Vector field design has a wide variety of applications in computer
graphics, including texture synthesis, non-photorealistic rendering, fluid and crowd simulation, and shape deformation. This paper addresses the problem of the design of time-varying vector fields on surfaces. The additional time dimension poses a number of unique challenges to the...
Support Vector Machines (SVM) and Random Forests (RF) have
consistently outperformed other machine learning algorithms on a variety of
problems. SVM can be used for classification and regression on many types of
data (e.g. nonlinear, high dimensional), but cannot handle missing or mixed data.
This research implements a permutation-based variable...
Support Vector Machines (SVM) and Random Forests (RF) have
consistently outperformed other machine learning algorithms on a variety of
problems. SVM can be used for classification and regression on many types of
data (e.g. nonlinear, high dimensional), but cannot handle missing or mixed data.
This research implements a permutation-based variable...
Reliable analysis of vector elds is crucial for the rigorous interpretation of the ow data stemming from a wide range of
engineering applications. Morse decomposition of a vector field has proven a useful topological representation that is more numerically stable than previous vector field skeletons. In this paper, we enhance...
