^October November – 2010 ^December

Active contours are used extensively in vision for more than two decades, primarily for applications such as image segmentation and object detection. The vast majority of active contours models make use of closed curves and the few that employ open curves rely on either fixed boundary conditions or no boundary conditions at all. We discuss a new class of open active contours with free boundary conditions, in which the end points of the open active curve are restricted to lie on two parametric boundary curves. We discuss how this class of curves may assist and facilitate various vision applications and we demonstrate its utility in applications such as boundary detection, feature tracking, seam carving, and image stitching.

Hidden Markov models (HMMs) were introduced and studied in the late 1960s, and early 1970s. One of the first applications of HMMs was speech recognition, starting in the mid-1970s. In the second half of the 1980s, HMMs began to be applied to recognition of handwritten text in images, commonly known as offline handwriting recognition (OHR). In this talk, I'll present the Hidden Markov Model, and show some examples, of how it can be applied to offline handwriting recognition.

Can we use statistical Natural Language Processing methods on redundant data? Document collections (corpora) may include large amount of redundancy due to copied texts, this phenomena is common in news articles and Electronic Health Records. Methods for detecting and handling redundancy are common in the fields of Bioinformatics for creating sequence databases as well as for plagiarism detection. We will show that redundant text may bias statistical methods for processing such corpora as well as a robust heuristic for identifying a non-biased subset a corpus.

We define a novel variation on the constrained sequence alignment problem, the Sequence Alignment with Regular Expression Path Constraint (SA-REPC) problem, in which the constraint is given in the form of a regular expression. Our definition extends and generalizes the existing definitions of alignment-path constrained sequence alignments to the expressive power of regular expressions. We give a solution for the new variation of the problem and demonstrate its application to integrate microRNA-target interaction patterns into the target prediction computation. Our approach can serve as an efficient filter for more computationally demanding target prediction filtration algorithms. We compare our implementation for the SA-REPC problem, cAlign, to other microRNA target prediction algorithms.