**Whittney Research Award**

**Deborah Kunkel**

OSU Department of Statistics

**Title: Anchored Bayesian Gaussian Mixture Models**

**Cooley Memorial Prize**

**Justin Strait**

**Title: A Survey of Landmark-Constrained Elastic Statistical Shape Analysis**

Shape analysis describes the application of statistical methods to shape data. The study of shape data, typically extracted from images and videos, is of increasing importance, particularly in computer vision, medical imaging, and biology. A vital component of shape analysis is the choice of mathematical representation for the shape, as it must respect various shape-preserving transformations. One class of representations used in the literature is based on landmarks, which are (a finite number of) labeled points on an object’s outline. The other primary class, elastic, treats an outline as a flexibly-parameterized continuous function. In this talk, I will describe a new landmark-constrained elastic shape representation which can resolve possible alignment issues. Metrics for shape comparison will be introduced, and some available statistical methods for landmark-constrained shape data will be discussed. A possible model for the inference of landmark constraints will also be proposed, if these points are not readily available.