Algorithmic Improvements to Finding Approximately Neutral Surfaces

Algorithmic improvements for variational inference

Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially useful when applying variational Bayes (VB) to models outside the conjugate-exponential family. For them, variational Bayesian expectation maximization (VB EM) algorithms are not easily available, and gradient-based m...

Fast 2.5D Mesh Segmentation to Approximately Convex Surfaces

—A range image segmentation approach is presented. A range image obtained by a 3D sensor is transformed to a 2.5D triangle mesh. This mesh is then segmented into approximately convex surfaces by an iterative procedure based on the incremental convex hull algorithm and region growing. The proposed approach is designed as a fast mid-level tool whose task is to provide suitable geometric primitive...

Point Cloud Segmentation to Approximately Convex Surfaces for Fruit Recognition

A fruit recognition approach based on segmenting the point cloud acquired by a 3D camera into approximately convex surfaces is considered. A segmentation approach which transforms a depth image into a triangular mesh and then segments this mesh into approximately convex segments is applied to depth images of fruits on trees. An analysis of the results obtained by this approach is performed with...

Algorithmic Improvements in Regular Model Checking

Algorithmic Approach for finding Convolutional

PONNALA, LALIT Algorithmic Approach for finding Convolutional Code generators for the Translation Initiation of Escherichia coli K-12. (Under the direction of Professor Donald L. Bitzer and Professor Winser E. Alexander). Using error-control coding theory, we parallel the functionality of the translation of mRNA into amino acids to the decoding of noisy parity streams that have been encoded usi...