Visualization plays a critical role in the scientific understand of the massive streams of data from scientific simulations and experiments. Continued growth in performance and availability of large scale supercomputing resources (e.g. exascale and faster over the next decade) enables both increasing simulation resolutions and an increasing number of and breadth of simulation ensemble runs. In the modern scientific process these simulation ensembles are verified for correctness and then validated with experimental ensembles to increase our overall scientific knowledge. Effective visualization of the verification and validation (V&V) prices is a significant challenge. Additional challenges include the significant gap between supercomputing processing and data storage speeds. In this talk, I will highlight current accomplishments from the U.S. Exascale Computing Project to address these challenges include high-dimensional visual analysis, comparative visualization, in situ visualization, portable multi-threaded visualization algorithms, and automated techniques. I will present a vision of a set of needed initiatives to support the visual understanding of the complex and evolving modern scientific process.

Image Caption: This visualization is one member of a visualization ensemble used to study the potential effects of an asteroid impact in Earth's oceans. The study explores  the effects of varying the size of the asteroid, speed of the asteroid, and the angle of impact.

Historically, mathematical morphology has primarily focused on the processing and analysis of two-dimensional image data. In this talk, I will survey a number of other areas where mathematical morphology has found fruitful application. I plan to address the following topics.

1. Volume processing and visualization.

Some examples are: morphological pyramids for multiresolution visualization of 3D medical data by maximum intensity projection; connected morphological operators for combined volumetric filtering and visualization; and volumetric segmentation and visualization by morphological active surface models or level sets.

2. Visual exploration of high-dimensional data.

Here there are numerous applications, such as watershed algorithms for fast reconstruction and visualization of brain networks; finding and exploring relevant subspaces in high-dimensional astronomical data; or filtering and visualization of tensor fields such as diffusion tensor imaging (DTI) data.