Ein Gallerie mit aktuellen und stilvollen LaTeX-Vorlagen, Beispielen, die beim Lernen von LaTeX unterstützen, und Papers und Präsentationen, veröffentlicht von unseren Nutzern. Suchen oder unten durchblättern.
uumthesis is a LaTeX class for typesetting a Universiti Utara Malaysia (UUM) postgraduate research thesis.
Copyright (C) 2010--2014 Lim Lian Tze
Files:
uumuthesis.cls LaTeX class file
uumthesis-manual.pdf A quick user guide
thesis.tex Example 'main' thesis .tex file
*.tex Other component .tex files (chapters etc)
myrefs.bib Sample BibTeX bibliography database file
README Readme file
E-Mail: liantze@gmail.com
Website: http://liantze.penguinattack.org/
This code is free software; you can redistribute it and/or modify it under the terms of the LaTeX Project Public License (LPPL) as published by the LaTeX3 Project; either version 1.3 of the License, or (at your option) any later version.
Jacobs Landscape Poster
LaTeX Template
Version 1.0 (29/03/13)
Created by:
Computational Physics and Biophysics Group, Jacobs University
https://teamwork.jacobs-university.de:8443/confluence/display/CoPandBiG/LaTeX+Poster
Further modified by:
Nathaniel Johnston (nathaniel@njohnston.ca)
This template has been downloaded from:
http://www.LaTeXTemplates.com
License:
CC BY-NC-SA 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/)
Computational Physics and Biophysics Group, Jacobs University, with modification by Nathaniel Johnston and Vel Gayevskiy
Unofficial beamer template for typesetting diploma thesis presentations - Department of Computer Engineering , Technological Educational Institute of Peloponnese, Greece.
Based one the "beamer-greek-two" template provided by the Laboratory of Computational Mathematics, Mathematical Software and Digital Typography, Department of Mathematics, University of the Aegean.
For an English version of this template see here.
A sandbox to play with various useful packages, like qtree, shadedgauss, pfg-pie, bchart, dialogue, and booktabs.
Contains citation commands for apacite bibliography style.
Gesture recognition and its implementation that support Human Computer systems are becoming very popular mode of interaction now a days. It allows to interfacing the man machine commutative information flow naturally. Vision based gesture recognition has the potential that can provide intuitive and effective interaction between man and machine. However there are not adequate tools and techniques that support for developing, detecting or executing these tasks. In this paper we will implement a prototype that facilitates recording data during building some action based activities captured by the Kinect sensor. We analyze those recorded clips and visualize the user interactions by recognition the gestures objects based on depth, IR and skeletal data. Kinect tools include an analysis feature, a time-line-based approach that manually or automatically can mark the recording sequences of clips. We will implement both discrete and continuous gestures by using AdaBoast machine learning approach to detect hands activities. Our result suggest that the learning mechanism can achieve more than 98% of confidence level of given gestures.
Keywords: Gesture recognition, Kinect, HCI, Machine learning, AdaBoost, Computer vision
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