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In this paper we propose a Hidden Markov Model for modeling and extracting vine structure from images. We built up from previous research to infer connectivity of cane segments extracted from binary images. We use skeletonisation and polylines to model cane segments and we use simulated annealing to optimize an energy function defined in terms of attributes observed for each connection. We formulate...
In this paper we are interested in recovering 2D tree structure of vines from binary images. We propose a bottom-up approach that firstly segments an input image into cane parts, and second infer their connectivity by using Gibbs Sampling. Our approach is similar to previous work on vine structure inference [1], but instead of the use of heuristics for connecting cane parts, our method uses Gibbs...
With the goal of recovering the 2D tree structure present on grape vine binary images, in this paper we propose to use Mixture of Gaussians for canes segmentation. The main idea behind our approach is to use information criteria from model selection theory to guide directly the split-and-merge steps for learning Mixture of Gaussians via Expectation Maximization. A novel information criteria we found...
Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images, the first step of which requires robust segmentation...
Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. The first step requires robust segmentation...
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