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The document discusses an approach for extracting bridges from high-resolution panchromatic images using constrained Delaunay triangulation. It first segments rivers using texture analysis and morphological operations. It then extracts bridges along the medial axes of the rivers using constrained Delaunay triangulation. Experimental results on Google Earth images show the approach is effective but requires refinement to reduce false alarms from swells and shadows.
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I3E Technologies,
#23/A, 2nd Floor SKS Complex,
Opp. Bus Stand, Karur-639 001.
IEEE PROJECTS AVAILABLE,
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MINI-PROJECTS AVAILABLE.
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Mail ID: i3eprojectskarur@gmail.com
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We describe a new algorithm for computing the Voronoi diagram of a set of $n$ points in constant-dimensional Euclidean space. The running time of our algorithm is $O(f \log n \log \spread)$ where $f$ is the output complexity of the Voronoi diagram and $\spread$ is the spread of the input, the ratio of largest to smallest pairwise distances. Despite the simplicity of the algorithm and its analysis, it improves on the state of the art for all inputs with polynomial spread and near-linear output size. The key idea is to first build the Voronoi diagram of a superset of the input points using ideas from Voronoi refinement mesh generation. Then, the extra points are removed in a straightforward way that allows the total work to be bounded in terms of the output complexity, yielding the output sensitive bound. The removal only involves local flips and is inspired by kinetic data structures.
A Tutorial on Computational Geometry
My talk about computational geometry in NTU's APEX Club in NTU, Singapore in 2007. The club is for people who are keen on participating in ACM International Collegiate Programming Contests organized by IBM annually.
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This document discusses advanced bioinformatics approaches for analyzing proteomics datasets, including using signaling networks, association networks, and text mining. It describes using machine learning to predict protein interactions and developing scoring schemes to integrate data from multiple sources. The document also covers using text mining approaches like named entity recognition and information extraction to analyze the large amount of proteomics information available in scientific literature.
Bioinformatics
This document provides an overview of bioinformatics and related topics across 7 parts:
Part I introduces bioinformatics and its areas including genomics, proteomics, computational biology, and databases.
Part II discusses the history of bioinformatics from Darwin's theory of evolution to the human genome project.
Part III focuses on the human genome project, its goals of identifying genes and sequencing DNA, and its benefits like improved medicine.
Part IV explains how the internet plays an important role in bioinformatics for retrieving biological information and resources like databases, tools, and software.
Part V describes different types of biological databases including primary, secondary, and composite databases that combine different sources.
Part VI discusses knowledge discovery
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Today very high resolution DEM from satellite image data with resolution of about one meter allows to depict very detailed surface changes.
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Presented by Vladimir Iglovikov:
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gao.ppt
- 1. Bridge Extraction based on Constrained Delaunay Triangulation Feng Gao Lei Hu Zhaofeng He
- 2. Bridge Extraction Panchromatic Image: High reolution provides detailed descriptions. Challenge: Bridges are surrounded by complex backgrounds.
- 11. Experiments Extensive experiments are performed on high resolution image gathered from Google earth. However, swells and building shadows cause many false alarms.
- 13. Acknowledgements Special thanks to Shewchuk for providing the Triangle program Special thanks to anonymous reviewers Special thanks to session chair and audience Thank you
- 14. Q&A
Editor's Notes
- What is this work about? Detect bridges in remote sensing images. Strength: High resolution provides detailed descriptions Weakness: Complex background
- Many researches extract water regions by using histogram threshold, in our view, the segmentation method based on histogram is not practical.