Brief Description:

 

Mathematical Morphology (MM) is a theory of image processing based on lattices and offers non-linear operators contrasting with the traditional image processing operators. In the pre-deep learning era, MM-based image processing techniques were popularly used. The popularity of MM can be attributed to the simplicity of the theory and easy adaptability of its operators. Recently, deep learning (DL) has become a de facto go-to method for image processing. The popularity of DL can also be attributed to the simplicity of the theory and wide adaptability of the basic techniques. Recall that the fundamental units of DL comprise a large number of linear functions and link functions (or activation functions) that connect layers in the network. However, this is predominantly based on linear operators. Hence a natural question arises - Can one combine the fundamentally non-linear MM tools with the current DL techniques to obtain better models? This is broadly the main topic of my research. It is hypothesized that integrating MM with DL would help improve the robustness of the techniques, leading to better generalizability and stable models.

 

 

Keywords:

 

Mathematical Morphology,  Combinatorial Optimization, Deep Learning