The Little Green BATS is the first and so far only Dutch team in the 3D simulation league. We are a group of graduate students from the department of AI at the University of Groningen, The Netherlands. Our team name is derived from the fact that the first 3D agents in the league were balls and from the very philosophical observation that Balls Are Truly Spheres (BATS). This abbreviation reminded us of our favorite song Little Green Bag by The George Baker Selection and so the whole team name was born.
We entered the competition for the first time at the 10th edition of RoboCup at Bremen, Germany. unfortunately our hard work didn t pay off that time: already in the second round we got eliminated. However, after this we had a good base to build upon and the good time we had at the event and the nice community inspired us to continue and work hard for another year. This turned out to be defiantly worth it, because in 2007 in Atlanta we managed to become vice world champions!
This sample is a simple example on how to perform a glow effect by rendering into
an arbitrary size Frame Buffer Object (FBO).
The Glow effect is performed on a specific part of the screen and can be done only
on specific objects of the scene.
You can imagine using such a postprocessing effect in CAD/DCC to emphasize
some items from a Selection or picking for example.
OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.
how to add arrays
* Use of const (constant) values.
* Creation of vectors.
* Passing vectors as function arguments.
* Reading from files of unknown size (monitoring istream status).
* Repetitive structures (while and for loops).
* The increment operators (++).
* Selection structures (if-else statements).
* Use of the .size, .empty, .begin, .insert, .erase, .resize, .clear and .swap vector class member functions.
The advantages of automation can be exploited in order to solve or to
minimize the needs of manual approach. In order to support the development of
survey accurate cadastral system, an automatic programming approach will be
adopted. Database Selection system will conduct several outliers integrity
checking, rebuild cadastral spatial topology (cadastral lot) and make selfcorrection
procedures based on cadastral survey concepts and mathematical
model respective to the cadastral lots selected. This is to ensure that all cadastral
lots are kept in a closed polygon and provide accurate and "clean" cadastral
information.. This system was developed in windows environment.
Sensors and Transducers is highly readable text which provides a unique introduction to the Selection and application of sensors, transducers and switches.
Libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
regression. It also provides an automatic model Selection tool for
C-SVM classification. This document explains the use of libsvm.
