Murmuring

Haar Classifier

2009-09-14T16:46:00.004+08:00

Preparing the following files
- ..\OpenCV\samples\c\convert_cascade.exe
- ..\OpenCV\bin\createsamples.exe
- ..\OpenCV\bin\haartraining.exe
Preparing positive and negative samples
- Negative( "..\negative\" )
  - dir /b/s > neg.txt
  - replacing "..\" to ""
- Positive( "..\positive\" )
  - dir /b/s > pos.txt
  - replacing "..\" to ""
  - adding "NUM X Y WIDTH HEIGHT" to each end of line
    e.g. positive\a.png 1 0 0
Moving "pos.txt" and "neg.txt" to "..\"
Creating the positive vector
createsamples -info pos.txt -vec pos.vec -num POSTOTAL -w WIDTH -h HEIGHT
e.g. createsamples -info pos.txt -vec pos.vec -num 35784 -w 32 -h 24
(Optional) Checking samples
createsamples -vec pos.vec -w WIDTH -h HEIGHT
e.g. createsamples -vec pos.vec -w 32 -h 24
Training
haartraining -data data\cascade -vec pos.vec -bg neg.txt -npos POSTOTAL -nneg NEGTOTAL -nstages STAGE -mem MEMORY -mode ALL -w WIDTH –h HEIGHT
e.g. haartraining -data data\cascade -vec pos.vec -bg neg.txt -npos 35784 -nneg 3240 -nstages 30 -mem 1000 -mode ALL -w 32 –h 24
Generating a xml file
convert_cascade --size="x" PATH CLASSIFIER
e.g. convert_cascade --size="32x24" \data\cascade\ haarcascade.xml

[Reference]

Tutorial: OpenCV haartraining

LaTex Memo

2009-06-23T18:52:00.008+08:00

Command
[Create PDF]
pdflatex FILENAME

Basic example
\documentclass[parameter]{format}
\usepackage{name}
\begin{document}
\title{string}
\author{string}
\section{string}
\subsection{string}
\end{document}

[\documentclass]
parameter
e.g. 10pt, a4paper
format
e.g. article, report, CLS file name

[\usepackage]
name
e.g. subfigure, graphicx

Image insertion
\begin{figure}
\begin{center}
\includegraphics[format]{name}
\caption{string}
\label{string}
\end{center}
\end{figure}

[\begin{center}]
置中

[\includegraphics]
format
e.g: width=0.4\textwidth
name
e.g: name.png, name.jpg

[\caption{string}]
圖片的標題

[\label{string}]
\ref{string}用

Subfigure
\subfigure[string]
{
　　\label{string}
　　\includegraphics[format]{name}
}
\subfigure[string]
{
　　\label{string}
　　\includegraphics[format]{name}
}

[\subfigure]
string
子圖標題

Bibliography
\begin{thebibliography}{num}
\bibitem{tag}
\end{thebibliography}

[\begin{thebibliography}]
num
起始數字

[\bibitem]
tag
\cite{tag}用

Vision for a Smart Kiosk

2009-06-15T16:36:00.007+08:00

Vision for a Smart Kiosk
James M. Rehg, Maria Loughlin, Keith Waters

The kiosk interface supports public interaction with multiple users.

[Obtained Information]

three dimensional location
body language
facial expressions

[Modules]

Motion blob detection
Color tracking
a color histogram model of each user's shirt
Stereo triangulation
DECface
Behavior

[DECface Agent]

speak an arbitrary piece of text at a specific speech rate in one of eight voices from one of eight faces
the creation of simple facial expressions under control of a facial muscle model
simple head and eye rotation

[Taxonomy of vision tasks]

	Features	Attributes	Behaviors
Distant (Dist)	whole body	position	monitor
Midrange (Mid)	head and torso	orientation	entice
Proximate (Prox)	face / hands	expression	communicate

[Experiments]

推測position的正確性
tracking和behvior模組的測試

[Conclusion]

Color is a valuable feature for tracking people in real-time.

Median filter

2009-06-12T21:37:00.006+08:00

[Step][Example]

Decide a window with odd samples.
Calculate the median of these samples.

[Purpose]

消除雜訊

Funny about Molly

2009-06-08T19:11:00.009+08:00

Solving a learning system (Ax=b) by iterative methods

2009-05-27T18:41:00.030+08:00

Jacobi Method
for i = 1 : n
　x[ i ]^k+1 = ( b[ i ]
　　　　　　　 - SIGMA(j = 1 : i-1)( a[ i ][ j ] * x[ j ]^k )
　　　　　　　 - SIGMA(j = i+1 : n)( a[ i ][ j ] * x[ j ]^k ) )
　　　　　　/ a[ i ][ i ]
end

[Convergence]
M = diag( diag( A ) )　N = - ( A - M )
若M^-1*N的eigenvalue皆在-1~1之間的話，則其會收斂。

Gauss-Seidal Method
for i = 1 : n
　x[ i ]^k+1 = ( b[ i ]
　　　　　　　 - SIGMA(j = 1 : i-1)( a[ i ][ j ] * x[ j ]^k+1 )
　　　　　　　 - SIGMA(j = i+1 : n)( a[ i ][ j ] * x[ j ]^k ) )
　　　　　　/ a[ i ][ i ]
end

Steepest Descent Method
x(0) = initial guess
r(0) = b - A * x(0)
k = 0
while r^k != 0
　k = k + 1
　alpha(k) = ( (r^k-1)' * r^k-1 ) / ( (r^k-1)' * A * r^k-1 )
　x^k = x^k-1 - alpha^k * r^k-1
　r^k = b - A * x^k
end

Conjugate Gradient Method
k = 0
r(0) = b - A * x(0)
while r^k != 0
　k = k + 1
　if k = 1
　　p(1) = r(0)
　else
　　beta^k = ( (r^k-1)' * r^k-1 ) / ( (r^k-2)' * r^k-2 )
　　p^k = r^k-1 + beta^k * p^k-1
　end
　alpha^k = ( (r^k-1)' * r^k-1 ) / ( (p^k)' * A * p^k )
　x^k = x^k-1 + alpha^k * p^k
　r^k = r^k-1 - alpha^k * A * p^k
end
x = x^k

[Reference]

Gene H. Golub and Charles Van Loan, Matrix computation, 1996.
[Part 1] [Part 2]

Evaluation

2009-05-19T18:34:00.036+08:00

		Actual condition
		True	False
Test result	True	True Positive (TP)	False Positive (FP)
Test result	False	False Negative (FN)	True Negative (TN)

Accuracy = ( TN + TP ) / ( TP + FN + FP + TN )

Precision = TP / ( TP + FP )

Recall = TP / ( TP + FN )