• CCCG
• 2009
We present a method for extracting contours from digital images using techniques from computational geometry. Our approach is different from traditional pixelbased methods in image processing. Instead of working directly with pixels, we extract a set of oriented feature points from the input digital images, then apply classical geometric techniques such as(More)
• Trans. Computational Science
• 2011
We present a simple method based on computational-geometry for extracting contours from digital images. Unlike traditional image processing methods, our proposed method first extracts a set of oriented feature points from the input images, then applies a sequence of geometric techniques, including clustering, linking, and simplification, to find contours(More)
RNA secondary structure prediction is a fundamental problem in structural bioinformatics. The prediction problem is difficult because RNA secondary structures may contain pseudoknots formed by crossing base pairs. We introduce k-partite secondary structures as a simple classification of RNA secondary structures with pseudoknots. An RNA secondary structure(More)
• CCCG
• 2010
Given k labelings of a finite d-dimensional cubical grid, define the combined distance between two labels to be the sum of the l1-distance between the two labels in each labeling. We want to construct k labelings which maximize the minimum combined distance between any two labels. When d = 1, this can be interpreted as placing n non-attacking rooks in a(More)
The goal is to collect pearls (small golden circles) by sliding a droplet of water (large blue circle) over them in a grid map. The map contains obstacles (square blocks). In each move, the droplet slides in one of the four directions to the maximal extent, until it is stopped by an obstacle. 7 Moves! left (1st pearl) up, left (2nd pearl) down, right, up,(More)
• ArXiv
• 2009
Let S be a set of n symbols. Let A be an n × n square grid with each cell labeled by a distinct symbol in S. Let B be another n × n square grid, also with each cell labeled by a distinct symbol in S. Then each symbol in S labels two cells, one in A and one in B. Define the combined distance between two symbols in S as the distance between the two cells in A(More)
• Theor. Comput. Sci.
• 2014
• 1